Lanzhen Chen

Royal jelly (RJ) is highly regarded for its bioactive compounds and salutary effects. However, the traceability and authenticity of royal jelly are significantly challenged due to the considerable variability in its composition, which is influencedby the diverse food sources of bees. This study examines the impact of three food sources—natural foods, sugar‐water, and pollen substitutes—on stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotope fractionation in RJ produced during different floral periods. The findings indicate that RJ derived from natural honey and beebread exhibited lower δ¹³C values, whereas RJ produced from sugar‐water feeding showed higher δ¹³C values. Furthermore, a notable degree of variation in δ¹³C was observed regarding the diverse beebread sources. A positive correlation was identified between δ¹⁵N in beebreads and RJ, whereas a negative correlation (r = −0.89) was observed between δ¹⁵N in pollen substitutes and RJ. The application of machine learning (ML) models, including artificial neural networks (ANNs) and random forests (RFs), resulted in 100% classification accuracy in the identification of RJ on the basis of feeding sources and floral periods, utilizing calculated fractionation factors. These findings demonstrate that δ¹³C and δ¹⁵N are reliable markers for RJ authenticity and highlight the importance of integrating isotopic data with feeding conditions for precise identification. The success of ANN and RF models underscores the potential of combining isotope fractionation with ML for high‐precision traceability, offering a framework for food traceability and sustainability in apiculture.

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 δ ¹³ C, δ ² H, and δ ¹⁸ 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 δ ¹³ C, 7.9 to 12.9‰ for δ ² H _protein ) for the δ ¹³ C of whole honey (−23.8‰), its protein (−24.4‰), fructose (−23.5‰), glucose (−23.6‰), and disaccharide (−24.7‰), and also δ ² H of the protein (103.5‰) than those in low‐altitude regions (HB; Hubei, SC; Sicuan, and JS; Jingsu). The δ ¹⁸ O _{rape honey} was a useful index to differentiate whether rape honey from coastal (JS) or non‐coastal (HB, SC, and QH) regions. The δ ¹³ C, δ ² H, and δ ¹⁸ O values in rape honey are affected by geographical factors, such as temperature and altitude. The δ ¹³ C _protein and δ ¹³ C _{rape honey} values were better to identify the geographical origin of rape honey than δ ¹³ C _saccharides . The δ ¹⁸ O and δ ² H values of rape honey protein were more suitable for traceability than those of rape honey. The combination of the δ ¹³ C, δ ² H, and δ ¹⁸ 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.