Principal Component Analysis and Multiple Linear Regression Analysis of CIELAB Parameters and Anthocyanins in Red Wine

doi:10.7506/spkx1002-6630-20190408-080

Abstract

Abstract: A total of 119 red wine samples were analyzed for color parameters by using CIELAB color space, the contents of 16 different anthocyanin monomers by ultra-high performance liquid chromatography tandem mass spectrometry, and total anthocyanins content by the pH differential method. Principal component analysis, correlation analysis and multiple linear regression analysis were conducted to study the relationship between CIELAB color parameters (L*, a* and b* values) and the contents of total anthocyanins and anthocyanin monomers and pH in red wine. Three principal component factors reflecting the color in red wine samples were selected, which cumulatively accounted for 84.11% of the total variability. The color parameters were affected by different anthocyanin monomers. Among the 16 anthocyanin monomers, cyanidin-3-glucoside content had the?greatest?impact?on?L* and a* values, while malvidin content had the?greatest?impact on b* value. The content of total anthocyanins had a very significant effect on all color parameters. A significant negative correlation occurred between L* and?a* values.

Key words: CIELAB color space, anthocyanin, correlation analysis, principal component analysis, multiple linear regression analysis

CLC Number:

TS207.3

GUO Yaodong, WANG Fei, DONG Shaojie, WANG Shengyi, ZHANG Ang. Principal Component Analysis and Multiple Linear Regression Analysis of CIELAB Parameters and Anthocyanins in Red Wine[J]. FOOD SCIENCE, 2019, 40(18): 210-215.

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Principal Component Analysis and Multiple Linear Regression Analysis of CIELAB Parameters and Anthocyanins in Red Wine

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