how to critique a quantitative research paper example

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Guidelines:

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• The Critique Process: Reviewing and Critiquing Research
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How to write a critique

• Starting well
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• Finishing well

Before you start writing, it is important to have a thorough understanding of the work that will be critiqued.

• Study the work under discussion.
• Make notes on key parts of the work.
• Develop an understanding of the main argument or purpose being expressed in the work.
• Consider how the work relates to a broader issue or context.

Example template

There are a variety of ways to structure a critique. You should always check your unit materials or Canvas site for guidance from your lecturer. The following template, which showcases the main features of a critique, is provided as one example.

Introduction

Typically, the introduction is short (less than 10% of the word length) and you should:

• name the work being reviewed as well as the date it was created and the name of the author/creator
• describe the main argument or purpose of the work
• explain the context in which the work was created - this could include the social or political context, the place of the work in a creative or academic tradition, or the relationship between the work and the creator’s life experience
• have a concluding sentence that signposts what your evaluation of the work will be - for instance, it may indicate whether it is a positive, negative, or mixed evaluation.

Briefly summarise the main points and objectively describe how the creator portrays these by using techniques, styles, media, characters or symbols. This summary should not be the focus of the critique and is usually shorter than the critical evaluation.

Critical evaluation

This section should give a systematic and detailed assessment of the different elements of the work, evaluating how well the creator was able to achieve the purpose through these. For example: you would assess the plot structure, characterisation and setting of a novel; an assessment of a painting would look at composition, brush strokes, colour and light; a critique of a research project would look at subject selection, design of the experiment, analysis of data and conclusions.

A critical evaluation does not simply highlight negative impressions. It should deconstruct the work and identify both strengths and weaknesses. It should examine the work and evaluate its success, in light of its purpose.

Examples of key critical questions that could help your assessment include:

• Who is the creator? Is the work presented objectively or subjectively?
• What are the aims of the work? Were the aims achieved?
• What techniques, styles, media were used in the work? Are they effective in portraying the purpose?
• What assumptions underlie the work? Do they affect its validity?
• What types of evidence or persuasion are used? Has evidence been interpreted fairly?
• How is the work structured? Does it favour a particular interpretation or point of view? Is it effective?
• Does the work enhance understanding of key ideas or theories? Does the work engage (or fail to engage) with key concepts or other works in its discipline?

This evaluation is written in formal academic style and logically presented. Group and order your ideas into paragraphs. Start with the broad impressions first and then move into the details of the technical elements. For shorter critiques, you may discuss the strengths of the works, and then the weaknesses. In longer critiques, you may wish to discuss the positive and negative of each key critical question in individual paragraphs.

To support the evaluation, provide evidence from the work itself, such as a quote or example, and you should also cite evidence from related sources. Explain how this evidence supports your evaluation of the work.

This is usually a very brief paragraph, which includes:

• a statement indicating the overall evaluation of the work
• a summary of the key reasons, identified during the critical evaluation, why this evaluation was formed
• in some circumstances, recommendations for improvement on the work may be appropriate.

Reference list

Include all resources cited in your critique. Check with your lecturer/tutor for which referencing style to use.

• Mentioned the name of the work, the date of its creation and the name of the creator?
• Accurately summarised the work being critiqued?
• Mainly focused on the critical evaluation of the work?
• Systematically outlined an evaluation of each element of the work to achieve the overall purpose?
• Used evidence, from the work itself as well as other sources, to back and illustrate my assessment of elements of the work?
• Formed an overall evaluation of the work, based on critical reading?
• Used a well structured introduction, body and conclusion?
• Used correct grammar, spelling and punctuation; clear presentation; and appropriate referencing style?

Further information

• University of New South Wales: Writing a Critical Review
• University of Toronto: The Book Review or Article Critique

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Critical Appraisal for Health Students

• Critical Appraisal of a quantitative paper
• Critical Appraisal: Help
• Critical Appraisal of a qualitative paper
• Useful resources

Appraisal of a Quantitative paper: Top tips

• Introduction

Critical appraisal of a quantitative paper (RCT)

This guide, aimed at health students, provides basic level support for appraising quantitative research papers. It's designed for students who have already attended lectures on critical appraisal. One framework for appraising quantitative research (based on reliability, internal and external validity) is provided and there is an opportunity to practise the technique on a sample article.

Please note this framework is for appraising one particular type of quantitative research a Randomised Controlled Trial (RCT) which is defined as 

a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo.  The groups are then followed up to see if there are any differences between the results.  This helps in assessing the effectiveness of the intervention.(CASP, 2020)

Support materials

• Framework for reading quantitative papers (RCTs)
• Critical appraisal of a quantitative paper PowerPoint

To practise following this framework for critically appraising a quantitative article, please look at the following article:

Marrero, D.G.  et al.  (2016) 'Comparison of commercial and self-initiated weight loss programs in people with prediabetes: a randomized control trial',  AJPH Research , 106(5), pp. 949-956.

Critical Appraisal of a quantitative paper (RCT): practical example

• Internal Validity
• External Validity
• Reliability Measurement Tool

How to use this practical example 

Using the framework, you can have a go at appraising a quantitative paper - we are going to look at the following article:

Marrero, d.g.  et al  (2016) 'comparison of commercial and self-initiated weight loss programs in people with prediabetes: a randomized control trial',  ajph research , 106(5), pp. 949-956.,            step 1.  take a quick look at the article, step 2.  click on the internal validity tab above - there are questions to help you appraise the article, read the questions and look for the answers in the article. , step 3.   click on each question and our answers will appear., step 4.    repeat with the other aspects of external validity and reliability. , questioning the internal validity:, randomisation : how were participants allocated to each group did a randomisation process taken place, comparability of groups: how similar were the groups eg age, sex, ethnicity – is this made clear, blinding (none, single, double or triple): who was not aware of which group a patient was in (eg nobody, only patient, patient and clinician, patient, clinician and researcher) was it feasible for more blinding to have taken place , equal treatment of groups: were both groups treated in the same way , attrition : what percentage of participants dropped out did this adversely affect one group has this been evaluated, overall internal validity: does the research measure what it is supposed to be measuring, questioning the external validity:, attrition: was everyone accounted for at the end of the study was any attempt made to contact drop-outs, sampling approach: how was the sample selected was it based on probability or non-probability what was the approach (eg simple random, convenience) was this an appropriate approach, sample size (power calculation): how many participants was a sample size calculation performed did the study pass, exclusion/ inclusion criteria: were the criteria set out clearly were they based on recognised diagnostic criteria, what is the overall external validity can the results be applied to the wider population, questioning the reliability (measurement tool) internal validity:, internal consistency reliability (cronbach’s alpha). has a cronbach’s alpha score of 0.7 or above been included, test re-test reliability correlation. was the test repeated more than once were the same results received has a correlation coefficient been reported is it above 0.7 , validity of measurement tool. is it an established tool if not what has been done to check if it is reliable pilot study expert panel literature review criterion validity (test against other tools): has a criterion validity comparison been carried out was the score above 0.7, what is the overall reliability how consistent are the measurements , overall validity and reliability:, overall how valid and reliable is the paper.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Back to Journals » Nursing: Research and Reviews » Volume 3

Conducting an article critique for a quantitative research study: perspectives for doctoral students and other novice readers

• Get Permission
• Cite this article

Authors Vance DE   , Talley M , Azuero A , Pearce PF , Christian BJ

Received 29 January 2013

Accepted for publication 12 March 2013

Published 22 April 2013 Volume 2013:3 Pages 67—75

DOI https://doi.org/10.2147/NRR.S43374

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

David E Vance, 1 Michele Talley, 1 Andres Azuero, 1 Patricia F Pearce, 2 Becky J Christian 1 1 School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA; 2 Loyola University School of Nursing, New Orleans, LA, USA Abstract: The ability to critically evaluate the merits of a quantitative design research article is a necessary skill for practitioners and researchers of all disciplines, including nursing, in order to judge the integrity and usefulness of the evidence and conclusions made in an article. In general, this skill is automatic for many practitioners and researchers who already possess a good working knowledge of research methodology, including: hypothesis development, sampling techniques, study design, testing procedures and instrumentation, data collection and data management, statistics, and interpretation of findings. For graduate students and junior faculty who have yet to master these skills, completing a formally written article critique can be a useful process to hone such skills. However, a fundamental knowledge of research methods is still needed in order to be successful. Because there are few published examples of critique examples, this article provides the practical points of conducting a formally written quantitative research article critique while providing a brief example to demonstrate the principles and form. Keywords: quantitative article critique, statistics, methodology, graduate students

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Writing a Critique Paper: Seven Easy Steps

Were you assigned or asked by your professor to write a critique paper? It’s easy to write one. Just follow the following four steps in writing a critique paper and three steps in presenting it, then you’re ready to go.

