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FT-NIR and ATR-FTIR spectroscopy combined with machine learning for accurate identification of variants and hybrids of Gastrodia elata Blume.

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Title: FT-NIR and ATR-FTIR spectroscopy combined with machine learning for accurate identification of variants and hybrids of Gastrodia elata Blume.
Authors: Han, Duo1,2 (AUTHOR), Liu, Honggao1,2 (AUTHOR) honggaoliu@126.com, Wang, Yuanzhong1,3 (AUTHOR) boletus@126.com
Source: Microchemical Journal. Sep2025, Vol. 216, pN.PAG-N.PAG. 1p.
Subjects: Near infrared spectroscopy, Machine learning, Deep learning, Genetic variation, Attenuated total reflectance, Spectrometry
Abstract: Different variants of Gastrodia elata Blume (GEB) have differences in chemical composition due to genetic characteristics, which inevitably affect their quality, so the identification of GEB is of great significance for the effective utilization of its resources. Unsupervised visualization models Principal Component Analysis and T-Distributed Stochastic Neighbor Embedding (PCA and t-SNE), supervised models Partial Least Squares Discriminant Analysis and Support Vector Machine(PLS-DA and SVM), and a deep learning model Residual Convolutional Neural Network (ResNet) were constructed using FT-NIR, ATR-FTIR, and FT-NIR + ATR-FTIR dataset, respectively, to quickly and reliably identify Different species of GEB. The results showed that the visualization effect of t-SNE was better than PCA, but they could not classify four GEB. The performance of SVM model is better than PLS-DA model, but it requires complex preprocessing to improve the accuracy. The accuracy of ResNet model established by three spectral dataset reached 100 % in the training set, test set, and external validation set, it achieved the best effect without complex preprocessing. Comparing PLS-DA, SVM, and ResNet models under three spectral dataset, FT-NIR was relatively best. It can be inferred that ResNet model based on FT-NIR is more suitable for the identification of variants and hybrids of GEB. This method may be applied to GEB products and other research fields. [Display omitted] • FT-NIR and ATR-FTIR spectroscopy combined with Machine Learning to identify variants and hybrids of GEB. • The ResNet model identified variants and hybrids of GEB with 100.00 % accuracy, and it does not require complex preprocessing. • FT-NIR identification of variants and hybrids of GEB is superior to ATR-FTIR and FT-NIR + ATR-FTIR. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Different variants of Gastrodia elata Blume (GEB) have differences in chemical composition due to genetic characteristics, which inevitably affect their quality, so the identification of GEB is of great significance for the effective utilization of its resources. Unsupervised visualization models Principal Component Analysis and T-Distributed Stochastic Neighbor Embedding (PCA and t-SNE), supervised models Partial Least Squares Discriminant Analysis and Support Vector Machine(PLS-DA and SVM), and a deep learning model Residual Convolutional Neural Network (ResNet) were constructed using FT-NIR, ATR-FTIR, and FT-NIR + ATR-FTIR dataset, respectively, to quickly and reliably identify Different species of GEB. The results showed that the visualization effect of t-SNE was better than PCA, but they could not classify four GEB. The performance of SVM model is better than PLS-DA model, but it requires complex preprocessing to improve the accuracy. The accuracy of ResNet model established by three spectral dataset reached 100 % in the training set, test set, and external validation set, it achieved the best effect without complex preprocessing. Comparing PLS-DA, SVM, and ResNet models under three spectral dataset, FT-NIR was relatively best. It can be inferred that ResNet model based on FT-NIR is more suitable for the identification of variants and hybrids of GEB. This method may be applied to GEB products and other research fields. [Display omitted] • FT-NIR and ATR-FTIR spectroscopy combined with Machine Learning to identify variants and hybrids of GEB. • The ResNet model identified variants and hybrids of GEB with 100.00 % accuracy, and it does not require complex preprocessing. • FT-NIR identification of variants and hybrids of GEB is superior to ATR-FTIR and FT-NIR + ATR-FTIR. [ABSTRACT FROM AUTHOR]
ISSN:0026265X
DOI:10.1016/j.microc.2025.114725