how to write conceptual research paper

Concept Papers in Research: Deciphering the blueprint of brilliance

Concept papers hold significant importance as a precursor to a full-fledged research proposal in academia and research. Understanding the nuances and significance of a concept paper is essential for any researcher aiming to lay a strong foundation for their investigation.

Table of Contents

What Is Concept Paper

A concept paper can be defined as a concise document which outlines the fundamental aspects of a grant proposal. It outlines the initial ideas, objectives, and theoretical framework of a proposed research project. It is usually two to three-page long overview of the proposal. However, they differ from both research proposal and original research paper in lacking a detailed plan and methodology for a specific study as in research proposal provides and exclusion of the findings and analysis of a completed research project as in an original research paper. A concept paper primarily focuses on introducing the basic idea, intended research question, and the framework that will guide the research.

Purpose of a Concept Paper

A concept paper serves as an initial document, commonly required by private organizations before a formal proposal submission. It offers a preliminary overview of a project or research’s purpose, method, and implementation. It acts as a roadmap, providing clarity and coherence in research direction. Additionally, it also acts as a tool for receiving informal input. The paper is used for internal decision-making, seeking approval from the board, and securing commitment from partners. It promotes cohesive communication and serves as a professional and respectful tool in collaboration.

These papers aid in focusing on the core objectives, theoretical underpinnings, and potential methodology of the research, enabling researchers to gain initial feedback and refine their ideas before delving into detailed research.

Key Elements of a Concept Paper

Key elements of a concept paper include the title page , background , literature review , problem statement , methodology, timeline, and references. It’s crucial for researchers seeking grants as it helps evaluators assess the relevance and feasibility of the proposed research.

Writing an effective concept paper in academic research involves understanding and incorporating essential elements:

How to Write a Concept Paper?

To ensure an effective concept paper, it’s recommended to select a compelling research topic, pose numerous research questions and incorporate data and numbers to support the project’s rationale. The document must be concise (around five pages) after tailoring the content and following the formatting requirements. Additionally, infographics and scientific illustrations can enhance the document’s impact and engagement with the audience. The steps to write a concept paper are as follows:

1. Write a Crisp Title:

Choose a clear, descriptive title that encapsulates the main idea. The title should express the paper’s content. It should serve as a preview for the reader.

2. Provide a Background Information:

Give a background information about the issue or topic. Define the key terminologies or concepts. Review existing literature to identify the gaps your concept paper aims to fill.

3. Outline Contents in the Introduction:

Introduce the concept paper with a brief overview of the problem or idea you’re addressing. Explain its significance. Identify the specific knowledge gaps your research aims to address and mention any contradictory theories related to your research question.

4. Define a Mission Statement:

The mission statement follows a clear problem statement that defines the problem or concept that need to be addressed. Write a concise mission statement that engages your research purpose and explains why gaining the reader’s approval will benefit your field.

5. Explain the Research Aim and Objectives:

Explain why your research is important and the specific questions you aim to answer through your research. State the specific goals and objectives your concept intends to achieve. Provide a detailed explanation of your concept. What is it, how does it work, and what makes it unique?

6. Detail the Methodology:

Discuss the research methods you plan to use, such as surveys, experiments, case studies, interviews, and observations. Mention any ethical concerns related to your research.

7. Outline Proposed Methods and Potential Impact:

Provide detailed information on how you will conduct your research, including any specialized equipment or collaborations. Discuss the expected results or impacts of implementing the concept. Highlight the potential benefits, whether social, economic, or otherwise.

8. Mention the Feasibility

Discuss the resources necessary for the concept’s execution. Mention the expected duration of the research and specific milestones. Outline a proposed timeline for implementing the concept.

9. Include a Support Section:

Include a section that breaks down the project’s budget, explaining the overall cost and individual expenses to demonstrate how the allocated funds will be used.

10. Provide a Conclusion:

Summarize the key points and restate the importance of the concept. If necessary, include a call to action or next steps.

Although the structure and elements of a concept paper may vary depending on the specific requirements, you can tailor your document based on the guidelines or instructions you’ve been given.

Here are some tips to write a concept paper:

Example of a Concept Paper

Here is an example of a concept paper. Please note, this is a generalized example. Your concept paper should align with the specific requirements, guidelines, and objectives you aim to achieve in your proposal. Tailor it accordingly to the needs and context of the initiative you are proposing.

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Importance of a Concept Paper

Concept papers serve various fields, influencing the direction and potential of research in science, social sciences, technology, and more. They contribute to the formulation of groundbreaking studies and novel ideas that can impact societal, economic, and academic spheres.

A concept paper serves several crucial purposes in various fields:

In summary, a well-crafted concept paper is essential in outlining a clear, concise, and structured framework for new ideas or proposals. It helps in assessing the feasibility, viability, and potential impact of the concept before investing significant resources into its implementation.

How well do you understand concept papers? Test your understanding now! 

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Role of AI in Writing Concept Papers

The increasing use of AI, particularly generative models, has facilitated the writing process for concept papers. Responsible use involves leveraging AI to assist in ideation, organization, and language refinement while ensuring that the originality and ethical standards of research are maintained.

AI plays a significant role in aiding the creation and development of concept papers in several ways:

1. Idea Generation and Organization

AI tools can assist in brainstorming initial ideas for concept papers based on key concepts. They can help in organizing information, creating outlines, and structuring the content effectively.

2. Summarizing Research and Data Analysis

AI-powered tools can assist in conducting comprehensive literature reviews, helping writers to gather and synthesize relevant information. AI algorithms can process and analyze vast amounts of data, providing insights and statistics to support the concept presented in the paper.

3. Language and Style Enhancement

AI grammar checker tools can help writers by offering grammar, style, and tone suggestions, ensuring professionalism. It can also facilitate translation, in case a global collaboration.

4. Collaboration and Feedback

AI platforms offer collaborative features that enable multiple authors to work simultaneously on a concept paper, allowing for real-time contributions and edits.

5. Customization and Personalization

AI algorithms can provide personalized recommendations based on the specific requirements or context of the concept paper. They can assist in tailoring the concept paper according to the target audience or specific guidelines.

6. Automation and Efficiency

AI can automate certain tasks, such as citation formatting, bibliography creation, or reference checking, saving time for the writer.

7. Analytics and Prediction

AI models can predict potential outcomes or impacts based on the information provided, helping writers anticipate the possible consequences of the proposed concept.

8. Real-Time Assistance

AI-driven chat-bots can provide real-time support and answers to specific questions related to the concept paper writing process.

AI’s role in writing concept papers significantly streamlines the writing process, enhances the quality of the content, and provides valuable assistance in various stages of development, contributing to the overall effectiveness of the final document.

Concept papers serve as the stepping stone in the research journey, aiding in the crystallization of ideas and the formulation of robust research proposals. It the cornerstone for translating ideas into impactful realities. Their significance spans diverse domains, from academia to business, enabling stakeholders to evaluate, invest, and realize the potential of groundbreaking concepts.

Frequently Asked Questions

A concept paper can be defined as a concise document outlining the fundamental aspects of a grant proposal such as the initial ideas, objectives, and theoretical framework of a proposed research project.

A good concept paper should offer a clear and comprehensive overview of the proposed research. It should demonstrate a strong understanding of the subject matter and outline a structured plan for its execution.

Concept paper is important to develop and clarify ideas, develop and evaluate proposal, inviting collaboration and collecting feedback, presenting proposals for academic and research initiatives and allocating resources.

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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualize your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, “hours of study,” is the independent variable (the predictor, or explanatory variable)
• The expected effect, “exam score,” is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (“hours of study”).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the “effect” component of the cause-and-effect relationship.

