Application of Terahertz Technology in Food Safety: Rice Origin–Variety Classification Based on Spectral Analysis and Machine Learning
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Le résumé fourni par la source
Food security serves as a vital cornerstone for social stability. As one of the most important staple crops globally, the quality and geographical origin of rice are directly associated with consumer health. Traditional methods for classifying rice by origin and variety rely on sensory evaluation and manual inspection, which are subject to uncertainty and human error. To address this, this paper proposes a method for classifying rice by origin and variety based on terahertz time-domain spectroscopy. Terahertz technology features the advantages of non-destructive, high-sensitivity and non-contact detection, making it well-suited for food detection. This study employs terahertz time-domain spectroscopy combined with machine learning modeling methods, using 20 types of rice as the subject of investigation, with a focus on modeling and analyzing four representative samples. Refractive index and absorption coefficient were extracted through preprocessing methods including Savitzky-Golay convolution smoothing, wavelet denoising and moving average smoothing. Modeling, classification, and detection were implemented using principal component analysis, partial least squares discriminant analysis, and least-squares support vector machine. The experimental results indicate that principal component analysis (PCA) alone performs poorly in classification tasks. However, a classification model combining PCA for dimensionality reduction with a least-squares support vector machine (SVM), following Savitzky-Golay smoothing, demonstrated the best performance, achieving a prediction accuracy of 93.3%. In an extended test involving 20 samples, the model achieved an identification accuracy of 89.6%. Quantitative metrics demonstrate the feasibility of using terahertz technology combined with optimized machine learning algorithms for classifying rice by origin and variety.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Application of Terahertz Technology in Food Safety: Rice Origin–Variety Classification Based on Spectral Analysis and Machine Learning
- Date Crossref
- 03/06/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Aviation Industry Corporation of China (China) pays non établi dans la noticeEntreprise
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Anhui Polytechnic University pays non établi dans la noticeUniversité ou école supérieure
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Wuhu Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
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Tianjin University pays non établi dans la noticeUniversité ou école supérieure
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ZheJiang Academy of Agricultural Sciences pays non établi dans la noticeOrganisation à but non lucratif
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Ministry of Agriculture and Rural Affairs pays non établi dans la noticeOrganisme public
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Aviation Industry Corporation Huadong Photoelectric Co. pays non établi dans la noticeOrganisation à but non lucratif
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School of Artificial Intelligence pays non établi dans la noticeUniversité ou école supérieure
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School of Intelligent Manufacturing pays non établi dans la noticeUniversité ou école supérieure
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School of Precision Instruments and Opto-Electronics Engineering Key Laboratory of Opto-Electronics Information Technology pays non établi dans la noticeUniversité ou école supérieure
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Key Laboratory of Information Traceability for Agricultural Products pays non établi dans la noticeStructure de recherche
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State Key Laboratory for Quality and Safety of Agro-Products pays non établi dans la noticeStructure de recherche
Aviation Industry Corporation of China (China), Anhui Polytechnic University et Wuhu Institute of Technology, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.