Honey adulteration is becoming increasingly alarming incidents in food safety. Monitoring and detecting adulteration face greater challenges. Honey contains the major royal jelly proteins (MRJP) secreted by bee workers. To detect honey adulteration fast and accurately, a rapid gold sandwich immunochromatographic strip (GSIS) was developed based on two specific polyclonal antibodies (PoAbs) against the MRJP1, the most abundant protein of all MRJPs. We determined the best of pH value (pH 8.6) and PoAb SP-1 amount (5 μg/mL) in conjunction with colloidal. The cut-off value (sensitivity) of GSIS in detecting MRJP1 is 2.0 μg/mL in solution. Validation analysis with RJ, milk vetch honey, acacia honey and honey adulteration containing rice syrup and corn syrup with different ratios demonstrated that the GSIS could show consistent Test line (T line) when the test samples contain more than 30% pure honey or MRJP1 0.4 mg/g. The validation results by isotope ratio mass spectrometry on the same pure and all adulteration milk vetch honey samples showed the same information of GSIS test. The qualitative assay GSIS provided a valuable new way for honey authenticity and laid the foundation for the future application of GSIS with monoclonal antibodies in honey authentication.
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.
危险品化学品经营许可证（带存储）