Let’s add the moderator “IQ.” Here, a student’s IQ level can change the effect that the variable “hours of study” has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our “IQ” moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the “IQ” moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs. mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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Conceptual Research: Definition, Framework, Example and Advantages

Conceptual Research: Definition

Conceptual research is defined as a methodology wherein research is conducted by observing and analyzing already present information on a given topic. Conceptual research doesn’t involve conducting any practical experiments. It is related to abstract concepts or ideas. Philosophers have long used conceptual research to develop new theories or interpret existing theories in a different light.

For example, Copernicus used conceptual research to come up with the concepts of stellar constellations based on his observations of the universe. Down the line, Galileo simplified Copernicus’s research by making his own conceptual observations which gave rise to more experimental research and confirmed the predictions made at that time.

The most famous example of conceptual research is Sir Issac Newton. He observed his surroundings to conceptualize and develop theories about gravitation and motion.

Einstein is widely known and appreciated for his work on conceptual research. Although his theories were based on conceptual observations, Einstein also proposed experiments to come up with theories to test the conceptual research.

Nowadays, conceptual research is used to answer business questions and solve real-world problems. Researchers use analytical research tools called conceptual frameworks to make conceptual distinctions and organize ideas required for research purposes.

Conceptual Research Framework

Conceptual research framework constitutes of a researcher’s combination of previous research and associated work and explains the occurring phenomenon. It systematically explains the actions needed in the course of the research study based on the knowledge obtained from other ongoing research and other researchers’ points of view on the subject matter.

Here is a stepwise guide on how to create the conceptual research framework:

01. Choose the topic for research

Before you start working on collecting any research material, you should have decided on your topic for research. It is important that the topic is selected beforehand and should be within your field of specialization.

02. Collect relevant literature

Once you have narrowed down a topic, it is time to collect relevant information about it. This is an important step, and much of your research is dependent on this particular step, as conceptual research is mostly based on information obtained from previous research. Here collecting relevant literature and information is the key to successfully completing research.

The material that you should preferably use is scientific journals , research papers published by well-known scientists , and similar material. There is a lot of information available on the internet and in public libraries as well. All the information that you find on the internet may not be relevant or true. So before you use the information, make sure you verify it.  

03. Identify specific variables

Identify the specific variables that are related to the research study you want to conduct. These variables can give your research a new scope and can also help you identify how these can be related to your research design . For example, consider hypothetically you want to conduct research about the occurrence of cancer in married women. Here the two variables that you will be concentrating on are married women and cancer.

While collecting relevant literature, you understand that the spread of cancer is more aggressive in married women who are beyond 40 years of age. Here there is a third variable which is age, and this is a relevant variable that can affect the end result of your research.  

04. Generate the framework

In this step, you start building the required framework using the mix of variables from the scientific articles and other relevant materials. The research problem statement in your research becomes the research framework. Your attempt to start answering the question becomes the basis of your research study. The study is carried out to reduce the knowledge gap and make available more relevant and correct information.

Example of Conceptual Research Framework

Thesis statement/ Purpose of research: Chronic exposure to sunlight can lead to precancerous (actinic keratosis), cancerous (basal cell carcinoma, squamous cell carcinoma, and melanoma), and even skin lesions (caused by loss of skin’s immune function) in women over 40 years of age.

The study claims that constant exposure to sunlight can cause the precancerous condition and can eventually lead to cancer and other skin abnormalities. Those affected by these experience symptoms like fatigue, fine or coarse wrinkles, discoloration of the skin, freckles, and a burning sensation in the more exposed areas.

Note that in this study, there are two variables associated- cancer and women over 40 years in the African subcontinent. But one is a dependent variable (women over 40 years, in the African subcontinent), and the other is an independent variable (cancer). Cumulative exposure to the sun till the age of 18 years can lead to symptoms similar to skin cancer. If this is not taken care of, there are chances that cancer can spread entirely.

Assuming that the other factors are constant during the research period, it will be possible to correlate the two variables and thus confirm that, indeed, chronic exposure to sunlight causes cancer in women over the age of 40 in the African subcontinent. Further, correlational research can verify this association further.

Advantages of Conceptual Research

1. Conceptual research mainly focuses on the concept of the research or the theory that explains a phenomenon. What causes the phenomenon, what are its building blocks, and so on? It’s research based on pen and paper.

2. This type of research heavily relies on previously conducted studies; no form of experiment is conducted, which saves time, effort, and resources. More relevant information can be generated by conducting conceptual research.

3. Conceptual research is considered the most convenient form of research. In this type of research, if the conceptual framework is ready, only relevant information and literature need to be sorted.

QuestionPro for Conceptual Research

QuestionPro offers readily available conceptual frameworks. These frameworks can be used to research consumer trust, customer satisfaction (CSAT) , product evaluations, etc. You can select from a wide range of templates question types, and examples curated by expert researchers.

We also help you decide which conceptual framework might be best suited for your specific situation.

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How to Write a Concept Paper Easily with Our Guide

Did you know that some of the most revolutionary ideas in history started with a simple concept paper? From scientific breakthroughs to groundbreaking inventions, the power of well-crafted concept papers cannot be underestimated.

In this article, experts at our academic essay writing service will demystify the process of writing a concept paper, offering straightforward tips and guidance to help you articulate your ideas effectively. Whether you're a researcher, entrepreneur, or student, you'll lay the foundation for your next big endeavor effortlessly.

Defining What is a Concept Paper

A concept paper is a starting point for any major project or research endeavor. When you're asked to write one, what your teachers or professors are really asking for is a clear, concise summary of what you plan to explore or investigate. It's your chance to explain your idea, why it matters, and how you're going to tackle it.

Imagine you're pitching your idea to someone who doesn't know anything about it. You want to grab their attention and get them excited about what you're planning to do. That's what a concept paper is all about – setting the stage for your project or research in a way that makes people want to learn more.

Don't Delay Your Scholarly Pursuits!

Our team is here to nurture your concepts! Seize this opportunity to lay the groundwork for your academic exploration.

Why Does a Concept Paper Matter

So, why does knowing how to write a concept paper for academic research matter? First off, it helps you clarify your thoughts and organize your ideas. Writing down your concept forces you to think through the details of your project, which can be super helpful, especially when things start to get overwhelming.

Secondly, it's a way to get feedback early on. By sharing your concept paper with your teachers, advisors, or classmates, you can get valuable input that can help you refine your idea and make it even better.

Plus, it shows that you're serious about your project. Taking the time to write a concept paper demonstrates to your instructors that you've put thought and effort into your work, which can earn you some serious brownie points.

Understanding How Long is a Concept Paper

When it comes to the length of a concept paper, think quality over quantity. It's not about hitting a specific word count; it's about conveying your ideas clearly and concisely. In general, a concept paper is meant to be short and to the point. You want to give enough detail to explain your idea thoroughly, but you don't want to overwhelm your reader with unnecessary information.

As a rule of thumb, most concept papers range from 1 to 3 pages. However, this can vary depending on your specific assignment or the requirements of the project you're proposing.

The key is to focus on the essentials. Include a brief introduction to your topic, a clear statement of your purpose or objective, an overview of your methodology or approach, and a summary of the potential impact or significance of your project. And if you ever need further help, simply ask us - write my research paper for the professionally crafted project.

Concept Paper Vs. Research Paper

While both concept papers and research papers are common in academia, they serve different purposes and have distinct formats.

A concept paper, as we've discussed, is a concise document that outlines the basic idea or proposal for a project. It's like the blueprint or roadmap for your research endeavor. The focus here is on articulating the central concept, defining the objectives, and outlining the methodology. Think of writing a concept paper as laying the groundwork before diving into the detailed work of a research project.

On the other hand, a research paper is a more comprehensive and in-depth exploration of a topic or question. It involves conducting original research, analyzing data, and presenting findings in a formal written format. Research papers typically follow a structured format, including an introduction, literature review, methodology, results, discussion, and conclusion.

