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. |
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| 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] |
| Copyright of Microchemical Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187504407 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: FT-NIR and ATR-FTIR spectroscopy combined with machine learning for accurate identification of variants and hybrids of Gastrodia elata Blume. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Han%2C+Duo%22">Han, Duo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Honggao%22">Liu, Honggao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> honggaoliu@126.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yuanzhong%22">Wang, Yuanzhong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> boletus@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Microchemical+Journal%22">Microchemical Journal</searchLink>. Sep2025, Vol. 216, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Near+infrared+spectroscopy%22">Near infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+variation%22">Genetic variation</searchLink><br /><searchLink fieldCode="DE" term="%22Attenuated+total+reflectance%22">Attenuated total reflectance</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrometry%22">Spectrometry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Microchemical Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.microc.2025.114725 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Near infrared spectroscopy Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Genetic variation Type: general – SubjectFull: Attenuated total reflectance Type: general – SubjectFull: Spectrometry Type: general Titles: – TitleFull: FT-NIR and ATR-FTIR spectroscopy combined with machine learning for accurate identification of variants and hybrids of Gastrodia elata Blume. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Han, Duo – PersonEntity: Name: NameFull: Liu, Honggao – PersonEntity: Name: NameFull: Wang, Yuanzhong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0026265X Numbering: – Type: volume Value: 216 Titles: – TitleFull: Microchemical Journal Type: main |
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