One of the students’ requirements I specified in the course module is a critique paper. Just so everyone benefits from the guide I prepared for that class, I share it here.

To standardize the format they use in writing a critique paper, I came up with the following steps to make their submissions worthwhile.

Table of Contents

Step-by-step procedure in writing a critique paper.

I quickly wrote this simple guide on writing a critique paper to help you evaluate any composition you want to write about. It could be a book, a scientific article, a gray paper, or whatever your professor assigns. I integrated the essence of the approach in this article.

The critique paper essentially comprises two major parts, namely the:

1) Procedure in Writing a Critique Paper, and the

First, you will need to know the procedure that will guide you in evaluating a paper. Second, the format of the critique paper refers to how you present it so that it becomes logical and scholarly in tone.

The Four Steps in Writing a Critique Paper

Here are the four steps in writing a critique paper:

To write a good critique paper, it pays to adhere to a smooth flow of thought in your evaluation of the piece. You will need to introduce the topic, analyze, interpret, then conclude it.

Introduce the Discussion Topic

Introduce the topic of the critique paper. To capture the author’s idea, you may apply the  5Ws and 1H approach  in writing your technical report.

That means, when you write your critique paper, you should be able to answer the Why , When , Where , What , Who , and How questions. Using this approach prevents missing out on the essential details. If you can write a critique paper that adheres to this approach, that would be excellent.

Here’s a simplified example to illustrate the technique:

The news article by John Doe was a narrative about a bank robbery. Accordingly, a masked man  (Who)  robbed a bank  (What)  the other day  (When)  next to a police station  (Where) . He did so in broad daylight  (How) . He used a bicycle to escape from the scene of the crime  (How) . In his haste, he bumped into a post. His mask fell off; thus, everyone saw his face, allowing witnesses to describe him. As a result, he had difficulty escaping the police, who eventually retrieved his loot and put him in jail because of his wrongdoing  (Why) .

Hence, you give details about the topic, in this case, a bank robbery. Briefly describe what you want to tell your audience. State the overall purpose of writing the piece and its intention.

Analyze means to break down the abstract ideas presented into manageable bits.

What are the main points of the composition? How was it structured? Did the view expressed by the author allow you, as the reader, to understand?

If you want to split a log, what would you do? Do you use an ax, a chainsaw, or perhaps a knife? The last one is out of the question. It’s inappropriate.

Now, you are ready to interpret the article, book, or any composition once the requisites of analysis are in place.

Visualize the event in your mind and interpret the behavior of actors in the bank robbery incident. You have several actors in that bank heist: the robber, the police, and the witnesses of the crime.

Imagine, his mode of escape is a bicycle. What got into him? Maybe he did not plan the robbery at all. Besides, there was no mention that the robber used a gun in the heist.

If we examine the police’s response, they were relatively quick. Right after the robber escaped the crime scene, they appeared to remedy the situation. The robber did not put up a fight.

If we look at the witnesses’ behavior, we can discern that perhaps they willingly informed the police of the bank robber’s details. They were not afraid. And that’s because the robber appears to be unarmed. But there was no specific mention of it.

Assess or Evaluate

Finally, judge whether the article was a worthwhile account after all. Did it meet expectations? Was it able to convey the information most efficiently? Or are there loopholes or flaws that should have been mentioned?

Format of Presenting the Critique Paper

The logical format in writing a critique paper comprises at least three sections: the introduction, the body, and the conclusion. This approach is systematic and achieves a good flow that readers can follow.

Introduction

In any scientific article, there is always a thesis that guides the write-up. A thesis is a statement that expresses what the author believes in and tries to test in his study. The investigation or research converges (ideally) to this central theme as the author’s argument.

If you need more information about this, please refer to my previous post titled “ How to Write a Thesis .”

How is the introduction of a critique paper structured? It follows the general guidelines of writing from a broad perspective to more specific concerns or details. See how it’s written here:  Writing a Thesis Introduction: from General to Specific .

This section is similar to the results and discussion portion of a scientific paper. It describes the outcome of your analysis and interpretation.

Besides, who wants to adopt the perspective of an author who has not even got hold of a mobile phone if your paper is about  using mobile phones to facilitate learning during the pandemic caused by COVID-19 ? Find a more recent one that will help you understand the situation.

Objectively examine the major points presented by the author by giving details about the work. How does the author present or express the idea or concept? Is he (or she) convincing the way he/she presents his/her paper’s thesis?

Therefore, always find evidence to support your position. Explain why you agree or disagree with the author. Point out the discrepancies or strengths of the paper.

If you have read up to this point, then thank you for reading my musings. I hope that helped you clarify the steps in writing a critique paper. A well-written critique paper depends on your writing style.

Read More : How to Write an Article with AI: A Guide to Using AI for Article Creation and Refinement

Final Tip : Find a paper that is easy for you to understand. In that way, you can clearly express your thoughts. Write a critique paper that rocks!

Related Reading

Ragins, B. R., & Sundstrom, E. (1989). Gender and power in organizations: A longitudinal perspective. Psychological bulletin , 105 (1), 51.

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Dr. Regoniel, a hobbyist writer, 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.

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Research essentials. How to critique quantitative research

Affiliation.

• 1 Anglia Ruskin University.
• PMID: 26558974
• DOI: 10.7748/ncyp.27.9.12.s14

QUANTITATIVE RESEARCH is a systematic approach to investigating numerical data and involves measuring or counting attributes, that is quantities. Through a process of transforming information that is collected or observed, the researcher can often describes a situation or event, answering the 'what' and 'how many' questions about a situation ( Parahoo 2014 ).

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• What's the object of object working memory in infancy? Unraveling 'what' and 'how many'. Kibbe MM, Leslie AM. Kibbe MM, et al. Cogn Psychol. 2013 Jun;66(4):380-404. doi: 10.1016/j.cogpsych.2013.05.001. Epub 2013 Jun 12. Cogn Psychol. 2013. PMID: 23770623
• Understanding the basic aspects of research papers. Lee P. Lee P. Nurs Times. 2006 Jul 4-10;102(27):28-30. Nurs Times. 2006. PMID: 16850704
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How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

• Parenthetical citation: (OpenAI, 2023)
• Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.

We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

Update: APA Journals has published policies on the use of generative AI in scholarly materials .

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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REVIEW article

One of the major challenges of masking the bitter taste in medications: an overview of quantitative methods for bitterness.

• 1 Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
• 2 School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
• 3 Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Province Engineering Laboratory for Clinical Evaluation Technology of Chinese Medicine, Zhengzhou, China
• 4 Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-Constructed by Henan Province, Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
• 5 Zhengzhou Traditional Chinese Medicine Hospital, Zhengzhou, China
• 6 Third Level Laboratory of Traditional Chinese Medicine Preparations of the State Administration of Traditional Chinese Medicine, Zhengzhou, China

Objective: The aim of the present study was to carry out a systematic research on bitterness quantification to provide a reference for scholars and pharmaceutical developers to carry out drug taste masking research. Significance: The bitterness of medications poses a significant concern for clinicians and patients. Scientifically measuring the intensity of drug bitterness is pivotal for enhancing drug palatability and broadening their clinical utility.

Methods: The current study was carried out by conducting a systematic literature review that identified relevant papers from indexed databases. Numerous studies and research are cited and quoted in this article to summarize the features, strengths, and applicability of quantitative bitterness assessment methods.

Results: In our research, we systematically outlined the classification and key advancements in quantitative research methods for assessing drug bitterness, including in vivo quantification techniques such as traditional human taste panel methods, as well as in vitro quantification methods such as electronic tongue analysis. It focused on the quantitative methods and difficulties of bitterness of natural drugs with complex system characteristics and their difficulties in quantification, and proposes possible future research directions.

Conclusion: The quantitative methods of bitterness were summarized, which laid an important foundation for the construction of a comprehensive bitterness quantification standard system and the formulation of accurate, efficient and rich taste masking strategies.