How to Write a Concept Paper in 8 Steps

Alright, getting into the nitty-gritty of writing your concept paper format might seem a bit overwhelming at first, but don't worry! We've got your back. By breaking down the process into eight manageable steps, we'll guide you through each stage with clarity and confidence.

Define the Study Title and Its Objectives

The first crucial step in crafting your concept paper is to clearly define the study title and its objectives. This sets the foundation for your entire paper and helps guide your research direction.

Begin by crafting a clear and concise title that effectively communicates the essence of your study. Your title should be descriptive yet succinct, giving readers a glimpse into the focus of your research.

Next, outline the objectives of your study. What specific goals do you aim to achieve through your research? Be precise and realistic in outlining these objectives, ensuring they are achievable within the scope of your study.

Explain the Study's Context and Extent

After defining the title and objectives, it's essential to provide context and define the extent of your study. This step of how to write a concept paper for college helps readers understand the background and scope of your research.

Start by providing background information on the topic of your study. Discuss relevant theories, concepts, or existing research that contextualizes your work and highlights its importance.

Next, define the extent of your study by outlining its boundaries and limitations. What specific aspects of the topic will you focus on, and what areas will you exclude? Clarifying these boundaries helps ensure that your research remains focused and manageable.

Additionally, consider discussing the significance of your study within the broader field. How does your research contribute to existing knowledge, and what potential impact does it have?

Identify the Issue

This is where you clearly articulate the core challenge or question that your research seeks to explore. Start by providing a concise overview of the issue at hand. What is the specific problem or question that motivates your research? Why is it important or relevant within your field of study?

Next, consider providing context or background information that helps readers understand the significance of the issue. This could include discussing relevant trends, statistics, or real-world examples that highlight the importance of addressing the problem.

Finally, be sure to articulate the significance of the issue within the broader context of your field. Why is it important to study this particular issue, and what potential impact could your research have on addressing it?

List Goals and Objectives

In this step, you'll make a concept paper outline of the specific goals and objectives of your study. Goals represent the broader aims of your research, while objectives provide clear, measurable steps toward achieving those goals.

Start by defining your overarching goals. What do you hope to accomplish through your research? Think about the broader outcomes or changes you aim to bring about in your field or community.

Next, break down these goals into smaller, achievable objectives. Objectives should be specific, measurable, achievable, relevant, and time-bound (SMART). They should outline the concrete steps you will take to accomplish your goals.

Consider organizing your goals and objectives into a hierarchical structure, with broader goals at the top and more specific objectives underneath. Even if you'd rather buy essay from our pros, this step will help you provide clarity and coherence to your research plan.

Approach and Methodology

In this step, you'll detail the approach and methodology you'll use to conduct your research. According to our expert thesis writing service , this section is crucial as it outlines the methods you'll employ to address your research question and achieve your objectives.

Start by explaining your overall approach to research. Will you be conducting qualitative or quantitative research, or perhaps a combination of both? Describe the rationale behind your chosen approach and how it aligns with your research goals.

Next, outline the specific methodologies you'll use to collect and analyze data. This may include methods such as surveys, interviews, experiments, or literature reviews. Provide justification for why each method is appropriate for addressing your research question and objectives.

Be sure to consider any ethical considerations or limitations associated with your chosen methodologies and outline how you plan to address them.

Finally, discuss your data analysis plan. How will you analyze the data you collect to draw meaningful conclusions? Will you use statistical analysis, thematic coding, or another method?

Overview of Planned Methods and Expected Outcomes

In this step of how to write a concept paper for research, you'll provide an overview of the specific methods you plan to use and outline the expected outcomes or results.

Start by summarizing the methods you'll employ to collect data. This may include qualitative methods such as interviews or focus groups, quantitative methods such as surveys or experiments, or a combination of both. Briefly explain why you've chosen these methods and how they align with your research goals.

Next, outline the planned steps for implementing each method. Describe the procedures you'll follow to collect and analyze data, including any tools or instruments you'll use.

After detailing your methods, discuss the expected outcomes or results of your research. What do you hope to learn or discover through your study? How will your findings contribute to existing knowledge in your field?

Be realistic in your expectations and consider potential challenges or limitations that may affect your results. By acknowledging these factors upfront, you demonstrate a thoughtful and nuanced understanding of your research process.

Include Supporting Details

Here, you'll enrich your concept paper by incorporating supporting details that bolster your argument and provide additional context for your research.

Start by providing relevant background information or literature reviews that support your research topic. This could include citing key studies, theories, or concepts that inform your understanding of the issue.

Next, consider including any relevant data, statistics, or examples that illustrate the significance of your research topic. This could involve presenting findings from previous studies, real-world examples, or case studies that highlight the need for further investigation.

Additionally, discuss any theoretical frameworks or conceptual models that underpin your research approach. How do these frameworks help guide your study and shape your research questions?

Finally, be sure to cite your sources properly using the appropriate citation style (e.g., APA, MLA). This demonstrates academic integrity and allows readers to verify the information you've presented.

Wrap Up with a Summary

In this final step, you'll bring your concept paper to a close by summarizing the key points and reinforcing the significance of your research.

If you're uncertain how to write a conclusion for an essay , start by briefly recapping the main elements of your concept paper, including the research topic, objectives, methodology, and expected outcomes. This helps reinforce the central message of your paper and reminds readers of the key insights you've presented.

Next, reiterate the importance of your research topic and its potential impact within your field. Emphasize how your study fills a gap in existing knowledge or addresses a pressing issue, highlighting the relevance and significance of your research.

Finally, conclude with a call to action or a thought-provoking statement that encourages further reflection or discussion. This could involve suggesting avenues for future research, proposing practical implications for policymakers or practitioners, or inviting readers to consider the broader implications of your findings.

Tips for Writing a Concept Paper

Now that you've got a solid understanding of how to write a concept paper, let's explore some invaluable tips to help you navigate the writing process with finesse.

• Be Specific in Your Objectives : Clearly define your objectives with measurable outcomes. Avoid vague language and ensure each objective is actionable and achievable within the scope of your study. Specific objectives provide clarity and help guide your research effectively.
• Provide Contextual Background : Offer sufficient background information to contextualize your research topic. This includes explaining relevant theories, historical context, or existing literature related to your study. Providing context in your concept paper helps readers understand the significance of your research and its relevance within the broader field.
• Justify Your Methodological Choices : Explain why you've chosen specific research methods and justify their appropriateness for your study. Consider factors such as feasibility, ethical considerations, and alignment with your research objectives. Providing a rationale for your methodological choices adds credibility to your research approach.
• Anticipate and Address Limitations : Acknowledge potential limitations or challenges associated with your study and discuss how you plan to mitigate them. This demonstrates a thoughtful approach to your research and shows that you've considered the broader implications of your study. Being transparent about limitations also helps manage expectations and build trust with your audience.

Concept Paper Example

Now that we've explored the steps and tips for writing a concept paper let's put theory into practice. In this section, we'll provide you with a concept paper example to illustrate how these principles can be applied in a real-world scenario.

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Concept Paper Topics

In this section, we'll provide you with a range of thought-provoking concept paper ideas spanning various disciplines and interests. Whether you're passionate about social issues, scientific advancements, or want to learn how to research a topic on cultural phenomena, you're sure to find inspiration here.

