Authentication of chaste honey adulterated with high fructose corn syrup by HS-SPME-GC-MS coupled with chemometrics

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 com-pounds 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.

Automated summary

This study optimized the conditions of headspace solid-phase micro-extraction (HS-SPME) to develop a method for detecting the adulteration of chaste honey with high fructose corn syrup (HFCS). Gas chromatography-mass spectrometry (GC-MS) was used to analyze the volatile compounds in HFCS-adulterated chaste honey. Linear discriminant analysis (LDA), principal component analysis (PCA), and artificial neural network (ANN) were used to analyze the data. A back propagation (BP) artificial neural network (ANN) model was constructed based on m/z RT pair data, which showed good accuracy in predicting the chaste honey content in HFCS-adulterated samples without relying on the identification of volatile compounds.