Comprehensive evaluation of Dendrobium officinale from different geographical origins using near-infrared spectroscopy and chemometrics

化学计量学 石斛 线性判别分析 数学 偏最小二乘回归 统计 计算机科学 人工智能 机器学习 植物 生物
作者
Yue Yang,Xiangting She,Xiaoqing Cao,Liuchang Yang,Jiamin Huang,Xu Zhang,Laijin Su,Mingjiang Wu,Haibin Tong,Xiaoliang Ji
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier]
卷期号:277: 121249-121249 被引量:14
标识
DOI:10.1016/j.saa.2022.121249
摘要

Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (Rp2) in the independent test sets were 12.7768 g kg-1 and 0.9586, 1.1346 g kg-1 and 0.9670, 0.3938 g kg-1 and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.
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