• The Influence of Instagram Fitness Influencers on Body Image Perception Among Adolescent Girls
• Implementing Bicycle-Sharing Programs to Reduce Carbon Emissions in Downtown Metropolitan Areas
• Analyzing the Effectiveness of Food Pantry Programs in Alleviating Food Insecurity Among Undergraduate Students at Urban Universities
• Assessing the Accuracy and Efficiency of Machine Learning Algorithms in Early Detection of Breast Cancer Using Medical Imaging Data
• Strategies for Increasing Female Representation in Computer Science and Engineering Programs at Universities
• Investigating the Impact of Workplace Mindfulness Programs on Employee Burnout Rates in High-stress Industries
• Barriers to Accessing Mental Health Services in Rural Appalachia: A Case Study
• The Ecological Impact of Microplastic Contamination on Coral Reef Ecosystems in the Caribbean
• Addressing Online Harassment and Cyberbullying Among Middle School Students Through Digital Literacy Education Programs
• The Relationship Between Proximity to Parks and Greenspaces and Mental Health Outcomes in Urban Dwellers: A Cross-sectional Study
• Virtual Reality Rehabilitation for Upper Limb Motor Recovery After Stroke: A Comparative Analysis of Traditional Therapy Methods
• Evaluating the Economic Viability and Environmental Sustainability of Indoor Vertical Farming Systems in Urban Settings
• Psychological Profiles of Adolescent Online Gamers: A Longitudinal Study on Risk Factors for Gaming Addiction
• Peer Mentoring Interventions for Improving Academic Performance and Retention Rates Among First-generation College Students in STEM Majors
• Universal Basic Income Pilot Programs: Assessing Socioeconomic Impacts and Policy Implications in Scandinavian Countries.

And there you have it - you've journeyed through the ins and outs of concept paper writing! You've learned the ropes, discovered valuable tips, explored an example, and got a bunch of topic ideas to fuel your creativity.

Now armed with the know-how, it's time to dive in and start crafting your concept paper. Remember to keep it focused, stay organized, and don't forget to let your passion shine through. With your enthusiasm and newfound skills, there's no doubt you'll create a paper that grabs attention and makes a real impact in your field.

Related Articles

Conceptual Framework: A Step-by-Step Guide on How to Make One

What is a conceptual framework? How do you prepare one? This article defines the conceptual framework and lists the steps on how to prepare it. A simplified example is added to strengthen the reader’s understanding.

In preparing your research paper as one requirement for your course as an undergraduate or graduate student, you will need to write the conceptual framework of your study. The conceptual framework steers the whole research activity. The conceptual framework serves as a “map” or “rudder” that will guide you towards realizing your study’s objectives or intent.

What, then, is a conceptual framework in empirical research? The next section defines and explains the term.

Table of Contents

Definition of conceptual framework.

A conceptual framework represents the researcher’s synthesis of the literature on how to explain a phenomenon. It maps out the actions required in the study’s course, given the researcher’s previous knowledge of other researchers’ point of view and his or her observations about the phenomenon studied.

The conceptual framework is the researcher’s understanding of how the particular  variables  in the study connect. Thus, it identifies the variables required in the research investigation. It is the researcher’s “map” in pursuing the investigation.

As McGaghie  et al . (2001) put it: The conceptual framework “sets the stage” to present the particular research question that drives the investigation being reported based on the problem statement. The problem statement of a thesis gives the context and the issues that caused the researcher to conduct the study.

The conceptual framework lies within a much broader framework called a theoretical framework . The latter draws support from time-tested theories that embody many researchers’ findings on why and how a particular phenomenon occurs.

I expounded on this definition, including its purpose, in my recent post titled “ What is a Conceptual Framework? Expounded Definition and Five Purposes .”

4 Steps on How to Make the Conceptual Framework

Before you prepare your conceptual framework, you need to do the following things:

Choose your topic

Decide on what will be your research topic. The topic should be within your field of specialization. (Generate your research topic using brainstorming tips ).

Do a literature review

Review relevant and updated research on the theme that you decide to work on after scrutiny of the issue at hand. Preferably use peer-reviewed , and well-known scientific journals as these are reliable sources of information.

Isolate the important variables

Identify the specific variables described in the literature and figure out how these are related. Some research abstracts contain the variables, and the salient findings thus may serve the purpose. If these are not available, find the research paper’s summary.

If the variables are not explicit in summary, get back to the methodology or the results and discussion section and quickly identify the study variables and the significant findings. Read  the TSPU Technique  to skim articles efficiently and get to the essential points with little fuss.

Generate the conceptual framework

Build your conceptual framework using your mix of the variables from the scientific articles you have read. Your problem statement or research objective serves as a reference for constructing it. In effect, your study will attempt to answer the question that other researchers have not explained yet. Your research should address a knowledge gap .

Example of a Conceptual Framework

Research topic.

Statement number 5 introduced in an earlier post titled How to Write a Thesis Statement  will serve as the basis of the illustrated conceptual framework in the following examples.

The youth, particularly students who need to devote a lot of time using their mobile phones to access their course modules, laptops, or desktops, are most affected. Also, they spend time interacting with their mobile phones as they communicate with their friends on social media channels like Facebook, Messenger, and the like.

When free from schoolwork, many students spend their time viewing films on Netflix, YouTube, or similar sites. These activities can affect their sleeping patterns and cause health problems in the long run because light-emitting diode (LED) exposure reduces the number of hours spent sleeping.

Thesis Statement

Related to the students’ activity, we can write the thesis statement thus:

Thesis statement : Chronic exposure to blue light from LED screens (of computer monitors ,  mobile phones, tablets, and television) deplete melatonin levels, thus reducing the number of sleeping hours among the youth, particularly students who need to work on their academic requirements.

Review of Literature

The literature review supports the thesis statement as among those that catch one’s attention is a paper that warns against the use of LED devices at night. Although we can save a lot of electrical energy by using the efficient LED where the inventors Isamu Akasaki, Hiroshi Amano and Shuji Nakamura received a Nobel prize in physics in 2014, there is growing evidence that it can cause human health problems, particularly cancer.

Haim & Zubidat (2015) of the Israeli Center for Interdisciplinary Research in Chronobiology synthesized the literature about LEDs. They found out that blue light from the light-emitting diodes (LED) inhibits melatonin production, particularly during active secretion at night. Melatonin is a neuro-hormone that regulates sleep and wake cycles. Also, it can slow down aging and prevent cancer (Srinivasan et al., 2011).

Thus, looking directly at your laptop, mobile phone, or television at night not only can severely damage your eyes but also prevent the achievement of sound sleeping patterns. As a countermeasure, sleep experts recommend limiting the use of digital devices until 8 o’clock in the evening.

Those affected experience insomnia (see 10 Creative Ways on How to Get Rid of Insomnia ); they sleep less than required (usually less than six hours), and this happens when they spend too much time working on their laptops doing some machine learning stuff, monitoring conversations or posts on social media sites using their mobile phones, or viewing the television at night.

Variables Isolated from the Literature

Using the background information backed by evidence in the literature review, we can now develop the study’s paradigm on the effect of LED exposure to sleep. We will not include all the variables mentioned and select or isolate only those factors that we are interested in.

Figure 1 presents a visual representation, the paradigm, of what we want to correlate in this study. It shows measurable variables that can produce data we can analyze using a statistical test such as either the parametric test Pearson’s Product-Moment Correlation or the nonparametric test Spearman Rho (please refresh if you cannot see the figure).

Notice that the variables of the study are explicit in the paradigm presented in Figure 1. In the illustration, the two variables are:

1) the number of hours devoted in front of the computer, and 2) the number of hours slept through the night until dawn.

The former is the independent variable, while the latter is the dependent variable. Both variables are easy to measure. It is just counting the number of hours spent in front of the computer and the number of hours slept through the night in the study subjects.

Assuming that other things are constant during the study’s performance, it will be possible to relate these two variables and confirm that, indeed, blue light emanated from computer screens can affect one’s sleeping patterns. (Please read the article titled “ Do you know that the computer can disturb your sleeping patterns ?” to find out more about this phenomenon). A correlation analysis will show if the relationship is significant.

Related Reading :

• How the conceptual framework guides marketing research

Evolution of a Social Theory as Basis of Conceptual Framework Development

Related to the development of the conceptual framework, I wrote a comprehensive article on how a social theory develops by incisively looking at current events that the world is facing now — the COVID-19 pandemic. It shows how society responds to a threat to its very survival.

