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Discrimination of botanical origins for Chinese honey according to free amino acids content by high‐performance liquid chromatography with fluorescence detection with chemometric approaches
Author(s) -
Chen Hui,
Jin Linghe,
Chang Qiaoying,
Peng Tao,
Hu Xueyan,
Fan Chunlin,
Pang Guofang,
Lu Meiling,
Wang Wenwen
Publication year - 2017
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.8008
Subject(s) - amino acid , high performance liquid chromatography , phenylalanine , principal component analysis , chromatography , chemistry , derivatization , food science , biology , biochemistry , mathematics , statistics
BACKGROUND The contents of 18 free amino acids in 87 Chinese honey samples from four botanical origins (linden, acacia, vitex and rape) were determined by developing a high‐performance liquid chromatography with fluorescence detector ( HPLC‐FLD ) method with an in‐loop automated pre‐column derivatization. The free amino acid profiles of these samples were used to construct a statistical model to distinguish honeys from various floral origins. RESULTS The average contents of all free amino acids in linden honey were lower than in the other three types of honey. Phenylalanine was particularly useful in the present study because its average content in vitex honey was far higher than in any other honey samples. There is no doubt that both phenylalanine and tyrosine can be considered as the marker free amino acid in Chinese vitex honey. Principal component analysis ( PCA ) was conducted based on 15 free amino acids and showed significant differences among the honey samples. The cumulative variance for the first two components was 80.62%, and the four principal components can explain 94.18% of the total variance. In the two first component scores, the honey samples can be separated according to their botanical origins. Cluster analysis of amino acid data also revealed that the botanical origins of honey samples correlated with their amino acid content. Back‐propagation artificial neural network ( BP‐ANN ) and naïve Bayes methods were employed to construct the classification models. The results revealed an excellent separation among honey samples according to their botanical origin with 100% accuracy in model training for both BP‐ANN and naïve Bayes. CONCLUSION It indicated that the free amino acid profile determined by HPLC‐FLD can provide sufficient information to discriminate honey samples according to their botanical origins. © 2016 Society of Chemical Industry