1 Introduction

As widely recognized, the axiom “good medicine tastes bitter” epitomizes a fundamental attribute of pharmaceuticals, with many drugs exhibiting a bitter taste ( Bahia et al., 2018 ). Our investigation revealed that bitter herbs or decoction pieces constituted 49.0% of the 2020 edition of the “Chinese Pharmacopoeia" ( Lin et al., 2016 ), while 66% of compounds cataloged in the Drug Bank library were projected to possess a bitter taste ( Dagan-Wiener et al., 2017 ). The Bitter DB database archives over 1,000 bitter molecules. Humans and animals exhibit heightened sensitivity to bitterness perception, capable of discerning bitterness even at lower concentrations ( Aliani and Eskin, 2017 ). The intrinsic aversion to bitterness among humans significantly impacts patient medication adherence ( Beauchamp, 2016 ; Boesveldt and de Graaf, 2017 ), thereby influencing clinical efficacy ( Amin et al., 2018 ; Zheng et al., 2018 ). The prevalent distaste for medications is frequently cited as a primary reason for patient non-compliance, particularly among children ( Clapham et al., 2023 ). Surveys indicate that over 90% of pediatricians identify drug taste and palatability as major barriers to completing clinical treatments ( Milne and Bruss, 2008 ). In a survey involving nearly 700 European children, 63.7% of respondents attributed difficulty in medication intake to dislike for the drug’s taste ( Nordenmalm et al., 2019 ). Peter Drucker, often regarded as the father of modern management, emphasized the necessity of objective and accurate quantitative evaluation of drug bitterness as a crucial prerequisite for understanding its taste patterns and enhancing palatability.

Bitterness primarily arises from the activation of TAS2R (also referred to as bitter receptors). Upon binding of the bitter compound to the receptor, located prominently on taste receptor cells (TRCs), a signal transduction cascade ensues. This activation prompts TAS2R to catalyze the dissociation and liberation of Gβ3/Gγ13 subunits from the Gβ3/Gγ13 heterotrimeric receptor, thereby activating phospholipase C (PLCβ2). Subsequently, PLCβ2 catalyzes the breakdown of phospholipid PIP2, yielding inositol-1,4,5-phosphate (IP3). IP3, in turn, binds to IP3 receptors on the endoplasmic reticulum, eliciting the release of intracellularly stored calcium ions (Ca 2+ ). The elevated intracellular Ca 2+ concentration ([Ca 2+ ]i) prompts the opening of membrane-associated TRPM5 channels, facilitating the influx of sodium ions (Na + ). This ion exchange initiates receptor cell membrane depolarization, triggering the release of adenosine triphosphate (ATP) through calcium homeostasis regulator one and 3 (CALHM1 and CALHM3) channels. Ultimately, the liberated ATP activates purinergic receptors on afferent nerve fibers, converting the chemical signals of bitter compounds into electrical signals, which are relayed to the taste nucleus of the brainstem. Subsequently, these signals are transmitted to the thalamus and eventually to the taste cortex of the cerebral cortex, culminating in the perception of bitterness ( Finger et al., 2005 ; Roper, 2007 ; Ma et al., 2018 ), as shown in Figure 1 .

Figure 1 . Activation mechanism of human TAS2R and signal transduction pathway of bitterness stimulation.

According to the law of taste and bitter perception, the factors affecting drug bitterness are affected by the difference of genes and receptors. Single nucleotide polymorphisms (SNPs) in genes encoding bitter taste receptors (TAS2R) leading to varied responses to bitter stimuli. Consequently, individuals with one gene form may perceive strong bitterness, while those with another gene form may not perceive bitterness at all ( Hayes et al., 2011 ; Mennella et al., 2011 ; Roudnitzky et al., 2011 ; Roudnitzky et al., 2015 ; Nolden and Feeney, 2020 ). The heterologous functional expression experiments have revealed that 25 TAS2Rs exhibit varying degrees of regulation characteristics as bitter taste receptors ( Meyerhof et al., 2010 ). The ability of bitter compounds to stimulate TAS2Rs varies. Compared to the activation of a single receptor, simultaneous activation of multiple receptors by a compound can elicit a larger cellular or neural response, thereby increasing bitterness. Additionally, the factors that affect the bitterness also include the following: (1) Chemical structure. According to the three-point contact theory of bitterness formation, bitter molecules can be categorized into two groups ( Hans-Dieter et al., 1985 ; Meyerhof, 2005 ): one consisting of hydrophobic lipophilic compounds with limited water solubility, such as olefins and terpenoids; the other comprising highly polar compounds capable of forming robust hydrophobic interactions, exemplified by alkaloids ( Zeng, 1990 ). (2) Substance concentration. Within a specific concentration range of the same drug, bitterness demonstrates a positive correlation with concentration. Studies have shown that a logarithmic relationship between human sensory intensity and stimulus physical quantity under moderate intensity stimula56tion conditions (S = KlgR) ( Omür-Ozbek and Dietrich, 2008 ; Li et al., 2016 ). (3) Interaction between substances. The bitterness between substances can be affected by electrostatic interaction ( Schalk et al., 2018 ), hydrophobic interaction ( Ogi et al., 2015 ), covalent bonding ( Bohin et al., 2013 ), and inclusion interaction ( Shah and Mashru, 2008 ). (4) pH. It is established that certain acidic peptides can mitigate bitterness ( Sakurai et al., 2009 ). Bitterness inhibition of sesquiterpene lactone can be achieved by pH control ( Yanagisawa and Misaka, 2021 ). (5) Solution viscosity. Studies have found that an increase in the viscosity of the resulting aqueous solvent leads to a reduction in taste intensity. Additionally, studies have indicated that emulsions demonstrate bitterness inhibition effects on KCl and/or caffeine compared to aqueous solutions ( Torrico et al., 2015 ).

In the realm of food taste assessment, extensive studies have focused on quantitatively evaluating taste attributes. For instance, the bitterness of beer is commonly assessed based on the concentration of isomerized α-acids, the primary source of beer bitterness. Methods such as European Bitterness Units (EBU) ( Polshin et al., 2010 ; Rudnitskaya et al., 2010 ), International Bitterness Units (IBU) ( Howard, 1968 ; Donley and Anheuser, 1992 ; Christensen et al., 2005 ), E.B.C. Bitterness Units ( Bishop, 2013 ), and Bitterness Units (BU) ( Tomlinson et al., 2013 ) are utilized for this purpose. Caffeine, containing numerous bitter compounds, undergoes bitterness intensity evaluation using Sensory Lexicon ( Shibamoto et al., 1981 ; Ginz and Engelhardt, 2000 ; Aree, 2019 ). These established quantification methods in the field of food taste can serve as valuable references for assessing drug bitterness. While the bitterness of chemical drugs can be accurately measured based on the content of bitter compounds due to their clear and singular composition, natural drugs encompass a multitude of bitter substances, intricate substance interactions, and a diverse array of taste components. These complexities confer inherent bitterness to natural drugs, emphasizing the importance of elucidating the mechanisms underlying their bitter taste and exploring tailored quantification methodologies.

As research into drug palatability continues to evolve, investigators have undertaken studies on the measurement and quantitative assessment of drug bitterness ( Gaudette and Pickering, 2013 ). These studies primarily encompass in vivo and in vitro methods. In vivo evaluation methods include the traditional human taste panel method (THTPM) ( Miyanaga et al., 2003 ; Rudnitskaya et al., 2013 ), taste strips test method ( Liu et al., 2020 ), animal behavior tests ( Lemon et al., 2019 ), and facial expression analysis ( Lemon et al., 2019 ), with THTPM recognized as the gold standard for taste assessment ( Gunaratne et al., 2019 ). In vitro bitterness detection methods mainly consist of electronic tongue methods (ETM) ( Hui et al., 2014 ; Immohr et al., 2017 ), and cell-based evaluations ( Qingjun and Ping, 2009 ), among others. Following an understanding of drug bitterness, researchers have employed taste-masking techniques utilizing flavoring agents, bitterness inhibitors, cyclodextrins, and nanoemulsions ( Hu et al., 2023 ). These methods have contributed positively to advancing the objective measurement and precise control of bitterness, thereby enhancing clinical drug compliance. Nonetheless, despite these advancements, there remains a lack of systematic summarization of research methods for bitterness quantification, as each method possesses unique characteristics and applications. This article seeks to analyze the research progress in drug bitterness quantification, delineate the primary factors influencing drug bitterness, and compile the methodologies for bitterness quantification. The aim is to foster a systematic comprehension of the principles, methodologies, and attributes of bitterness quantification, thereby offering insights for research endeavors in areas such as drug bitterness intensity evaluation, taste masking, and related fields.

2 Quantitative methods for bitterness

In light of the various factors influencing drug bitterness as outlined above, researchers are continuously innovating quantitative methods for assessing drug bitterness. Broadly, these methods can be categorized into two types: one involves quantifying the bitterness of drugs by measuring bitter molecules, while the other quantifies drug bitterness based on the intensity of taste stimulation. The results of both methods are elaborated upon below.

THTPM is a method used to assess the taste of drugs or food, relying on specific technical specifications and processes and utilizing the taste sense of the evaluation group ( Medicine, 2024 ). This method falls under the category of quantifying bitterness based on the intensity of human taste stimulus. The main methods were illustrated in Figure 2 . Currently, a range of internationally recognized standards for quantitative sensory evaluation have been established ( Clapham et al., 2023 ), paving the way for researchers to conduct various explorations into quantitative bitterness assessment.

