Classification of the botanical and geographical origins of Chinese honey based on 1H NMR profile with chemometrics. (November 2020) - Zhang, Jialin; Chen, Hui; Fan, Chunlin; Gao, Shuai; Zhang, Zijuan; Bo, Lin
Graphical abstract: Highlights: The honey components in different floral honeys were compared by their NMR spectra. 8 classes monofloral honeys are distinguished by this new developed NMR-based method. The geographical origins of two honeys are classified at provincial and town levels. Abstract: In this paper, we report a newly developed non-target 1 H NMR detection associated with chemometrics method to classify the botanical and geographical origins of the monofloral Chinese honey. 1 H NMR tests of 218 monofloral honey samples of 8 classes (Acacia, Jujube, Linden, Longan, Orange, Rape, Sunflower, Vitex) collected in 2017–2019 across China were conducted under the optimal sample preparation conditions and NMR acquisition parameters. The whole profiles of NMR spectra instead of individual or partial signals from specific components were processed and extracted, then fed to SIMCA-P to classify the botanical and geographical origins through non-target statistical analysis. For the botanical origins, most of them could be classified clearly according to Principal Component Analysis (PCA) with both R 2 and Q 2 close to 1. Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) model could classify the honey floral types successfully with R 2 Y and Q 2 greater than 0.85. It is found that the integral bin for data extraction has no obvious influence on the classification. For the geographical origins, the classification at different geographical levels (providence and town) could be successfully distinguished by OPLS-DA model. The promising preliminary results with the geographical classification at 40 km level unambiguously demonstrate the application of this NMR-based multi-species non-targeted method for the honey authenticity. Successful result is obtained on a pilot prediction of the geographical classification. Comparing with the methods based on other techniques, the advantages of this reported one are less sample amount needed, simple preparation, short test time, and non-targeted multi-species detection.