数字农科院2.0

Machine Learning for Precise Identification of Royal Jelly From Various Food Sources With Stable Isotope and Physicochemical Properties

文献类型： 外文期刊

作者： Liu, Zhaolong;Yu, Xinlei;Yin, Xin;Qiao, Dong;Li, Hongxia;Chen, Lanzhen

作者机构：

关键词： authenticity;correlation analysis;food sources;machine learning;royal jelly;stable isotope fractionation

期刊名称： FOOD FRONTIERS

ISSN：

年卷期： 2025 年

页码：

收录情况： EI（2025版） ; ; ESCI（2025版）

摘要： 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 (delta 13C) and nitrogen (delta 15N) isotope fractionation in RJ produced during different floral periods. The findings indicate that RJ derived from natural honey and beebread exhibited lower delta 13C values, whereas RJ produced from sugar-water feeding showed higher delta 13C values. Furthermore, a notable degree of variation in delta 13C was observed regarding the diverse beebread sources. A positive correlation was identified between delta 15N in beebreads and RJ, whereas a negative correlation (r = -0.89) was observed between delta 15N 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 delta 13C and delta 15N 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.

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