C/H/O stable isotope of rape honey and its components combined with machine learning algorithms to identify geographical origin.

RATIONALE: The distribution of rape honey is among the largest and most diverse of all honeys available to humankind with respect to the geographical origin. Accurate isotopic reference values for rape honey are therefore important for precise verification of honey origin and its traceability. New combined rape honey δ13 C, δ2 H, and δ18 O values in combination with values on its compounds (protein and saccharides) were used to complement existing databases to better identify the geographical origin of Chinese rape honey. METHODS: Traceability methods based on elemental analyzer isotope ratio mass spectrometry and liquid chromatography isotope ratio mass spectrometry were established for geographical origin of rape honey. RESULTS: Rape honey harvested in the high-altitude region (QH; Qinghai) had significantly higher values (1.4 to 5.3‰ for δ13 C, 7.9 to 12.9‰ for δ2 Hprotein ) for the δ13 C of whole honey (-23.8‰), its protein (-24.4‰), fructose (-23.5‰), glucose (-23.6‰), and disaccharide (-24.7‰), and also δ2 H of the protein (103.5‰) than those in low-altitude regions (HB; Hubei, SC; Sicuan, and JS; Jingsu). The δ18 Orape honey was a useful index to differentiate whether rape honey from coastal (JS) or non-coastal (HB, SC, and QH) regions. The δ13 C, δ2 H, and δ18 O values in rape honey are affected by geographical factors, such as temperature and altitude. The δ13 Cprotein and δ13 Crape honey values were better to identify the geographical origin of rape honey than δ13 Csaccharides . The δ18 O and δ2 H values of rape honey protein were more suitable for traceability than those of rape honey. The combination of the δ13 C, δ2 H, and δ18 O values of rape honey and its extracted protein and saccharides improved the precision of three models (linear discriminant analysis, SVM, and random forest) used to discriminate rape honey from different regions in China. The SVM model obtained the best accuracy (93.2%). CONCLUSIONS: Stable isotopes could be significant predictors in determining the geographical origin of rape honey.

Study on stable carbon isotope fractionation of rape honey from rape flowers (Brassica napus L.) to its unifloral ripe honey.

A new idea and strategy for honey traceability and identification was provided by studying the carbon isotope fractionation of rape honey and its components in the different ripening process, as well as the fractionation from rape flowers, stamens, nectar to rape honey. The results showed the moisture content of rape honey continued to decrease, and the glucose and fructose content continued to increase during the ripening process. The δ13C of rape honey and its protein were less affected by honey ripeness, while the δ13C of sugars in rape honey were greatly affected by this. At the same time, the fractionation of carbon isotope from rape flowers to honey was significant. The δ13C of rape honey and its protein, disaccharide, fructose, and glucose had a strong correlation, and the δ13C of rape honey and its components were mainly related to rape flowers and its stamens.

Discrimination of geographical origins of Chinese acacia honey using complex 13C/12C, oligosaccharides and polyphenols.

The aim of this study was to predict the geographical origin of acacia honey of China through analysis of physicochemical parameters combination with chemometrics. Samples from six different origins were investigated on parameters of δ13C value, oligosaccharides and polyphenols, using EA-IRMS/LC-IRMS, GC-MS and HPLC-MS, respectively. The results indicated that the δ13C value of honey from Gansu region were lower than those of other regions. Oligosaccharides of honey from Shanxi and Shaanxi regions were both higher than other four regions. Polyphenols of honey from Shandong region was the highest and were better parameters than both δ13C and oligosaccharides in discrimination of geographical origins. Partial Least Square Discriminant Analysis (PLS-DA) showed that when all 31 different parameters were combined, a correct classification rate of 94.12% could be achieved using external cross validation method. In conclusion, the method in discrimination of geographical can be used to provide reliable and useful reference information.