2023
DOI: 10.1016/j.crfs.2022.11.022
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Modeling and application of sensory evaluation of blueberry wine based on principal component analysis

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Cited by 10 publications
(8 citation statements)
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“…A PCA was used to describe the differences between samples and to provide more information on the response variables that mainly influenced sample similarities and differences, in order to reduce the complexity of the data and address the most important features [ 55 ]. In this study, the application of the PCA on the dataset was used to analyze the antioxidant potential (DPPH and ABTS + ) of the evaluated EOs from different plant samples, as well as their yields and chemical profiles.…”
Section: Resultsmentioning
confidence: 99%
“…A PCA was used to describe the differences between samples and to provide more information on the response variables that mainly influenced sample similarities and differences, in order to reduce the complexity of the data and address the most important features [ 55 ]. In this study, the application of the PCA on the dataset was used to analyze the antioxidant potential (DPPH and ABTS + ) of the evaluated EOs from different plant samples, as well as their yields and chemical profiles.…”
Section: Resultsmentioning
confidence: 99%
“…Therefore, PCA was utilized to extract key physicochemical indicators, visualizing the differentiation between samples. PCA is a valuable tool to exploit more information on the variables mainly influencing the character of the sample by reducing the dimension of the original data [ 17 ]. In this study, two principal components (PCs) were selected based on the eigenvalue and total contribution rate of PCs [ 17 ], which had an eigenvalue greater than 1 and a total variance of 86.71% ( Table 3 ).…”
Section: Resultsmentioning
confidence: 99%
“…Food quality is a complex concept because the components in it may be related to each other and contribute a different weight to the product quality characteristics [ 17 ]. However, at present, most studies evaluate the quality of Z. latifolia only by a simple comparison of the attributes (e.g., texture, color, components).…”
Section: Introductionmentioning
confidence: 99%
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“…And it is time-consuming and costly to determine each indicator. In order to carry out e cient and accurate evaluation, it is necessary to apply statistical methods, such as variability analysis, correlation analysis, hypothesis testing, multiple regression, principal component analysis (PCA) and clustering (Kaivan et al, 2012), to screen the important attributes, which are representative, differentiated and independent of each other, and further establish a mathematical model (Zhao et al, 2023). At present, evaluation models for fruit quality has been reported in longan (Han et al, 2015), orange (Tang et al, 2018), walnut (Shi et al, 2022), kiwifruit (Chen et al, 2021) and other plants.…”
mentioning
confidence: 99%