FOOD SCIENCE ›› 2019, Vol. 40 ›› Issue (10): 243-250.doi: 10.7506/spkx1002-6630-20180523-336

• Composition Analysis • Previous Articles     Next Articles

Principal Component Analysis and Cluster Analysis for Evaluating Free Amino Acids of Different Cultivars of Daylily Buds

LIU Wei1,2, ZHANG Qun1, LI Zhijian1, BIAN Jianming1, HUANG Lühong1, ZHU Xiangrong1, SHAN Yang1,*   

  1. 1. Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China; 2. Longping Branch Graduate School, Central South University, Changsha 410125, China
  • Online:2019-05-25 Published:2019-05-31

Abstract: This study aimed to analyze the differences in the composition, content and taste of free amino acids in daylily buds from 10 main cultivars in Hunan province. We determined the free amino acid profiles with an automatic amino acid analyzer and a systematic evaluation was performed by measurement of taste activity value (TAV), correlation analysis, principal component analysis (PCA) and cluster analysis. The results showed that daylily buds contained abundant free amino acids. A total of 14–17 free amino acids were detected at a total content of 13.935 mg/g. There were significant differences in the contents of free amino acids (FAA), total essential amino acids (EAA), taste-active amino acids (DAA) and limiting amino acids (LAA) in the 10 cultivars of daylily buds, among which the highest TFAA, EAA, DAA and LAA were observed in the cultivar CT. Glu had the greatest influence on the flavor of daylily buds, and its TAV value was between 3.53–7.51 with an average of 5.66. Three principal components were extracted by PCA, which contributed cumulatively to 89.242% of the total variance. The comprehensive scores of CT, TT and BY were ranked as the top three. Cluster analysis was used to divide these daylily cultivars into 4 groups, and the results were consistent with the PCA, which reflected the distinction among different germplasms of daylily in Hunan.

Key words: daylily, free amino acid, taste activity value, principal component analysis, cluster analysis

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