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Evaluation indicators of Ruditapes philippinarum nutritional quality.


ABSTRACT: To access the nutritional quality of the Ruditapes philippinarum, a comprehensive quality evaluation procedure is always important to be established. In this study, fifteen nutritional quality evaluation indicators of R. philippinarum from 7 months were analyzed, and the most important indicators were determined using a combination of multiple chemometric methods such as correlation analysis (CA), principal component analysis (PCA), and system cluster analysis (SCA). Significant differences in nutritional quality were observed across the 7 months, as per the ANOVA results (P < 0.05). The coefficient of variation values for the fifteen evaluation indicators for R. philippinarum across 7 months was 1.67-43.47%. The CA results revealed that some indicators were correlated to each other within a certain range. Four principal components with eigen-values > 1 were obtained with PCA, and a cumulative contribution of 92.11% was achieved. In addition, four essential quality indicators were extracted using SCA. Using these four indicators, a simple and efficient procedure can be applied for quality control in aquaculture.

SUBMITTER: Chen L 

PROVIDER: S-EPMC8249540 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Evaluation indicators of <i>Ruditapes philippinarum</i> nutritional quality.

Chen Lipin L   Yu Fanqianhui F   Sun Shuhong S   Liu Xiangyu X   Sun Zhongkai Z   Cao Wanxiu W   Liu Shengnan S   Li Zhaojie Z   Xue Changhu C  

Journal of food science and technology 20200930 8


To access the nutritional quality of the <i>Ruditapes philippinarum</i>, a comprehensive quality evaluation procedure is always important to be established. In this study, fifteen nutritional quality evaluation indicators of <i>R. philippinarum</i> from 7 months were analyzed, and the most important indicators were determined using a combination of multiple chemometric methods such as correlation analysis (CA), principal component analysis (PCA), and system cluster analysis (SCA). Significant di  ...[more]

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