Specifically, this article focuses on the COVID-19 vaccine, how it develops and gets integrated into the complex fabric of human society. It shows how the development of the vaccine is only part of the story. A major consideration in its development resides in the supporters of the vaccine’s development, the government, and the recipients’ trust, thus the final acceptance of the vaccine.

Social theory serves as the backdrop or theoretical framework of the more focused or variable level conceptual framework. Hence, the paradigm that I develop at the end of that article can serve as a lens to examine how the three players of vaccine development interact more closely at the variable level. It shows the dynamics of power and social structure and how it unfolds in response to a pandemic that affects everyone.

Check out the article titled “ Pfizer COVID-19 Vaccine: More Than 90% Effective Against the Coronavirus .” This article shall enrich your knowledge of how an abstract concept narrows down into blocks of researchable topics.

Haim, A., & Zubidat, A. E. (2015). LED light between Nobel Prize and cancer risk factor. Chronobiology International , 32 (5), 725-727.

McGaghie, W. C.; Bordage, G.; and J. A. Shea (2001). Problem Statement, Conceptual Framework, and Research Question. Retrieved on January 5, 2015 from http://goo.gl/qLIUFg

Srinivasan, V., R Pandi-Perumal, S., Brzezinski, A., P Bhatnagar, K., & P Cardinali, D. (2011). Melatonin, immune function and cancer. Recent patents on endocrine, metabolic & immune drug discovery , 5 (2), 109-123.

©2015 January 5 P. A. Regoniel

Cite as: Regoniel, P. A. (2015, January 5). Conceptual framework: a step-by-step guide on how to make one. Research-based Articles. https://simplyeducate.me/wordpress_Y/2015/01/05/conceptual-framework-guide/

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About the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

104 Comments

hallo! I would like to study “the socio-economic and environmental Impact of urban forests on livelihood: The perception of urban residents” how can my conceptual framework be like?

Hello Jesse, there are many free alternatives online if you are diligent enough in finding them. The reason I wrote this article is that in 2015, when I originally wrote it, I could not find an easy-to-understand explanation of the conceptual framework which will help my students. I also have a vague knowledge of the concept at that time, even with the available literature. Hence, I painstakingly gathered all materials I could from online and offline literature, synthesized them, and wrote about the concept in the simplest way I could without losing the essence. Now, I have seen many articles and even videos using the ideas I have rigorously prepared. If you find the tedious work I did irrelevant, then perhaps the ebook is expensive notwithstanding the many expenses on hosting, domain name, time and effort in maintaining the site that I incur in keeping this website online and make this ebook available to everyone.

I read the article how still struggling to come up with a conceptual framework, may you please assist, how should I go about as a new researcher my topic; INVESTIGATE THE DECISION TO TRANSFER NINE (9) FUNCTIONS OF ENVIRONMENTAL HEALTH TO LOCAL GOVERNMENT . Purpose: The purpose of this research is to review the delivery of EHS at the local government with a view to understanding the variation in performance and their causes. questions are: 1.2.1 What factors explain the performance variation in the delivery of EHS across municipalities? 1.2.2 How has devolution of the EH function aided or harmed the delivery of EHS?

Hello Mr. Siyabonga. I think what you want to find out is how environmental health services (EHS) performed at the local government level. In doing so, you need to have a set of indicators of successful transition. How is performance assessed? Once you already have a measure of success, then you need to define which variables in the local government have significantly influenced performance. After you have done so, then you can try to correlate local government characteristics and their performance.

I hope that helps.

My name is Jobson, my research topic is: The scope of Ugandan nurses and midwives in using the nursing process in the care of patients

our topic is Neutrophil and Lymphocyte Ratio as a Diagnostic Biomarker for Kidney Stones (experimental) How do I come up with a conceptual framework? What would be the variables?

My topic: E-commerce Platform for Agricultural and Construction Supplies with e-KYC Identification, Feed Page, and Products Bidding Will you please help me to make Conceptual Framework written with visual representation. thank you so much in advance.

Good day Jomar, I am not so clear about what you want to do. Can you write the objectives of your study? You can read about framing the research objectives here: https://simplyeducate.me/wordpress_Y/2020/03/15/research-objective/

My topic is; Mitigating against Childmaltreatment in earlychildhood through positive parenting: Chronicles of first time parents in XYZ City”

How do I come up with a conceptual framework? What would be variables?

Hello Phathi, Apparently, you are trying to relate parenting and child behavior?

Assessing the use of Geographic Information Systems (GIS) as a storage information tool in estate management:

How do i come up with a conceptual framework. What would be variables??

Dear Mbuso, Why will you assess the GIS use in estate management? What is it for?

Conceptual review papers: revisiting existing research to develop and refine theory

• Theory/Conceptual
• Published: 29 April 2020
• Volume 10 , pages 27–35, ( 2020 )

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• John Hulland 1  

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Conceptual review papers can theoretically enrich the field of marketing by reviewing extant knowledge, noting tensions and inconsistencies, identifying important gaps as well as key insights, and proposing agendas for future research. The result of this process is a theoretical contribution that refines, reconceptualizes, or even replaces existing ways of viewing a phenomenon. This paper spells out the primary aims of conceptual reviews and clarifies how they differ from other theory development efforts. It also describes elements essential to a strong conceptual review paper and offers a specific set of best practices that can be used to distinguish a strong conceptual review from a weak one.

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Meta-analysis: integrating accumulated knowledge.

Dhruv Grewal, Nancy Puccinelli & Kent B. Monroe

Designing conceptual articles: four approaches

Elina Jaakkola

Contours of the marketing literature: Text, context, point-of-view, research horizons, interpretation, and influence in marketing

Terry Clark, Thomas Martin Key & Carol Azab

Palmatier et al. ( 2018 ) reference a study of the frequency with which review papers were published in top marketing journals during the 2012–2016 period. Focusing on the top six journals included in the Financial Times (( FT-50 ) journal list, the study found that “ JAMS has become the most common outlet … publishing 31% of all review papers that appeared in the top six marketing journals.”

The bifurcation here between theory development “from scratch” versus through conceptual review is potentially somewhat misleading, since the latter can also result in novel theoretical insights. Furthermore, many conceptual papers make significant theoretical contributions by building on existing theory without themselves being review papers. Nonetheless, conceptual reviews necessarily involve working with extant, published work.

This focus is quite distinct from the approach proposed by Zeithaml et al. ( 2020 ). Their emphasis is on “an approach that is ideally suited to the development of theories in marketing: the ‘theories-in-use’ (TIU) approach” (p. 32). They propose it as an alternative inductive methodology (vs. case studies and ethnographies) to developing grounded theory.

These elements are drawn from Hulland & Houston ( 2020 ), MacInnis ( 2011 ), Palmatier et al. ( 2018 ), and Yadav ( 2010 ). Houston ( 2020 ), MacInnis ( 2011 ), Palmatier, Houston & Hulland et al. ( 2018 ), and Yadav ( 2010 ).

These underlying assumptions are a crucial component in developing strong arguments for theory development (Toulmin 1958 ).

MacInnis ( 2011 ) describes eight critical skills for conceptual thinking that are arrayed across four dimensions: envisioning (identifying vs. revising), explicating (delineating vs. summarizing), relating (differentiating vs. integrating, and debating (advocating vs. refuting). For conceptual review papers, summarizing and revising represent critical skills that need to be harnessed by the author (whereas identifying and delineating are skills more critical to uncovering new ideas). For the other two dimensions (relating and debating), a more balanced use of the associated skills is needed (i.e., both differentiating and integrating are important, and both advocating and refuting are important).

