Academic Journal
Raman spectral analysis of Chinese yam for geographical origin discrimination using an attention-enhanced deep learning approach.
| Τίτλος: | Raman spectral analysis of Chinese yam for geographical origin discrimination using an attention-enhanced deep learning approach. |
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| Συγγραφείς: | 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. |
| Πηγή: | 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. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | 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: Original Publication: [Kidlington, Oxford, U.K. ; Tarrytown, NY] : Pergamon, c1994- |
| Ιατρικοί όροι (MeSH): | Dioscorea*/chemistry , Dioscorea*/classification , Spectrum Analysis, Raman*/methods , Deep Learning*, Geography ; China |
| Περίληψη: | 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 |
| Βάση Δεδομένων: | MEDLINE |
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