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Raman spectral analysis of Chinese yam for geographical origin discrimination using an attention-enhanced deep learning approach.

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Title: Raman spectral analysis of Chinese yam for geographical origin discrimination using an attention-enhanced deep learning approach.
Authors: Gao S; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China., Wu R; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China., Yang Z; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China., Chen M; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China., Zhang H; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China., Qin Y; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China., Zhang M; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China., Li P; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China. Electronic address: lipeng@haut.edu.cn.
Source: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2027 Jan 05; Vol. 364 (Pt 1), pp. 128530. Date of Electronic Publication: 2026 Aug 01.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE
Imprint Name(s): Publication: : Amsterdam : Elsevier
Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994-
MeSH Terms: Dioscorea*/chemistry , Dioscorea*/classification , Spectrum Analysis, Raman*/methods , Deep Learning*, Geography ; China
Abstract: Dioscorea spp. (Chinese yam) is a medicinal and edible plant whose chemical composition and spectral characteristics are strongly influenced by geographical origin. Developing a rapid and non-destructive Raman spectroscopic strategy is important for characterizing origin-related spectral variations in yam samples. In this study, an attention-enhanced deep learning approach was developed for the analysis of complex Raman spectra of Chinese yam. By integrating multi-scale feature extraction with adaptive feature enhancement, the proposed method effectively mitigates peak overlap, improves the representation of weak origin-discriminative spectral patterns, and suppresses background interference. A total of 960 Raman spectra were collected from 192 yam slice samples across six major production regions. The proposed method achieved a classification accuracy of 98.08%, outperforming conventional machine learning models and standard 1D-CNNs. Attention visualization further revealed that the model focused on chemically meaningful Raman fingerprint regions, indicating improved interpretability in spectral feature extraction. These results demonstrate that Raman spectroscopy combined with attention-enhanced deep learning provides an effective and non-destructive strategy for geographical origin discrimination of Dioscorea spp. and spectral analysis of complex medicinal and edible plant matrices.
(Copyright © 2026 Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Deep learning; Dioscorea spp.; Geographical origin discrimination; Raman spectroscopy; Spectral analysis
Entry Date(s): Date Created: 20260805 Date Completed: 20260907 Latest Revision: 20260907
Update Code: 20260908
DOI: 10.1016/j.saa.2026.128530
PMID: 42556292
Database: MEDLINE
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  Data: Raman spectral analysis of Chinese yam for geographical origin discrimination using an attention-enhanced deep learning approach.
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  Data: <searchLink fieldCode="AU" term="%22Gao+S%22">Gao S</searchLink>; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Wu+R%22">Wu R</searchLink>; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Yang+Z%22">Yang Z</searchLink>; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Chen+M%22">Chen M</searchLink>; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Zhang+H%22">Zhang H</searchLink>; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Qin+Y%22">Qin Y</searchLink>; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Zhang+M%22">Zhang M</searchLink>; Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou 450001, China.<br /><searchLink fieldCode="AU" term="%22Li+P%22">Li P</searchLink>; Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China. Electronic address: lipeng@haut.edu.cn.
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  Data: <searchLink fieldCode="MM" term="%22Dioscorea%22">Dioscorea*</searchLink>/<searchLink fieldCode="MM" term="%22Dioscorea+chemistry%22">chemistry</searchLink> <br /><searchLink fieldCode="MM" term="%22Dioscorea%22">Dioscorea*</searchLink>/<searchLink fieldCode="MM" term="%22Dioscorea+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Spectrum+Analysis%2C+Raman%22">Spectrum Analysis, Raman*</searchLink>/<searchLink fieldCode="MM" term="%22Spectrum+Analysis%2C+Raman+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Deep+Learning%22">Deep Learning*</searchLink><br /><searchLink fieldCode="MH" term="%22Geography%22">Geography</searchLink> ; <searchLink fieldCode="MH" term="%22China%22">China</searchLink>
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  Data: Dioscorea spp. (Chinese yam) is a medicinal and edible plant whose chemical composition and spectral characteristics are strongly influenced by geographical origin. Developing a rapid and non-destructive Raman spectroscopic strategy is important for characterizing origin-related spectral variations in yam samples. In this study, an attention-enhanced deep learning approach was developed for the analysis of complex Raman spectra of Chinese yam. By integrating multi-scale feature extraction with adaptive feature enhancement, the proposed method effectively mitigates peak overlap, improves the representation of weak origin-discriminative spectral patterns, and suppresses background interference. A total of 960 Raman spectra were collected from 192 yam slice samples across six major production regions. The proposed method achieved a classification accuracy of 98.08%, outperforming conventional machine learning models and standard 1D-CNNs. Attention visualization further revealed that the model focused on chemically meaningful Raman fingerprint regions, indicating improved interpretability in spectral feature extraction. These results demonstrate that Raman spectroscopy combined with attention-enhanced deep learning provides an effective and non-destructive strategy for geographical origin discrimination of Dioscorea spp. and spectral analysis of complex medicinal and edible plant matrices.<br /> (Copyright © 2026 Elsevier B.V. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: <i>Keywords: </i>Deep learning; Dioscorea spp.; Geographical origin discrimination; Raman spectroscopy; Spectral analysis
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