In her paper, Jaakkola ( 2020 ) describes four different types of research designs for conceptual reviews: (1) theory synthesis, (2) theory adaptation, (3) typology, and (4) model. In the current paper, elements from all four of these types are discussed.

In doing so, Khamitov et al. discover seven overarching insights that reveal gaps in the interfaces between the three streams. This highlighting of gaps represents stage four in the theory refinement process.

Not all of the gaps in a specific domain are necessarily valuable, however. Just because no one has studied a phenomenon in a particular industry or region, or with a particular method does not mean that a filling of that gap is required (or even valued).

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Hulland, J. Conceptual review papers: revisiting existing research to develop and refine theory. AMS Rev 10 , 27–35 (2020). https://doi.org/10.1007/s13162-020-00168-7

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How To Write a Concept Paper for Academic Research: An Ultimate Guide

A concept paper is one of the first steps in helping you fully realize your research project. Because of this, some schools opt to teach students how to write concept papers as early as high school. In college, professors sometimes require their students to submit concept papers before suggesting their research projects to serve as the foundations for their theses.

If you’re reading this right now, you’ve probably been assigned by your teacher or professor to write a concept paper. To help you get started, we’ve prepared a comprehensive guide on how to write a proper concept paper.

Related: How to Write Significance of the Study (with Examples)

Table of Contents

What is the concept paper, 1. academic research concept papers, 2. advertising concept papers, 3. research grant concept papers, concept paper vs. research proposal, tips for finding your research topic, 2. think of research questions that you want to answer in your project, 3. formulate your research hypothesis, 4. plan out how you will achieve, analyze, and present your data, 2. introduction, 3. purpose of the study, 4. preliminary literature review, 5. objectives of the study, 6. research questions and hypotheses, 7. proposed methodology, 8. proposed research timeline, 9. references, sample concept paper for research proposal (pdf), tips for writing your concept paper.

Generally, a concept paper is a summary of everything related to your proposed project or topic. A concept paper indicates what the project is all about, why it’s important, and how and when you plan to conduct your project.

Different Types of the Concept Paper and Their Uses

This type of concept paper is the most common type and the one most people are familiar with. Concept papers for academic research are used by students to provide an outline for their prospective research topics.

These concept papers are used to help students flesh out all the information and ideas related to their topic so that they may arrive at a more specific research hypothesis.

Since this is the most common type of concept paper, it will be the main focus of this article.

Advertising concept papers are usually written by the creative and concept teams in advertising and marketing agencies.

Through a concept paper, the foundation or theme for an advertising campaign or strategy is formed. The concept paper can also serve as a bulletin board for ideas that the creative and concept teams can add to or develop. 

This type of concept paper usually discusses who the target audience of the campaign is, what approach of the campaign will be, how the campaign will be implemented, and the projected benefits and impact of the campaign to the company’s sales, consumer base, and other aspects of the company.

This type of concept paper is most common in the academe and business world. Alongside proving why your research project should be conducted, a research grant concept paper must also appeal to the company or funding agency on why they should be granted funds.

The paper should indicate a proposed timeline and budget for the entire project. It should also be able to persuade the company or funding agency on the benefits of your research project– whether it be an increase in sales or productivity or for the benefit of the general public.

It’s important to discuss the differences between the two because a lot of people often use these terms interchangeably.

A concept paper is one of the first steps in conducting a research project. It is during this process that ideas and relevant information to the research topic are gathered to produce the research hypothesis. Thus, a concept paper should always precede the research proposal. 

A research proposal is a more in-depth outline of a more fleshed-out research project. This is the final step before a researcher can conduct their research project. Although both have similar elements and structures, a research proposal is more specific when it comes to how the entire research project will be conducted.

Getting Started on Your Concept Paper

1. find a research topic you are interested in.

When choosing a research topic, make sure that it is something you are passionate about or want to learn more about. If you are writing one for school, make sure it is still relevant to the subject of your class. Choosing a topic you aren’t invested in may cause you to lose interest in your project later on, which may lower the quality of the research you’ll produce.

A research project may last for months and even years, so it’s important that you will never lose interest in your topic.

• Look for inspiration everywhere. Take a walk outside, read books, or go on your computer. Look around you and try to brainstorm ideas about everything you see. Try to remember any questions you might have asked yourself before like why something is the way it is or why can’t this be done instead of that . 
• Think big. If you’re having trouble thinking up a specific topic to base your research project on, choosing a broad topic and then working your way down should help.
• Is it achievable? A lot of students make the mistake of choosing a topic that is hard to achieve in terms of materials, data, and/or funding available. Before you decide on a research topic, make sure you consider these aspects. Doing so will save you time, money, and effort later on.
• Be as specific as can be. Another common mistake that students make is that they sometimes choose a research topic that is too broad. This results in extra effort and wasted time while conducting their research project. For example: Instead of “The Effects of Bananas on Hungry Monkeys” , you could specify it to “The Effects of Cavendish Bananas on Potassium-deficiency in Hungry Philippine Long-tailed Macaques in Palawan, Philippines”.

Now that you have a general idea of the topic of your research project, you now need to formulate research questions based on your project. These questions will serve as the basis for what your project aims to answer. Like your research topic, make sure these are specific and answerable.

Following the earlier example, possible research questions could be:

• Do Cavendish bananas produce more visible effects on K-deficiency than other bananas?
• How susceptible are Philippine long-tailed macaques to K-deficiency?
• What are the effects of K-deficiency in Philippine long-tailed macaques?

After formulating the research questions, you should also provide your hypothesis for each question. A research hypothesis is a tentative answer to the research problem. You must provide educated answers to the questions based on your existing knowledge of the topic before you conduct your research project.

After conducting research and collecting all of the data into the final research paper, you will then have to approve or disprove these hypotheses based on the outcome of the project.

Prepare a plan on how to acquire the data you will need for your research project. Take note of the different types of analysis you will need to perform on your data to get the desired results. Determine the nature of the relationship between different variables in your research.

Also, make sure that you are able to present your data in a clear and readable manner for those who will read your concept paper. You can achieve this by using tables, charts, graphs, and other visual aids.

Related: How to Make Conceptual Framework (with Examples and Templates)

Generalized Structure of a Concept Paper

Since concept papers are just summaries of your research project, they are usually short and  no longer than 5 pages. However, for big research projects, concept papers can reach up to more than 20 pages.

Your teacher or professor may give you a certain format for your concept papers. Generally, most concept papers are double-spaced and are less than 500 words in length. 

Even though there are different types of concept papers, we’ve provided you with a generalized structure that contains elements that can be found in any type of concept paper.

The title for your paper must be able to effectively summarize what your research is all about. Use simple words so that people who read the title of your research will know what it’s all about even without reading the entire paper. 

The introduction should give the reader a brief background of the research topic and state the main objective that your project aims to achieve. This section should also include a short overview of the benefits of the research project to persuade the reader to acknowledge the need for the project.

The Purpose of the Study should be written in a way that convinces the reader of the need to address the existing problem or gap in knowledge that the research project aims to resolve. In this section, you have to go into more detail about the benefits and value of your project for the target audience/s. 

This section features related studies and papers that will support your research topic. Use this section to analyze the results and methodologies of previous studies and address any gaps in knowledge or questions that your research project aims to answer. You may also use the data to assert the importance of conducting your research.

When choosing which papers and studies you should include in the Preliminary Literature Review, make sure to choose relevant and reliable sources. Reliable sources include academic journals, credible news outlets, government websites, and others. Also, take note of the authors for the papers as you will need to cite them in the References section.

Simply state the main objectives that your research is trying to achieve. The objectives should be able to indicate the direction of the study for both the reader and the researcher. As with other elements in the paper, the objectives should be specific and clearly defined.

Gather the research questions and equivalent research hypotheses you formulated in the earlier step and list them down in this section.

