Effects of different floral periods and environmental factors on royal jelly identification by stable isotopes and machine learning analyses during non-migratory beekeeping.

It is crucial to monitor the authenticity of royal jelly (RJ) because the qualities of RJs produced by different floral periods vary substantially. In the context of non-migratory beekeeping, this study aims to identify rape RJ (RRJ), chaste RJ (CRJ), and sesame RJ (SRJ) based on δC, δN, δH, and δO combined with machine learning and to evaluate environmental effect factors. The results showed that δC (-27.62‰ ± 0.24‰), δN (2.88‰ ± 0.85‰), and δO (28.02‰ ± 1.30‰) of RRJ were significantly different from other RJs. The δC, δH, and δO in CRJ and SRJ were strongly correlated with temperature and precipitation, suggesting that these isotopes are influenced by environmental elements such as sunlight and rainfall. In addition, the artificial neural network (ANN) model was superior to the random forest (RF) model in terms of accuracy, sensitivity, and specificity. This study revealed that combining stable isotopes with ANN models and the unique correlation between stable isotopes and environmental factors could provide promising ideas for monitoring the authenticity of RJ.