Principal component analysis of d-prime values from sensory discrimination tests using binary paired comparisons

Linander, Christine Borgen; Christensen, Rune Haubo Bojesen; Cleaver, Graham; Brockhoff, Per B.

doi:10.1016/j.foodqual.2019.103864

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…PCA is a multivariate pattern recognition method that can be applied to characterize a sensory profile and compare products [176]. Recent studies have considered other applications such as the acquisition of information about d-prime values across sensory attributes [103], the analysis of the impact of treatments on a product's shelf life, the detection of correlations between studied responses [177], and the contribution of product positioning with correct approaches or strategies [30]. More comprehensive statistical techniques have emerged in recent litera-ture, including LASSO-PCA (least absolute shrinkage and selection operator, -principal component analysis) comprehensive evaluation for matcha sensory quality [178].…”

Section: Sensory Data Treatmentmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Sensory Characterization Techniques: From Classical Descriptive Analysis to the Emergence of Novel Profiling Methods

Marques

Correia

Dinis

et al. 2022

Foods

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Sensory science provides objective information about the consumer understanding of a product, the acceptance or rejection of stimuli, and the description of the emotions evoked. It is possible to answer how consumers perceive a product through discriminative and descriptive techniques. However, perception can change over time, and these fluctuations can be measured with time-intensity methods. Instrumental sensory devices and immersive techniques are gaining headway as sensory profiling techniques. The authors of this paper critically review sensory techniques from classical descriptive analysis to the emergence of novel profiling methods. Though research has been done in the creation of new sensory methods and comparison of those methods, little attention has been given to the timeline approach and its advantages and challenges. This study aimed to gather, explain, simplify, and discuss the evolution of sensory techniques.

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Section: Sensory Data Treatmentmentioning

confidence: 99%

“…A nine-point hedonic scale is applied to assess acceptability. These kinds of ordinal data are usually analyzed by interval-scale data and paired t-test or ANOVA [103].…”

Section: Sensory Data Treatmentmentioning

confidence: 99%

An Overview of Sensory Characterization Techniques: From Classical Descriptive Analysis to the Emergence of Novel Profiling Methods

Marques

Correia

Dinis

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…Quantitative description analysis (QDA) is an effective method for food sensory evaluation, which can combine the qualitative and quantitative evaluation of food quality through multivariate analysis (Tan et al, 2020). Principal component analysis (PCA) is a full‐fledged quantitative description analysis method and a commonly used dimensionality reduction method in data processing (Granato, Santos, Escher, Ferreira, & Maggio, 2018; Linander, Bojesen Christensen, Cleaver, & Brockhoff, 2020). Besides, a few comprehensive indicators or factors can be used to replace many indicators or factors with strong correlation through this analysis method.…”

Section: Introductionmentioning

confidence: 99%

“…Principal component analysis (PCA) is a full-fledged quantitative description analysis method and a commonly used dimensionality reduction method in data processing (Granato, Santos, Escher, Ferreira, & Maggio, 2018;Linander, Bojesen Christensen, Cleaver, & Brockhoff, 2020). Besides, a few comprehensive indicators or factors can be used to replace many indicators or factors with strong correlation through this analysis method.…”

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confidence: 99%

Study on the prediction model of basic components on the quality of buckwheat noodles

Zhang

Fan

et al. 2022

Journal of Texture Studies

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The sensory quality of noodles is the key factor in determining consumers' acceptance, and the physicochemical properties can reflect the quality of noodles. In this study, the rheological and thermodynamic properties, noodle quality indexes, and molecular and structural parameters were characterized by adding different levels of buckwheat flour. Pearson correlation analysis was used to evaluate the correlation between physicochemical indexes and basic components of noodles. A comprehensive evaluation model was established by the combination of principal component analysis (PCA) and regression analysis (RA) to evaluate the sensory quality of noodles. The results showed that there was a significant correlation between the physicochemical indexes and the basic components. The two principal components extracted by PCA could explain 89.4% of the total variance of the data. RA showed that the comprehensive evaluation value of the principal component model had a very significant negative correlation with the total score of sensory evaluation (R2 = 0.94). In conclusion, this work demonstrated that PCA and RA as an objective protocol had great potential in predicting the sensory quality of noodles.

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“…The ability to accurately quantify relative acceptability from the paired preference test paradigm would be a key step forward in sensory testing for those who struggle with scale usage (e.g., children). Additionally, producing reliable numeric values from paired preference data can lead to the ability to implement other statistical tools commonly used, such as principal component analysis or clustering (Linander, Christensen, Cleaver, & Brockhoff, 2019). This study was designed to assess various methods of analyzing paired preference data and serves as a model for researchers looking for alternatives to nine‐point hedonic scales in assessing relative acceptability.…”

Section: Introductionmentioning

confidence: 99%

Estimates of relative acceptability from paired preference tests

Luckett

Burns

Jenkinson

2020

Journal of Sensory Studies

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The relative acceptability of a food or beverage is an important data point in many different scenarios. In humans, a nine-point category scale is typically used when the hedonic characteristics of several products are of interest. However, these scales are not consistently used by participants and therefore reliability is low. This article outlines the effectiveness of four techniques (mElo, Thurstonian modeling, Bradley-Terry, and Friedman) to calculate numerical values for products based upon their performance in a paired preference paradigm. In this study, acceptance data from four separate studies were compared to numeric scores constructed from a paired preference paradigm. In general, numeric ratings constructed from paired comparisons correlated very well with the mean overall liking ratings. The relationship between these acceptance ratings and numerical preference ratings did show to be somewhat dependent on the type of food product. This study serves as a guide to those looking to further quantify paired preference data. Practical Applications: Paired preference tests are simple and easy to understand for those who struggle with test instructions and numerical scales. However, in a multisample paradigm, sensory scientists have overwhelmingly chosen to use rating scales for a variety of reasons. This study shows the effectiveness of alternative data analysis techniques to extract numerical values for relative acceptance based on paired preference tests. This information could be used in the implementation of paired preference tests to replace traditional affective testing in groups that do not perform well with scales (e.g., children) or as an alternate analysis method for paired preference tests.

show abstract

Principal component analysis of d-prime values from sensory discrimination tests using binary paired comparisons

Cited by 6 publications

References 16 publications

An Overview of Sensory Characterization Techniques: From Classical Descriptive Analysis to the Emergence of Novel Profiling Methods

An Overview of Sensory Characterization Techniques: From Classical Descriptive Analysis to the Emergence of Novel Profiling Methods

Study on the prediction model of basic components on the quality of buckwheat noodles

Estimates of relative acceptability from paired preference tests

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