In this section, you should be able to guide the reader through the process of how you will conduct the research project. Make sure to state the purpose for each step of the process, as well as the type of data to be collected and the target population.

Depending on the nature of your research project, the length of the entire process can vary significantly. What’s important is that you are able to provide a reasonable and achievable timeline for your project.

Make sure the time you will allot for each component of your research won’t be too excessive or too insufficient so that the quality of your research won’t suffer.

Ensure that you will give credit to all the authors of the sources you used in your paper. Depending on your area of study or the instructions of your professor, you may need to use a certain style of citation.

There are three main citation styles: the American Psychological Association (APA), Modern Language Association (MLA), and the Chicago style.

The APA style is mostly used for papers related to education, psychology, and the sciences. The APA citation style usually follows this format:

The MLA citation style is the format used by papers and manuscripts in disciplines related to the arts and humanities. The MLA citation style follows this format:

The Chicago citation style is usually used for papers related to business, history, and the fine arts. It follows this citation format:

This is a concept paper sample provided by Dr. Bernard Lango from the Jomo Kenyatta University of Agriculture and Technology (modified for use in this article). Simply click the link above the download the PDF file.

• Use simple, concise language. Minimize the use of flowery language and always try to use simple and easy-to-understand language. Too many technical or difficult words in your paper may alienate your readers and make your paper hard to read. 
• Choose your sources wisely. When scouring the Internet for sources to use, you should always be wary and double-check the authenticity of your source. Doing this will increase the authenticity of your research project’s claims and ensure better data gathered during the process.
• Follow the specified format, if any. Make sure to follow any specified format when writing your concept paper. This is very important, especially if you’re writing your concept paper for class. Failure to follow the format will usually result in point deductions and delays because of multiple revisions needed.
• Proofread often. Make it a point to reread different sections of your concept paper after you write them. Another way you can do this is by taking a break for a few days and then coming back to proofread your writing. You may notice certain areas you’d like to revise or mistakes you’d like to fix. Make proofreading a habit to increase the quality of your paper.

Written by Ruth Raganit

in Career and Education , Juander How

Last Updated May 30, 2022 04:34 PM

Ruth Raganit

Ruth Raganit obtained her Bachelor of Science degree in Geology from the University of the Philippines – Diliman. Her love affair with Earth sciences began when she saw a pretty rock and wondered how it came to be. She also likes playing video games, doing digital art, and reading manga.

Browse all articles written by Ruth Raganit

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Baltimore bridge collapse: a bridge engineer explains what happened, and what needs to change

Associate Professor, Civil Engineering, Monash University

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Colin Caprani receives funding from the Department of Transport (Victoria) and the Level Crossing Removal Project. He is also Chair of the Confidential Reporting Scheme for Safer Structures - Australasia, Chair of the Australian Regional Group of the Institution of Structural Engineers, and Australian National Delegate for the International Association for Bridge and Structural Engineering.

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When the container ship MV Dali, 300 metres long and massing around 100,000 tonnes, lost power and slammed into one of the support piers of the Francis Scott Key Bridge in Baltimore, the bridge collapsed in moments . Six people are presumed dead, several others injured, and the city and region are expecting a months-long logistical nightmare in the absence of a crucial transport link.

It was a shocking event, not only for the public but for bridge engineers like me. We work very hard to ensure bridges are safe, and overall the probability of being injured or worse in a bridge collapse remains even lower than the chance of being struck by lightning.

However, the images from Baltimore are a reminder that safety can’t be taken for granted. We need to remain vigilant.

So why did this bridge collapse? And, just as importantly, how might we make other bridges more safe against such collapse?

A 20th century bridge meets a 21st century ship

The Francis Scott Key Bridge was built through the mid 1970s and opened in 1977. The main structure over the navigation channel is a “continuous truss bridge” in three sections or spans.

The bridge rests on four supports, two of which sit each side of the navigable waterway. It is these two piers that are critical to protect against ship impacts.

And indeed, there were two layers of protection: a so-called “dolphin” structure made from concrete, and a fender. The dolphins are in the water about 100 metres upstream and downstream of the piers. They are intended to be sacrificed in the event of a wayward ship, absorbing its energy and being deformed in the process but keeping the ship from hitting the bridge itself.

The fender is the last layer of protection. It is a structure made of timber and reinforced concrete placed around the main piers. Again, it is intended to absorb the energy of any impact.

Fenders are not intended to absorb impacts from very large vessels . And so when the MV Dali, weighing more than 100,000 tonnes, made it past the protective dolphins, it was simply far too massive for the fender to withstand.

Read more: I've captained ships into tight ports like Baltimore, and this is how captains like me work with harbor pilots to avoid deadly collisions

Video recordings show a cloud of dust appearing just before the bridge collapsed, which may well have been the fender disintegrating as it was crushed by the ship.

Once the massive ship had made it past both the dolphin and the fender, the pier – one of the bridge’s four main supports – was simply incapable of resisting the impact. Given the size of the vessel and its likely speed of around 8 knots (15 kilometres per hour), the impact force would have been around 20,000 tonnes .

Bridges are getting safer

This was not the first time a ship hit the Francis Scott Bridge. There was another collision in 1980 , damaging a fender badly enough that it had to be replaced.

Around the world, 35 major bridge collapses resulting in fatalities were caused by collisions between 1960 and 2015, according to a 2018 report from the World Association for Waterborne Transport Infrastructure. Collisions between ships and bridges in the 1970s and early 1980s led to a significant improvement in the design rules for protecting bridges from impact.

Further impacts in the 1970s and early 1980s instigated significant improvements in the design rules for impact.

The International Association for Bridge and Structural Engineering’s Ship Collision with Bridges guide, published in 1993, and the American Association of State Highway and Transporation Officials’ Guide Specification and Commentary for Vessel Collision Design of Highway Bridges (1991) changed how bridges were designed.

In Australia, the Australian Standard for Bridge Design (published in 2017) requires designers to think about the biggest vessel likely to come along in the next 100 years, and what would happen if it were heading for any bridge pier at full speed. Designers need to consider the result of both head-on collisions and side-on, glancing blows. As a result, many newer bridges protect their piers with entire human-made islands.

Of course, these improvements came too late to influence the design of the Francis Scott Key Bridge itself.

Lessons from disaster

So what are the lessons apparent at this early stage?

First, it’s clear the protection measures in place for this bridge were not enough to handle this ship impact. Today’s cargo ships are much bigger than those of the 1970s, and it seems likely the Francis Scott Key Bridge was not designed with a collision like this in mind.

So one lesson is that we need to consider how the vessels near our bridges are changing. This means we cannot just accept the structure as it was built, but ensure the protection measures around our bridges are evolving alongside the ships around them.

Second, and more generally, we must remain vigilant in managing our bridges. I’ve written previously about the current level of safety of Australian bridges, but also about how we can do better.

This tragic event only emphasises the need to spend more on maintaining our ageing infrastructure. This is the only way to ensure it remains safe and functional for the demands we put on it today.

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Conducting thorough research and finding credible sources can be time-consuming and overwhelming, particularly for topics that are unfamiliar or require in-depth analysis.

Self-Doubt: 

Doubting one's writing abilities or fearing criticism from others can hinder creativity and confidence, making it difficult to express ideas effectively.

Procrastination: 

Putting off writing tasks until the last minute can result in rushed and subpar work, which can lead to increased stress and lower-quality outcomes.

Editing and Proofreading: 

Polishing and refining written work through editing and proofreading requires attention to detail and a critical eye, which can be challenging for some writers.

Writer's Fatigue: 

Writing for extended periods can be mentally and physically exhausting, decreasing productivity and creativity over time.

1.ProWritingCrew

ProWritingCrew is a preeminent provider of academic papers recognized for its professionalism, dependability, and outstanding quality. They offer a vast array of services to accommodate the requirements of both students and professionals, utilizing a group of seasoned writers and editors. ProWritingCrew provides superior essays, research papers, dissertations, and individualized presentations to meet each client's needs.