Figure 2 . Quantitative research method of bitterness based on the THTPM. (A) Schematic representation of the quantitative description analysis (QDA). Based on Huan-Huan Li, 2019 ( Li et al., 2019 ). (B) Schematic representation of the general labeled magnitude Scale (gLMS) ( Deng et al., 2022 ) (C) Schematic representation of Rank bitterness intensity (RB) and standard apparent bitterness (SAB). Representative images were our own work ( Li et al., 2023 ). (D) Schematic representation of Ratio of bitterness (ROB). Representative images were our own work ( Gao et al., 2022 ). (E) chematic representation of Molecular bitterness (MB) and equivalent molecular bitterness (EMB). Representative images were our own work ( Wang et al., 2022 ).

2.1.1 Quantitative description analysis (QDA)

QDA is a method used to characterize the sensory properties and intensity of drugs. Within QDA, standard reference materials provide the bitterness value against which samples are compared for evaluation. For instance, when assessing the bitterness of Huanglian Jiedu Decoction (HLJDD), scholars provide a reference standard of 0.2 g/mL HLJDD with a bitterness value of 10. Evaluators use this reference standard to assess the bitterness of HLJDD after masking with [mPEG2000-PLLA2000, γ-CD, and neotame], revealing a reduction in HLJDD bitterness ( Ke et al., 2022 ).

However, the methods mentioned above lack the ability to provide information on the temporal aspects of bitterness perception. Bitterness exhibits unique temporal characteristics, taking more time to reach peak intensity in the mouth and longer to return to baseline ( Guinard et al., 2010 ). Additionally, with repeated intake, the perceived intensity of bitterness tends to increase ( Guinard et al., 1986 ). The presence of polyphenols (in red wine) and isohumulone (in beer) may amplify the bitterness of these beverages during consumption ( Guinard et al., 1994 ; Noble, 1994 ). Hence, some scholars employ the time-intensity method to dynamically assess bitterness throughout the entire perception period. In a study evaluating the bitterness of berberine hydrochloride orally disintegrating tablets, researchers instructed subjects to record bitterness intensity levels at various intervals (30 s, 1 min, 2 min, 5 min, 10 min) within a span of 10 min. Bitterness intensity was rated on a scale from 0 to 3. The findings indicated that when the ratio of the drug to the pH-dependent polymer Eudragit E100 increased from 1:0 to 1:0.8, the bitterness of the drug microcapsules significantly decreased, reaching zero bitterness by the second minute ( Hu et al., 2013 ).

Quantitative descriptive analysis also encompasses the evaluation of bitterness across different taste categories. Some researchers ( Sook Chung and Lee, 2012 ) categorized bitterness into distinct types such as alcohol bitterness, coffee bitterness, grapefruit pith bitterness, medicinal bitterness, and cocoa bitterness. Each type of bitterness corresponds to unique definitions and references, posing higher demands on the evaluator’s assessment skills.

2.1.2 General labeled magnitude scale (gLMS)

The gLMS is a psychophysical scale used for sensory testing to evaluate the taste and aftertaste of various stimuli. It comprises a 150 mm line spanning from the bottom to the top of the vertical axis. The scale includes descriptors such as “almost undetectable” (2.1 mm; 1.4 units), “weak” (9 mm; 6 units), “medium” (25.5 mm; 17 units), “strong” (52.05 mm; 34.7 units), “very strong” (78.75 mm; 52.5 units), and “the most imaginable sensation” (150 mm; 100 units). The scale presents adjectives to evaluators without numerical values. Experimenters place the adjectives on the scale in a semi-logarithmic manner based on determined intervals to generate data equivalent to magnitude estimation ( Green et al., 1993 ; Green et al., 1996 ). Subsequently, numerical data are calculated based on the scale. Using the gLMS method, the intensity of different bitter substances can be directly compared. For instance, Deng ( Deng et al., 2022 ) employed the gLMS to conduct sensory tests on adults, comparing the bitterness intensity of prednisolone solution with that of quinine. The results indicated that the bitterness level of prednisolone saturated solution (average gLMS score: 46.8) was similar to that of a 1 mM quinine solution (average gLMS score: 40.1). However, substantial individual differences may exist in gLMS assessment. For example, researchers measured the minimum and maximum values of 1 mM quinine as 8.7 and 90.0, respectively ( Deng et al., 2022 ). Furthermore, variations in sensory test data among different researchers also occur. While one study ( Deng et al., 2022 ) reported the average gLMS score of 1 mM quinine as 40.1, another study ( Cruickshanks et al., 2009 ) documented a gLMS score of 50 at the same concentration. Hence, stringent and standardized conditions are necessary for bitterness evaluation using this method.

2.1.3 Rank bitterness intensity (RB) and standard apparent bitterness (SAB)

In bitterness evaluation, descriptions like “unbearable bitterness,” “a little bitterness,” and “almost no bitterness” often arise, necessitating a method for grading bitterness. In a study on chlorphenamine maleate bitterness, researchers categorized bitterness into five levels: (A) 5: very strong bitterness, (B) 4: strong bitterness, (C) 3: medium bitterness, (D) 2: slightly bitter, and (E) 1: tasteless. Using the uncomplexed pure drug as a control with an average bitterness value of 5, subjects were instructed to compare the bitterness of different drug resin complexes (DRC) with the control and express the perceived bitterness level. The findings revealed that Indion-234, Tulsion-343, and Tulsion-344 effectively masked the bitterness of chlorphenamine maleate, with the bitterness of the drug in DRC decreasing as the ratio of drug to resin increased ( Yewale et al., 2013 ).

Inspired by this approach, some scholars introduced the concept of “RB” ( Wang et al., 2021 ), using berberine (BBR) as a reference. After volunteers pre-tested multiple concentrations, different concentrations of BBR corresponding to each bitterness level were determined ( Table 1 ). The practical application of graded bitterness involves evaluating unknown bitterness samples by referencing the bitterness level and value range of the reference sample group. Once graded, the specific bitterness value is assigned according to the corresponding bitterness range of each grade. Bitterness determined for the reference material in the solution state is termed “Standard Reference Bitterness (SRB)" ( Liu et al., 2019 ; Zhang et al., 2021 ). Bitterness of other drugs established based on SRB as a reference in the solution state is referred to as “Standard Apparent Bitterness (SAB)", and the level of standard apparent bitterness is known as “Standard Apparent Rank Bitterness (SARB)". Employing these methods enables us to comprehend the bitterness levels and bitterness profiles of different bitter substances.

Table 1 . Bitterness ranking and concentration of corresponding reference samples.

Shi et al. (2013) utilized a berberine hydrochloride aqueous solution as the reference solution and applied the THTPM method to assess the bitterness grade of six notable bitter Chinese herbal decoction pieces, including Cortex Phellodendri, Radix Gentianae, Herba Andrographis, Radix Ginseng, and Nelumbinis Plumulae. They investigated the taste-masking effect of β-CD at various mass fractions. Results indicated that the taste-masking effect improved with increasing β-CD concentration. With the exception of Cortex Phellodendri, the bitterness of the liquid after adding 3% β-CD was within a low range (0.65 ± 0.05), all falling into the almost no bitterness grade. This suggested that β-CD could effectively mask the bitter taste of traditional Chinese medicine. ( Li et al., 2011 ). employed the THTPM method to assess the water decoction of 35 different single Chinese medicine decoction pieces with varying bitterness, using berberine hydrochloride as a reference. They preliminarily obtained the bitterness value and distribution characteristics of the water decoction of Chinese medicine decoction pieces, providing a crucial foundation for subsequent taste-masking research ( Liu et al., 2012 ; Liu et al., 2013 ). conducted research on matrine at different concentrations, with 20 evaluators assessing its bitterness level and specific standard apparent bitterness value. They evaluated sample bitterness using three methods: the order evaluation method (OEM ranking method), score evaluation method (SEM), and integrated score evaluation method (ISEM). Ultimately, the three methods were comprehensively analyzed based on sorting accuracy, judgment sensitivity, assignment precision, and fitting degree, with the ISEM taste evaluation method proving to be the most effective. In order to further explore the bitterness superposition rules of different bitter substances ( Zhang et al., 2021 ), selected nine types of Chinese medicinal slices as research carriers. On the basis of establishing a predictive model between the quality concentration of the monomer slice carrier and the bitterness tasted by mouth, they explored the relationship between the bitterness tasted by mouth when measuring the superposition of binary and ternary systems and the bitterness and quality concentration of the monomer slice. The research found that the quality concentration of the monomer slice can be well fitted to the predictive equation of the bitterness of the superimposed slices, and the contribution rate of Huanglian to the superimposed bitterness is often greater than that of the other components, fully confirming a Chinese saying, “A mute eats Huanglian, and the bitterness is unspeakable."

