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

第一作者： Liu, Zhaolong

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

作者机构：

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

期刊名称：FOOD RESEARCH INTERNATIONAL （ 影响因子：8.0； 五年影响因子：8.5 ）

ISSN： 0963-9969

年卷期： 2025 年 208 卷

页码：

收录情况： SCI

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 inApis 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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序