Why I Think They Are the Best:

ProWritingCrew sets itself apart with its commitment to excellence and customer satisfaction. Its writers are highly qualified and proficient in various subjects, ensuring that every paper is well-researched and expertly written. Additionally, its transparent communication and prompt delivery make it a trusted choice for students and professionals worldwide.

Countries They Write For:

ProWritingCrew provides writing services to students and professionals worldwide, including the United States, Canada, the United Kingdom, Australia, and many more.

What Do They Offer Exactly:

ProWritingCrew offers a comprehensive range of writing services, including essay writing, research paper writing, dissertation writing, thesis writing, editing, proofreading, and more. They also provide custom writing services tailored to each client's needs and requirements.

Competitive rates.

In conclusion, ProWritingCrew is an exceptional option for individuals seeking dependable and superior paper writing services. They guarantee client satisfaction throughout the entire process through the utilization of their knowledgeable writers, clear and candid communication, and timely delivery.

2. EssayMasterz

EssayMasterz is a trustworthy paper writing service that helps students all over the world with their schoolwork. With a team of experienced writers and editors, EssayMasterz offers a wide range of services tailored to meet the diverse needs of students across various academic levels and disciplines.

EssayMasterz stands out as one of the best paper writing services due to its commitment to excellence, reliability, and customer satisfaction. Their team of expert writers possesses the necessary skills and expertise to deliver top-notch essays, research papers, dissertations, and more. Additionally, EssayMasterz prioritizes communication with clients to ensure that every paper meets their specific requirements and expectations.

EssayMasterz provides writing services to students from around the globe, including but not limited to the United States, Canada, the United Kingdom, Australia, and Europe.

EssayMasterz's pricing is reasonable and transparent. Rates vary depending on factors such as the type of paper, academic level, deadline, and word count. 

In conclusion, EssayMasterz is a reliable and reputable paper writing service that excels in delivering high-quality academic assistance to students worldwide. With its experienced team of writers, commitment to customer satisfaction, and competitive pricing, it is a top choice for 

students seeking expert writing support.

3. EssayScribez 

EssayScribez is a trusted paper writing service that provides students high-quality academic assistance. With a team of skilled writers and editors, EssayScribez offers a range of services tailored to meet the diverse needs of students across various academic disciplines.

EssayScribez stands out as one of the best paper writing services due to its commitment to excellence, reliability, and customer satisfaction. Their team of expert writers possesses the necessary skills and expertise to deliver top-notch essays, research papers, dissertations, and more. Additionally, EssayScribez prioritizes communication with clients to ensure that every paper meets their specific requirements and expectations.

EssayScribez provides writing services to students from around the world, including the United States, Canada, the United Kingdom, Australia, and other countries.

The pricing at EssayScribez is competitive and transparent, with rates varying depending on factors such as the type of paper, academic level, deadline, and word count. 

In conclusion, EssayScribez is a reputable paper writing service that delivers high-quality academic assistance to students worldwide. With its experienced team of writers, commitment to customer satisfaction, and competitive pricing, it is a top choice for students seeking expert 

writing support.

4. SkilledEssayWriter 

SkilledEssayWriter is a reputable paper writing service that provides high-quality academic assistance to students worldwide. With a team of experienced writers and editors, SkilledEssayWriter offers a wide range of services tailored to meet the diverse needs of students across various academic levels and disciplines.

SkilledEssayWriter stands out as one of the best paper writing services due to its commitment to excellence, reliability, and customer satisfaction. Their team of expert writers possesses the necessary skills and expertise to deliver top-notch essays, research papers, dissertations, and more. Additionally, SkilledEssayWriter prioritizes communication with clients to ensure that every paper meets their specific requirements and expectations.

SkilledEssayWriter provides writing services to students from around the globe, including but not limited to the United States, Canada, the United Kingdom, Australia, and Europe.

The pricing at SkilledEssayWriter is Student-friendly, with rates varying depending on factors such as the type of paper, academic level, deadline, and word count. 

In conclusion, SkilledEssayWriter is a reliable and reputable paper writing service that delivers high-quality academic assistance to students worldwide. Their experienced team of writers, commitment to customer satisfaction, and competitive pricing make them a top choice for 

5. EssayLegend 

About essaylegend:.

EssayLegend is a trusted paper writing service committed to providing students with top-quality academic assistance. With a team of skilled writers and editors, EssayLegend offers various services tailored to meet the diverse needs of students across different academic disciplines.

EssayLegend stands out as one of the best paper writing services due to its dedication to excellence, reliability, and customer satisfaction. Their team of expert writers possesses the necessary skills and expertise to deliver high-quality essays, research papers, dissertations, and more. Moreover, EssayLegend strongly emphasizes communication with clients to ensure that every paper meets their specific requirements and expectations.

EssayLegend provides writing services to students from various countries worldwide, including the United States, Canada, the United Kingdom, Australia, and other international locations.

EssayLegend offers competitive and transparent pricing, with rates dependent on factors such as the type of paper, academic level, deadline, and word count. 

In conclusion, EssayLegend is a reputable and reliable paper writing service known for delivering top-notch academic assistance to students worldwide. With its skilled team of writers, dedication to customer satisfaction, and competitive pricing, it is a preferred choice for students seeking expert writing support.

6. QualityEssayWriter 

QualityEssayWriter is a distinguished paper writing service that provides students with exceptional academic assistance. With a team of proficient writers and editors, QualityEssayWriter offers a comprehensive range of services tailored to meet the diverse needs of students across various academic disciplines.

QualityEssayWriter earns its reputation as one of the best paper writing services due to its unwavering commitment to excellence, reliability, and customer satisfaction. Their team of seasoned writers possesses the requisite skills and expertise to deliver top-quality essays, research papers, dissertations, and more. Additionally, QualityEssayWriter prioritizes effective communication with clients to ensure that every paper meets their specific requirements and expectations.

QualityEssayWriter caters to students from across the globe, including but not limited to the United States, Canada, the United Kingdom, Australia, and other international locations.

The pricing at QualityEssayWriter is transparent and competitive. Rates vary based on factors such as the type of paper, academic level, deadline, and word count. They offer flexible pricing options to accommodate different budgets and needs, ensuring affordability without compromising quality.

How do I know if a paper writing service is legitimate?

Legitimate paper writing services typically have transparent policies, customer reviews, and clear communication channels. Look for services with verified testimonials and secure payment options to ensure legitimacy.

What factors should I consider when choosing a paper writing service?

Consider factors such as reputation, quality of writing, pricing, customer support, turnaround time, and guarantees offered by the service.

What if I need more than the quality of the paper I receive?

Reputable paper writing services typically offer revisions or refunds if you want more than the quality of the work. Check the service's policies regarding revisions and refunds before placing an order.

Do these paper-writing services provide any assurances?

Undoubtedly, authentic paper writing services frequently offer assurances, including confidentiality, money-back, and satisfaction. Consult the service's guarantee policies to understand your customer rights.

Do these paper writing services specialize in particular subject matters or academic levels?

Most paper writing services assist with various academic levels and topics. Whether one is a high school student or a doctoral candidate, there are services available that provide individualized educational support.

Conclusion:

Remember to use these services ethically and responsibly, adhere to academic integrity guidelines, and utilize the papers as valuable learning resources. With the support of these legitimate essay writing services, you can overcome educational challenges, excel in your studies, and embark on a path to success.

So, whether you're a student striving for academic excellence or a professional seeking expert writing assistance, the seven best paper writing services highlighted in this guide are your trusted partners in achieving your goals. Harness the power of these reputable services and confidently embark on your journey to academic and professional success.

Note: This article is for information purposes only and does not contain any recommendation from us for the readers.