The bitterness determination method based on RB and SAB offers a direct approach to determining the bitterness of various substances, including monomeric compounds, decoction pieces, and compound decoctions. However, the measurement process may be influenced by the intrinsic structure, concentration, and temperature of the molecule. Therefore, controlling appropriate external conditions during the measurement process is essential.

2.1.4 Ratio of bitterness (ROB)

In the sweetness evaluation method, there exists a calculation method known as “the relative sweetness value (RS)”, utilized for comparing different sweeteners. Researchers established the sweetness (Sr) of a 5% sucrose solution ( C r) as one and determined the mass concentration of other sweet substances equivalent to their sweetness. The RS of the sweet compound was then calculated using the formula RSs = C r/ C s × Sr ( Park et al., 2017 ). Building upon this concept, researchers proposed a method for determining the “Ratio of Bitterness (ROB)” of bitter substances ( Li et al., 2023 ). Specifically, they determined the specific bitterness (ROBr) of a BBR solution with a mass concentration of 0.05 mg/mL ( C r) as one and obtained the mass concentration of other bitter substances equivalent to their bitterness. The ROB of the bitter compound was then calculated using the formula “ROBs = C r/ C s × ROBr”. Due to the significant variance in their values, their natural logarithm is termed the ROB-index (ROBI). Serving as an absolute quantitative index, ROB reflects a fundamental attribute of bitter substances, facilitating a straightforward comparison of bitterness among different bitter substances. Following these principles, researchers successfully determined the ROB of six bitter drug monomers, offering a new bitterness scale for comparing bitterness across various bitter drug monomers and enhancing the scope of research on drug bitterness comparison scales ( Li et al., 2023 ).

2.1.5 Molecular bitterness (MB) and equivalent molecular bitterness (EMB)

Many drugs exhibit a bitter taste despite having different chemical structures. The emergence of bitterness is linked to factors such as the shape, size, and properties of functional groups within the molecule, as well as their positions. Eitan Margulis et al ( Margulis et al., 2021 ) successfully constructed a machine learning tool, termed “BitterIntense,” based on the chemical structural features of molecules. By calculating molecular descriptors, the tool classifies them into categories of “very bitter” or “not very bitter” with an accuracy rate of over 80%. This is significant for the early stages of drug development, as it allows for the rapid identification of compounds with intense bitterness. However, this method is a simple binary classification of bitterness intensity. How to establish a more precise bitterness intensity prediction algorithm based on molecular structural features remains a question that scholars are currently exploring. Liu et al., 2012 addressed the influence of concentration on the bitterness of bitter substances and introduced the concept of “molecular bitterness,” which pertains solely to the properties of drug molecules. The bitterness threshold concentration (BTC) of both the standard bitterness substance and the compound under examination was determined using the “minimum limit method,” representing the lowest concentration at which bitterness is detected by half of the volunteers. The Molecular Bitterness (MB) under the standard bitterness substance BTC was set as 1 (typically using berberine hydrochloride as the reference bitter substance, with an MB of one under BTC). Calculating the MB of the test compounds involved the formula “MBs = C r/ C s × MBr” (where C r signifies the BTC of the standard bitter molecule; MBr denotes the MB of standard bitter molecules; C s represents the BTC of unknown bitter molecules; MBs represents the MB of unknown bitter molecules ( Heath et al., 2006 ; Bora et al., 2008 ; Li Xuelin et al., 2013 ; Jelvehgari et al., 2014 ; Gao et al., 2022 )). Given the substantial variance in BTC among different bitter molecules, MB values differ significantly across substances. Therefore, the introduction of the “molecular bitterness index” (MB-Index, MBI) involves taking the natural logarithm of MB to normalize the magnitude difference, facilitating a more straightforward comparison of bitterness across different substances. Using this method, ( Gao et al., 2022 ) calculated the MB of 19 bitter monomer components such as quinine (alkaloids), naringin (glycosides), andrographolide (terpenes), and L-arginine (peptides) to be 0.8398, 0.0551, 0.0058, and 0.0002, respectively. The corresponding MBI values were −0.1746, −2.8982, −5.1447, and −8.3669, respectively, effectively illustrating the bitterness characteristics of various bitter components in a simple and intuitive manner.

The introduction and application of the MB concept addressed the comparison of bitterness between compounds. However, for natural medicine decoction pieces, and even compounds composed of multiple natural medicine decoction pieces, the evaluation extends beyond a single compound to encompass the combination of various bitter compounds. The change in bitterness value within such complex systems after combination is intricate. With numerous types of natural medicines, there’s an urgent need to establish an objective and appropriate bitterness evaluation method. Taking bitter natural medicines as an example, the current 2020 edition of the “Chinese Pharmacopoeia” includes a total of 2,711 natural medicines. Among them, 133 natural medicines exhibit a single bitter taste, comprising one very bitter, 14 extremely bitter, 47 bitter, one slightly bitter, and 70 slightly bitter ( Supplementary Table S1 ). Additionally, 180 natural medicines possess not only a bitter taste but also other flavors ( Supplementary Table S2 ). There are also nuanced differences in the taste descriptions of various natural medicines; for instance, Gentiana is described as “very bitter,” Sophorae Tonkinensis as “extremely bitter,” Bletilla as “bitter,” Eucommia as “slightly bitter,” and Lily bulb as “a little bitter” However, the distinction in bitterness between each description remains unknown. Furthermore, natural drugs described as ‘extremely bitter,’ such as Sophora flavescens and Aloe vera, pose the question: is their bitterness identical? Drawing from the MB calculation principle and bitterness measurement, Liu et al. ( Gao et al., 2022 ) introduced the concept of “Equivalent Molecular Bitterness (EMB)” for complex systems. This involves determining the BTC of standard bitter substances and unknown complex systems using the “minimum limit method.” The MBr under the standard bitter substance BTC is defined as 1 (typically using berberine hydrochloride as the reference bitter substance, with an MBr of one under BTC). Subsequently, the EMB calculation formula for other bitter Chinese herbal decoction pieces is “EMBs = C r/ C s × MBr,” where C r represents the BTC of berberine hydrochloride, and C s denotes the BTC of unknown Chinese herbal pieces. The natural logarithm of this ratio is termed the EMB-index (EMBI) ( Wang et al., 2022 ). measured the EMB and EMBI of 23 kinds of bitter Chinese herbal pieces using the aforementioned methods and established a quantitative method for determining the bitterness of bitter Chinese herbal pieces. This comparative analysis of the bitterness characteristics of different types of Chinese herbal pieces offers valuable insights, laying a robust foundation for the accurate masking of natural drugs within complex systems.

The comprehensive review reveals that researchers approach quantitative analysis of bitterness from diverse perspectives and levels using THTPM as a foundation. Each method presents distinct advantages, limitations, and applicability ( Table 2 ). When embarking on quantitative investigations into bitterness, it is crucial to select appropriate methodologies tailored to the study’s objectives and the nature of the research subject.

Table 2 . Analysis of the characteristics of various evaluation indexes of bitterness based on THTPM measurements.

Sensory group evaluation poses significant challenges due to medical ethics considerations, associated health risks, and the substantial costs of personnel training. Moreover, the inherent subjectivity among individuals can lead to fatigue, slow evaluation speeds, and a limited sample size. Throughout the evaluation process, there is a risk of sample perception migration and perception saturation, thereby imposing constraints on the assessment of taste within the general population ( Legin et al., 2004 ). The Gustation Analytical Fingerprint Technique (GFAT) represents a recent development in taste recognition and detection technology, relying on taste sensors and chemical information processing methods. These taste sensors function as intelligent recognition electronic systems, emulating the human taste mechanism to generate signals (optical, electrochemical, electrophysiological). They possess the capability to discern subtle differences in basic tastes, such as lingering or transient tastes. Notably, GFAT offers advantages such as rapid analysis, low cost, minimal sample preparation, and automation of analysis ( Rudnitskaya et al., 2010 ; Podrażka et al., 2017 ). Scholars have conducted a systematic evaluation of the application of ETM and sensory groups in taste assessments of pediatric drugs. The findings reveal that sensory tests for children are infrequent (10.3%), with ETM predominating in pediatric drug taste evaluations (57.5%), highlighting the efficacy of ETM ( Guedes et al., 2021 ). Over the past few decades, leveraging electronic tongue technology, researchers have successfully employed methods such as the conversion of electronic tongue taste information value ( Zeng et al., 2015 ; Li et al., 2016 ), bitter distance calculation, and the establishment of relationships between electronic tongue information and human sensory evaluation ( Ito et al., 1998 ; Uchida et al., 2001 ). These advancements have facilitated the quantitative analysis and prediction of various drug tastes, as shown in Figure 3 .

