数字农科院2.0

A machine learning approach fusing multisource spectral data for prediction of floral origins and taste components of Apis cerana honey

文献类型： 外文期刊

作者： Zhaolong Liu;Hongxia Li;Nan Liu;Cuiling Liu;Xiaorong Sun;Lanzhen Chen

作者机构：

关键词： Classification;Data fusion;Honey;Machine learning;Quantitative prediction;Spectroscopy

期刊名称： Food Research International

ISSN： 0963-9969

年卷期： 2025 年 208 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： This study explores the use of near-infrared (NIR), mid-infrared (MIR), and Raman spectral fusion for the rapid prediction of floral origins and main taste components in Apis cerana (A. cerana) honey. Feature-level fusion with the partial least squares regression - random forest (PLSR-RF) model achieved 100 % classification accuracy in identifying floral origins. Additionally, the model demonstrated strong predictive capability for sugars, amino acids, and organic acids, with R2 values ranging from 0.88 to 0.96, and performed exceptionally in predicting total organic acids and amino acids (R2 of 0.94 and 0.93, respectively). The PLSR-RF model showed effective clustering for proline, glucose, and fructose, achieving a 23.5 % improvement in predictive accuracy compared to data-level fusion. These findings confirm the efficacy of the PLSR-RF model for quantitative analysis of A. cerana honey.

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