Authentication of chaste honey adulterated with high fructose corn syrup by HS-SPME-GC-MS coupled with chemometrics. (15th February 2023)

• Record Type: Journal Article
• Authors: Wei, Quanzeng; Sun, Juntao; Guo, Jiaqi; Li, Xiaofei; Zhang, Xiaohua; Xiao, Fugang
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 176(2023)
• Publication Date: 2023-02-15
• Subjects: Plackett burman design -- Box-behnken design -- PCA -- LDA -- Artificial neural network; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2023.114509
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: The adulteration of the honey industry is serious, especially the syrup adulteration method is difficult to detect. To develop the detection technology of chaste honey adulteration with high fructose corn syrup (HFCS), the condition of headspace solid-phase micro-extraction (HS-SPME) was optimized for extracting the volatile compounds of chaste honey. HS-SPME conditions were selected for optimization using Plackett Burman, steepest ascent and Box-Behnken. Volatile compounds from chaste honey adulterated with HFCS were analyzed by gas chromatography-mass spectrometry (GC-MS). M/Z RT pairs data from differences in chaste honey contents in HFCS-adulterated samples were analyzed by linear discriminant analysis (LDA), principal component analysis (PCA), and artificial neural network (ANN). The results indicated distinguished adulterated chaste honey at different proportions was not ideal using LDA and PCA. A back propagation (BP) artificial neural network （ANN）model was constructed based on m/z RT pair data. Correlation coefficients of training, verification, testing and comprehensive data of BP-ANN were 0.994, 0.945, 0.968 and 0.979, respectively, indicating good accuracy of the BP-ANN prediction model. The present study discusses a new strategy that determined the chaste honey contents in HFCS-adulterated samples as well as did not rely on the identification of volatile compounds. Graphical abstract: Image 1 Highlights: m/z RT pairs data was used to discriminate the specific adulterated chaste honey. First report of content determination of chaste honey in adulterated samples by ANN. The results of ANN are the best among the three statistical analysis methods.