Figure 3 . Quantitative research method of bitterness based on the ETM. Representative images were our own work ( Rui-xin et al., 2013 ; Li et al., 2016 ; Gao et al., 2022 ).

2.2.1 Electronic tongue converts taste information value

Using the TS-5000Z multi-channel taste sensor as an illustration, it employs an artificial lipid membrane sensor technology akin to the functioning principle of human tongue taste cells. This sensor has the capability to objectively and digitally detect five basic tastes as well as flavor attributes such as “sharpness” and “richness.” In the TS-5000Z taste analysis system, Relative value (R) and Change of Membrane Potential caused by Adsorption (CPA) are commonly utilized ( Li et al., 2020 ). To initiate taste analysis, the taste sensor is immersed in a reference solution comprising a mixture of KCl and tartaric acid at a predetermined concentration, yielding the corresponding membrane potential, denoted as Vr. This reference solution is essentially tasteless. Subsequently, the sensor is submerged in the sample solution to determine the potential difference value of the solution potential (Vs.), which is then subtracted from Vr, termed as the first taste (R). Following a gentle cleanse of the taste sensor with the reference solution, it is re-immersed to detect the potential Vr’. The disparity between Vr’ and Vr is referred to as aftertaste (CPA), indicating the potential change induced by chemical adsorption.

The first taste and aftertaste can be calculated by Equations 1 , 2 :

Based on the initial taste and aftertaste values, a specific mathematical conversion is performed to derive the electronic tongue conversion taste information value (I.e.,), serving as a metric for quantitative bitterness evaluation ( Zeng et al., 2015 ). employed the electronic tongue technology-based taste analysis method to quantify the characteristics of Scutellaria baicalensis from various sources. By establishing a positive correlation between the bitterness, astringency, bitter aftertaste, astringency aftertaste, sour taste information of Scutellaria baicalensis, and the baicalin content, it was possible to infer the baicalin content in Scutellaria baicalensis. In a similar vein ( Jing et al., 2022 ), utilized electronic tongue technology to quantify the taste of 20 batches of Magnolia officinalis, assessing the taste of six monomer compounds. Pearson correlation analysis was employed to ascertain the correlation between eight chemical components and the taste sensor response value. The investigation revealed a significant positive correlation between honokiol, magnolol, and spicy menthol magnolol in Magnolia officinalis, and the bitter taste and bitter aftertaste detected by the electronic tongue.

2.2.2 Distance of bitterness

2.2.2.1 distance of bitterness in multidimensional space.

Using the French ASTREE electronic tongue method as a case study, the quantification of bitterness index relies on data gathered from seven sensors within the electronic tongue apparatus. Through meticulous data processing, a numerical value is derived, providing a quantitative or semi-quantitative representation of the drug’s bitterness. This value is termed the Bitterness Distance (D). Utilizing chemometric techniques such as PCA, the spatial disparity between the sample under examination and a reference solution is computed. This approach mirrors principles found in cluster analysis and other methodologies, where the distance between samples is evaluated within a multidimensional space comprising various variables.

Distance serves as a metric to gauge the spatial separation between two entities. Common distance metrics encompass Euclidean distance, Mahalanobis distance, Ming’s distance, among others. The Euclidean distance (EUCLID), or Euclidean metric, stands out as a prevalent distance measure, delineating the true geometric distance between two points within an m-dimensional space. Its applicability is underscored by its capacity to be expressed in a unified recursive formula, making it the most frequently utilized distance metric.

The Euclid and Standardized Euclid can be calculated by Equations 3 , 4 :

Where k represents the number of variables each sample possesses, with x i indicating the value of the first sample on the i-th variable, and y i representing the value of the second sample on the same variable. In the context of bitter samples, the Euclidean distance between samples exhibiting varying degrees of bitterness serves as a measure of the disparity in bitterness levels.

For instance, consider the compound BBR, which was formulated into samples of varying concentrations. Each sample, along with purified water, underwent analysis using an electronic tongue. The resulting dataset facilitated the direct calculation of its Euclidean distance, effectively quantifying the multidimensional space between them. Notably, a larger Euclidean distance between the sample and purified water signifies a higher bitterness level in the sample, and conversely, a smaller distance indicates lower bitterness.

2.2.2.2 Distance of bitterness in reduced-dimensional space

The data collected by the electronic tongue underwent reduction via Principal Component Analysis (PCA) and similar techniques. Subsequently, based on these findings, the distance between each sample and the reference solution within the principal component space (whether in two-dimensional, three-dimensional, or other dimensions) was computed to determine the relative bitterness of each sample.

In a two-dimensional or three-dimensional space, the Euclidean distance serves as the measure of separation between two points, delineating the extent of spatial disparity, as shown in Equations 5 , 6 :

When employing PCA for dimensionality reduction analysis, we can compare the bitterness differences among samples by assessing the distance from each sample to the reference solution in both two-dimensional and three-dimensional spaces. Nakamura et al. conducted a study to assess the taste of orally disintegrating tablets (ODT) containing famotidine and amlodipine besylate using the Astree electronic tongue and THTPM. The palatability of the tablets was further evaluated using a 100 mm VAS scale. The findings indicated that both physical masking and organoleptic masking could enhance the palatability of famotidine and amlodipine. In the electronic tongue analysis, the Euclidean distance of samples subjected to physical masking, organoleptic masking alone, and in combination, was found to be smaller compared to unmasked drugs ( Nakamura et al., 2015 ). Liu et al. investigated bitter drug carriers employing BBR and Andrographis paniculata decoction, screening taste masking agents by assessing bitterness reduction values in reduced-dimensional or multi-dimensional space ( Liu et al., 2013 ; Rui-xin et al., 2013 ). Li et al. (2011) assessed the masking effect of various agents on berberine hydrochloride using bitterness distance, D, and bitterness reduction distance, ΔD ( Li et al., 2013 ).

While the results derived from PCA analysis slightly underperform compared to multi-dimensional space distance, they offer a more intuitive representation through two-dimensional or three-dimensional maps, overcoming the graphical limitations of multi-dimensional spaces. Moreover, data standardization aids in further reducing system errors. However, it is important to note that this method is only applicable to distinguishing bitterness within the same component.

2.2.3 The relationship between electronic tongue taste information value and THTPM

The electronic tongue taste information is typically expressed through relative response values or bitterness values. Establishing the relationship between electronic tongue taste information and THTPM involves data-driven modeling and prediction, relying on experimental data and mathematical methods. Several studies have demonstrated a strong correlation between taste assessed by electronic tongue and human taste perception ( Ito et al., 2013 ; Wang et al., 2013 ; Maniruzzaman et al., 2014 ; Maniruzzaman and Douroumis, 2015 ). In recent years, there has been a proliferation of applications for quantitatively predicting bitterness using electronic tongue. For instance, Li ( Li et al., 2016 ) utilized berberine hydrochloride as a reference and matrine and oxymatrine as model drugs to establish a bitterness prediction model (BPM) based on THTPM bitterness ratings and data from the TS-5000Z electronic tongue sensor. The results indicated a significant correlation between taste bitterness and electronic tongue bitterness (R 2 matrine = 0.8955, R 2 oxymatrine = 0.9793). The electronic tongue-based bitterness prediction model for matrine and oxymatrine exhibited high accuracy (R 2 matrine = 0.9639, R 2 oxymatrine = 0.9535). ( Liu et al., 2014a ) developed a BPM for berberine hydrochloride using a genetic algorithm-back propagation neural network (GA-BP), incorporating bitterness intensity evaluated by sensory groups and data provided by electronic tongue. The model demonstrated excellent fitting (R 2 = 0.99965) and could effectively predict the bitterness of berberine hydrochloride across different concentrations, serving as a reference for developing BPMs for other drugs. Chen ( Chen et al., 2020 ) presented a biosensor utilizing Drosophila odorant binding protein (OBP) as a biosensitive material. This biosensor was employed to study typical bitter molecules such as denatonium, quinine, and berberine using electrochemical impedance spectroscopy. The findings revealed significant binding properties between the bitter molecules and OBP, displaying a linear response within the concentration range of 10-9-10–6 mg/mL, indicating broad application prospects for the OBP-based biosensor. (Xue, 2022) employed Weibull curve fitting to evaluate the taste of oseltamivir phosphate and ginkgo leaves, along with electronic tongue data, enabling quantitative description of bitterness. The prediction model’s accuracy and superiority were assessed through cross-validation. Additionally, the electronic tongue method’s ability to predict the bitterness of bitter substances was validated against THTPM results.

