您好，欢迎访问云南省农业科学院 机构知识库！

Infrared spectroscopy combined with machine learning: A fast method for origin tracing and dry matter content prediction of Dendrobium officinale Kimura et Migo

文献类型： 外文期刊

作者： Feng, Yangna 1 ; Yang, Shaobing 2 ; Wang, Yuanzhong 2 ;

作者机构： 1.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Peoples R China

2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China

关键词： FT-NIR; ATR-FTIR; Dendrobium officinal; Prediction; Origin tracing

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.6； 五年影响因子：6.9 ）

ISSN： 0023-6438

年卷期： 2025 年 228 卷

页码：

收录情况： SCI

摘要： It is a key step to evaluate the quality of Dendrobium officinale Kimura et Migo by verifying its geographical origin and quickly analyzing and predicting its component content. To this end, Fourier transform near-infrared (FTNIR) and attenuated total reflection Fourier transform mid-infrared (ATR-FTIR) spectroscopy were used to characterize the chemical profiles of D. officinale. The two-dimensional correlation spectrum (2DCOS) images improve the spectral resolution, which is particularly important for analyzing the spectral absorption peaks. Moreover, the potential of infrared spectroscopy in tracing and predicting dry matter content (DMC) of D. officinale in different geographical origins was explored. Different spectral data, preprocessing, feature variable selection, and data fusion methods were compared. The partial least squares discriminant analysis (PLS-DA) model based on the original FT-NIR spectrum was used to trace the origin of D. officinale with 100 % accuracy. The FT-NIR data after second derivative (SD) processing combined with long short-term memory (LSTM) regression model could roughly predict DMC (R2p = 0.8026, RPD = 1.9149, RMSEC/RMSEP = 1.0433), which provides a rich method reference for the study of D. officinale based on infrared spectrum.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：云南省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台