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

Abstract

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 δ¹³C, δ¹⁵N, δ²H, and δ¹⁸O combined with machine learning and to evaluate environmental effect factors. The results showed that δ¹³C (-27.62‰ ± 0.24‰), δ¹⁵N (2.88‰ ± 0.85‰), and δ¹⁸O (28.02‰ ± 1.30‰) of RRJ were significantly different from other RJs. The δ¹³C, δ²H, and δ¹⁸O 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.

Keywords: Environmental factors; Floral periods; Machine learning; Royal jelly; Stable isotopes.