In general, there exists a certain correlation between the taste information provided by the electronic tongue and the outcomes from THTPM, although this correlation may not always be consistent. Numerous factors contribute to this, including the type of electronic tongue, sensor selection, signal processing methods, data analysis techniques, standardization of tasting methods, and the training of evaluators. Due to variations in perception mechanisms and sensitivity between electronic tongues and human taste, the electronic tongue may not fully capture the nuanced characteristics of individual taste perception ( Uchida et al., 2001 ). Consequently, the relationship between electronic tongue taste information and taste assessment methods requires calibration and validation specific to the samples and conditions at hand and cannot be generalized.

2.3 Taste strips and filter paper disc method

Taste strips (TS) consist of filter paper infused with taste substances. When evaluating, the evaluator places the TS on the tongue’s center, closes the mouth, and gradually moves the tongue, allowing saliva to dissolve the taste enhancer on the strip. After a designated period, the strip is removed for taste assessment, as shown in Figure 4 . Ranmal ( Ranmal et al., 2023 ) examined subjects’ hedonistic responses to bitter stimuli from TS. Findings revealed that as the concentration of quinine hydrochloride (QHCl) on TS increased, both children and adults showed heightened aversion to bitterness. Similarly, Schienle ( Schienle and Schlintl, 2020 ) utilized QHCl TS to gauge taste intensity, ranging from no sensation to “the strongest imaginable sensation of any kind.” Green ( Green et al., 2022 ) employed TS containing high and low concentrations of four tastes (sour, sweet, bitter, and salty) to assess taste function in healthy participants. Results indicated elevated recognition levels among participants exposed to high-concentration taste strips in laboratory settings.

Figure 4 . Quantitative research method of bitterness based on taste strips (TS). Based on ( Ranmal et al., 2023 ).

Another bitterness measurement method akin to the TS method is the filter paper disc method (FPD). KATARINA( Berling et al., 2011 ) employed FPD to assess evaluators’ perception thresholds for various flavors. Each flavor agent comprised five different concentrations. Using a scoring system from one to 6, where one indicates the lowest threshold, five represents the highest measurable threshold, and six signifies an unmeasurable high threshold, evaluators progressed from low to high concentrations until they correctly identified the taste, thus determining the recognition threshold. Results indicated standard thresholds for four flavors: bitter 1.9 ± 1.30, acid 2.3 ± 1.09, salty 2.5 ± 1.53, and sweet 2.6 ± 1.37, respectively, with bitterness identified at a lower concentration than other flavors.

The TS and FPD methods offer a straightforward, rapid, safe, and effective out-of-laboratory (OOL) sensory evaluation approach for assessing bitterness perception. Nonetheless, further research is warranted to establish a stronger correlation between the “local stimulation” method and the “full mouth” method based on the classical population taste evaluation method.

2.4 Facial expression analysis

Facial expressions serve as a rich source of emotional information. When individuals taste different flavors of medications, their facial expressions vary accordingly. For instance, tasting non-bitter Chinese medicine may elicit “neutral” expressions, whereas tasting bitter Chinese medicine may provoke expressions of “disgust,” characterized by tight frowns and clenched teeth. Facial expression recognition technology leverages facial expression data to objectively analyze human emotional responses. Utilizing this technology, we can extract facial expression features of individuals and employ suitable expression classification methods to objectively assess taste perception, as shown in Figure 5 .

Figure 5 . Quantitative research method of bitterness based on facial expressions ( Wang, 2022a ).

Wang (2022a) utilized taste stimulation to perceive potential signals from nerve-related facial muscles and gland-related muscles, converting them into digital signals to acquire taste information, thus enabling the acquisition of taste information from electric potential signals. Furthermore, variations in the intensity of expression responses may occur when evaluators taste natural medicines with differing levels of bitterness ( Zhi et al., 2017 ). observed that facial expression intensity can indicate the degree of taste stimulation across various concentrations and levels. Most participants displayed pronounced aversion to medium and high concentrations of bitterness, manifesting as expressions of disgust. With the rapid advancement of deep learning, facial expression recognition technology has progressed from simple classification to intensity level analysis. Yang et al., 2010 introduced a novel technique for facial expression analysis based on a ranking model. They transformed the task of expression intensity analysis into a ranking problem and employed RankBoost modeling. The resulting ranking score can directly estimate intensity and demonstrated good performance on the Cohn-Kanade dataset. As facial expression recognition technology continues to evolve, researchers have established datasets such as JAFFE, FER 2013, and CK + for facial expression analysis. However, an exclusive dataset for bitterness evaluation is yet to be established. Developing such a dataset is crucial to advancing the intelligent and accurate quantification of bitterness.

2.5 Animal behavior test

When one animal is attracted to a stimulus while another avoids it, it suggests that the compound may possess distinct perceptual characteristics for different tasters, leading to varied evaluations ( Loney et al., 2012 ). The two-bottle preference test (TBP) ( Yoneda et al., 2007 ) is employed to assess the aversive taste of food or beverages, utilizing the preference index (PI) as the evaluation metric ( Loney et al., 2011 ). Rodents are commonly chosen as experimental subjects due to their highly homologous bitter taste receptors to humans, thus exhibiting similar taste perceptions ( Noorjahan et al., 2014 ). Han ( Han et al., 2018 ) established the relationship between quinine concentration and animal PI. Subsequently, the PI of 12 bitter Traditional Chinese herbal (TCH) compounds was determined using TBP, and the bitterness results were standardized into a unified numerical system based on the concentration-PI relationship. This standardization offers a methodological framework for sensory evaluation of natural medicines, as shown in Figure 6 .

Figure 6 . Quantitative research method of bitterness based on animal behavior test. Based on ( Han et al., 2018 ).

Magdalena Münster ( Münster et al., 2017 ) assessed the palatability of the bitter compound praziquantel using the rodent transient contact taste aversion (BATA) model and calculated the IC 50 value, representing the concentration of praziquantel inhibiting 50% of the maximum licking response. The findings revealed a decrease in licking frequency with increasing praziquantel concentration, with an IC 50 value of 0.06 mg/mL (95% CI 0.049-0.082). Comparative analysis indicated that praziquantel elicited a stronger aversive response compared to other bitter compounds such as sildenafil citrate, caffeine citrate, diclofenac, or paracetamol ( Soto, 2016 ).

It is important to note that the outcomes of animal studies are influenced by species-specific expression of bitter taste receptors, resulting in bitter taste responses that may differ from those in humans ( Dong et al., 2009 ). Future research endeavors should focus on refining methodologies to achieve more accurate quantitative assessments of bitter taste.

2.6 Cell-based assessment methods

Bitter substances, serving as flavoring agents, can stimulate certain taste bud cells. By describing the interaction strength between them, it is possible to achieve an objective measurement for the quantification of bitterness ( Narukawa et al., 2011 ), as shown in Figure 7 . ( Hui et al., 2012 ) utilized human intestinal endocrine STC-1 cells expressing G protein-coupled receptors and bitter receptors (type 2 members) as sensing devices to conduct specific detection of bitter substances. The findings demonstrated that the sensor utilizing STC-1 cells selectively responded to bitter agents and mixtures, with the type and concentration of bitter agents determinable via signal-to-noise ratio parameters. This approach offers a valuable avenue for investigating taste mechanisms and evaluating bitterness intensity. Nakamura ( Nakamura et al., 2003 ) investigated the effect of quinine on [[Ca 2 + ]i levels in cultured nerve-2a cells, exploring the potential of [[Ca 2 + ]i levels to predict the bitterness of quinine solutions. Following quinine stimulation, [Ca 2 +]i levels in nerve-2a cells increased in a concentration-dependent manner.

Figure 7 . Quantitative research method of bitterness based on animal behavior test. Based on ( Narukawa et al., 2011 ).

However, the cell-based biosensor evaluation method also has certain limitations, as cells may not be able to detect all bitter substances. For instance, Thomas ( Delompré et al., 2022 ) demonstrated the bitterness of vitamins B2 and B3 in sensory analysis, where cell-based assays failed to yield any information. This may be attributed to the inherent fluorescence characteristics of the two vitamins at high concentrations ( Chen and Chung, 2022 ). Additionally, current taste cell culture methods are susceptible to the influence of pseudo-taste cells, potentially leading to overinterpretation. Therefore, caution must be exercised when employing this method.

In summary, researchers have conducted numerous quantitative studies on bitterness using both in vivo and in vitro methods. Throughout this process, researchers have identified various dimensions of bitterness quantification, including local and overall characteristics, static and dynamic features, and external macro performance and internal micro mechanisms. Each method possesses its own advantages and disadvantages ( Table 3 ). When evaluating drug development, taste masking, and palatability, researchers can select appropriate methods based on research objectives, cost, time constraints, and other factors. However, it is important to note that bitterness research methods are still evolving. In the future, researchers need to continue exploring quantitative evaluation methods for bitterness, standardizing the evaluation process to facilitate the high-quality development of bitterness quantification.

