Rapid and accurate identification of Gastrodia elata Blume species based on FTIR and NIR spectroscopy combined with chemometric methods.

IF 5.6 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Talanta Pub Date : 2025-01-01 Epub Date: 2024-09-19 DOI:10.1016/j.talanta.2024.126910
Guangyao Li, Jieqing Li, Honggao Liu, Yuanzhong Wang
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引用次数: 0

Abstract

Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting.

基于傅立叶变换红外光谱和近红外光谱以及化学计量学方法,快速准确地鉴定 Gastrodia elata Blume 的种类。
不同品种的天麻品质不同,多糖、天麻素等有效成分含量也不同,一般认为有效成分含量越高,天麻品质越好,药效越强。因此,有效鉴定 G. elata Bl. 物种至关重要,具有重要的理论和实践意义。本研究首先建立了用于数据可视化的无监督 PCA 和 t-SNE,然后建立了传统机器学习(PLS-DA、OPLS-DA 和 SVM)模型,并基于三个 G. elata Bl. 物种的傅立叶变换红外光谱(FTIR)和近红外光谱(NIR)数据建立了深度学习(ResNet)模型。结果表明,PLS-DA、OPLS-DA 和 SVM 模型需要对光谱数据进行复杂的预处理才能建立稳定可靠的模型。与传统的机器学习模型相比,ResNet 模型不需要复杂的光谱预处理,基于原始近红外光谱和低级数据融合(傅立叶变换红外光谱+近红外光谱)光谱建立的 ResNet 模型的训练集和测试集的准确率达到 100%,基于低级数据融合的外部验证集的准确率达到 100%,基于近红外光谱的外部验证集只有一个样本的分类误差,没有过拟合现象。
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来源期刊
Talanta
Talanta 化学-分析化学
CiteScore
12.30
自引率
4.90%
发文量
861
审稿时长
29 days
期刊介绍: Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome. Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.
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