FOOD SCIENCE ›› 2022, Vol. 43 ›› Issue (16): 242-252.doi: 10.7506/spkx1002-6630-20210807-097

• Component Analysis • Previous Articles     Next Articles

Analysis, Classification and Prediction of Volatile Flavor Components in Milk Powders for Different Age Groups

YE Meixia, LI Rong, JIANG Zitao, WANG Ying, TAN Jin, TANG Shuhua   

  1. (1. College of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, China; 2. School of Food Engineering, Tianjin Tianshi College, Tianjin 301700, China)
  • Online:2022-08-25 Published:2022-08-31

Abstract: An electronic nose based on ultra-fast gas chromatography (UFGC E-nose) was used to analyze the volatile flavor components of 24 samples of whole milk powder for infants, students, adults and middle-aged and elderly people. The aroma characteristics of milk powders for different age groups were classified and predicted by linear discriminant analysis (LDA) and linear support vector machine (LSVM). The results showed that a total of 54 volatile flavor components were detected in all samples, among which 37 components were common to these samples including propionaldehyde, acetone, hexanal, and caproic acid. Methyl propionate and 1-octen-3-one were exclusively found in infant milk powder. (E,E)-2,4-Decadienal and γ-unsecalactone were unique to students’ milk powder. Heptanal was detected only in adult milk powder. 2-Butanone and benzaldehyde were only found in middle-aged and elderly milk powder. In total, 23 volatile components contributing to the aroma were identified according to their relative odor activity values and aroma radar map, and 10 of them were the key aroma compounds in milk powders for the four age groups, all of which had milky, vegetable-like, fruity, flowery, grassy, caramel-like and tar-like aromas. In addition, infant milk powder had a characteristic apricot kernel-like aroma. The sensory evaluation results showed that the flavor of students’ milk powder and adult milk powder was better than that of infant milk powder and middle-aged and elderly milk powder, which was significantly different from the aroma radar map results. The classification and prediction accuracy of LDA and LSVM for milk powder for different age groups were 93.3% and 94.2%, respectively.

Key words: milk powder; electronic nose based on ultra-fast gas chromatography; volatile flavor components; linear discriminant analysis; linear support vector machine

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