Table 3 . Analysis of the advantages and disadvantages of various bitterness quantitative methods.

3 Future research directions

Due to the influence of ethical reviews, the complexity of the regularity of bitter substance structural characteristics, the significant differences in the activation capacity of bitter taste receptors, and the surrounding environment of bitter substances, the quantitative research methods for bitterness are still actively being explored. The current research method system for bitterness, which is primarily based on THTPM and supplemented by other research methods, still requires further improvement to meet basic research needs. The main aspects which is shown in Figure 8 include: (1) Optimizing the quantification and evaluation methods for bitterness. By strictly selecting the evaluation population, establishing standardized operating procedures, and developing methods for handling outliers, the subjectivity of direct bitterness evaluation methods is reduced ( Medicine, 2024 ); by strengthening the basic research on the structural characteristics of bitter substances, the characteristics of activating ligands, and the mechanisms of bitterness presentation, the relationship between concentration-structure-function-bitterness is explored, as well as the relationship between key chromatographic information/electrical signals/fluorescent signals and bitterness. This provides foundational support for optimizing indirect bitterness evaluation methods and actively utilizes machine learning algorithms to enhance the objectivity, accuracy, speed, and transparency of indirect bitterness evaluation methods. (2) Conduct refined quantification of bitterness and explore new methods for bitterness quantification research. Since there is a subtle relationship between people’s preferences or aversions to bitterness ( Mura et al., 2018 ), research methods from the food field can be referenced to make refined distinctions in bitterness, such as good bitterness and bad bitterness, and to carry out refined quantitative evaluation of different types of bitterness in drugs ( Sook Chung and Lee, 2012 ; Araujo et al., 2021 ). At the same time, closely focus on the taste-affecting factors that influence the bitterness of drugs and construct new methods for bitterness quantification and evaluation. For example, based on methods such as virtual screening, biofishing, and physicochemical detection, establish the relationship between key parameters of the above methods and bitterness, and systematically analyze the comprehensive impact of structural characteristics, concentration, and external environmental factors of bitter substances on bitterness. (3) Construct a quantitative research platform for bitterness. Currently, researchers often reveal the mechanisms of bitterness from a mesoscopic or microscopic perspective, and the bitterness platforms constructed are mostly centered around qualitative identification (determining whether it is bitter or not) ( Chu et al., 2024 ). On this basis, there is an urgent need to build a quantitative research platform and equipment for bitterness, and to integrate different types of data in multiple dimensions, to promote the transformation of basic research on bitterness quantification to applied research. (4) Improve and establish a series of standards for quantitative research on bitterness. In order to achieve scientific measurement and effective evaluation of senses, a series of international documents have been issued for sensory analysis. For the sensory evaluation of bitterness, some scholars have already conducted research on the technical specifications for sensory evaluation based on the characteristics of natural medicines ( Medicine, 2024 ). In the future, it is still necessary to formulate industry, national, and global standards around the research design and plan framework guidelines, statistical analysis plans, methodological validation, data processing, etc., of bitterness quantification, to promote the standardization, scientification, and systematization of quantitative research on drug bitterness. (5) Research Extension. Bitter substances possess a variety of physiological activities ( Zuluaga, 2024 ). In traditional Chinese medicine theory, bitterness is believed to have effects such as " downbearing and discharging, drying dampness, and consolidating Yin " Advancing the research on the functional attributes of bitterness and its extension into the field of bioinformatics, including the relationships between bitterness and efficacy, and bitterness and receptors, can provide support for accelerating the development of target drugs.

Figure 8 . A framework addressing the challenges and future development directions in bitterness quantification.

In the process of exploring the quantification of bitterness, we also face many challenges. On one hand, there are issues such as non-standardized operating procedures and inconsistent technical parameters. These mainly include the lack of uniformity in scales, reference solutions, the volume of samples evaluated at one time, temperature, evaluation time and intervals, and limited instrument stability, which hampers the comparability of results between different studies ( Zuluaga, 2024 ). On the other hand, the complexity of bitter taste presentation makes the quantification of bitterness very difficult, especially for the measurement of the comprehensive bitterness in complex systems, where it is urgent to explore the taste rules in the independent state of substances and under the state of complex systems ( Zhang et al., 2021 ; Gao et al., 2023 ). In addition, the flexibility of virtual screening methods, the specificity and sensitivity of indirect measurement methods, and the computational power of different machine algorithms also affect the accuracy of bitterness quantification. In the future, researchers urgently need to further enrich the database of bitter substances, establish standardized and unified operating standards, and by improving detection technology and optimizing algorithm capabilities, jointly explore and mutually verify the characteristics of bitterness from macroscopic, mesoscopic, or microscopic perspectives ( Li et al., 2024 ).

4 Conclusion and foresight

In nature, various taste substances exist alongside intricate taste mechanisms, and numerous factors influence the quantification of drug bitterness to varying extents. In light of this, different quantitative evaluation methods for bitterness have been established, each possessing its own merits. Presently. Currently, an increasing number of researchers are leveraging column chromatography, HPLC, HPLC/ESI-MS, LC/ESI-MS/MS, UPLC-Q-TOF/MS, and nontargeted LC/MS flavoromics analysis to separate and identify the bitter compounds ( Suryawanshi et al., 2006 ; Mustafa et al., 2015 ; Höhme et al., 2023 ). They also combine methods such as sensory-guided, virtual screening, and chromatography-taste association to improve the efficiency of discovering bitter components ( Yu et al., 2020 ; Yang et al., 2023 ). This signifies that the study of bitterness in natural medicines is steadily advancing. However, the identification of bitter components represents merely the initial phase. A precise, dependable, and straightforward method for evaluating drug bitterness is required to investigate bitterness masking strategies for medications. Similar to the measurement of length using the international unit “meter” and temperature using the “degree Celsius,” bitterness should also be subject to standardized, objective quantitative methods and parameters. This review scrutinizes research on bitterness quantification, delineates factors influencing drug bitterness, and acknowledges the role of material, human, and environmental factors in affecting bitterness perception. Consequently, in the quantitative exploration of drug bitterness, it is imperative to identify and regulate these factors to ensure the reliability of outcomes. Furthermore, this paper consolidates the characteristics of various bitterness quantification methods, systematically categorizes the quantitative approaches for representative drugs, and emphasizes the challenges associated with quantifying bitterness in natural drugs characterized by complex systems. It also elucidates the future research directions that urgently need to be undertaken. This is of significant guiding importance for our continued in-depth focus on the research of quantitative bitterness methods and lays an important foundation for the development of precise, efficient, and rich taste-masking strategies. Such efforts aim to foster research into taste masking optimization and palatability enhancement, thereby laying a crucial groundwork for enhancing the clinical acceptance of natural medications.

Author contributions

PW: Writing–original draft. HL: Writing–original draft. YW: Investigation, Writing–review and editing. FD: Investigation, Writing–review and editing. HL: Writing–review and editing. XG: Investigation, Writing–review and editing. YR: Writing–original draft. XG: Investigation, Writing–review and editing. XL: Funding acquisition, Project administration, Writing–review and editing. RL: Funding acquisition, Project administration, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. We sincerely thank the National Natural Science Foundation of China (Nos 81001646 and 81774452), Henan Provincial Health Commission National Clinical Research Base of Traditional Chinese Medicine Research Project (2022JDZX110), Henan Province Traditional Chinese Medicine Scientific Research Project (2024ZY3019), Key research projects of universities in Henan Province (24B360005), The National Administration of Traditional Chinese Medicine 2022 Young Qihuang Scholars Training Project (No. [2022] 256), The High-level Talents in Henan Province Special Support “Central Plains Thousand Talents Plan” – “Central Plains Young Top Talents” project (No. ZYQR201912158), Excellent Youth Foundation of Henan Scientific Committee (No. 242300421023).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fchem.2024.1449536/full#supplementary-material

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Keywords: bitterness, quantitative method, traditional human taste panel method, active pharmaceuticals ingredients, traditional Chinese medicine

Citation: Wang P, Li H, Wang Y, Dong F, Li H, Gui X, Ren Y, Gao X, Li X and Liu R (2024) One of the major challenges of masking the bitter taste in medications: an overview of quantitative methods for bitterness. Front. Chem. 12:1449536. doi: 10.3389/fchem.2024.1449536

Received: 15 June 2024; Accepted: 29 July 2024; Published: 14 August 2024.

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Copyright © 2024 Wang, Li, Wang, Dong, Li, Gui, Ren, Gao, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ruixin Liu, [email protected] ; Xuelin Li, [email protected]

† These authors have contributed equally to this work and share first authorship

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