Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM
Different varieties of wheat as one of the strategic crops are cultivated in Iran based on the specific geographical and climatic conditions of each area. Classification of wheat varieties is important in order to guarantee the final products acquired from wheat flour. Fourier Transform-Mid Infrared...
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| Format: | Article |
| Language: | English |
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Ferdowsi University of Mashhad
2025-07-01
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| Series: | مجله پژوهشهای علوم و صنایع غذایی ایران |
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| Online Access: | https://ifstrj.um.ac.ir/article_46905_e08e13a6c207438aa6d513bb291771da.pdf |
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| author | Amir Kazemi Asghar Mahmoudi Seyyed Hossein Fattahi |
| author_facet | Amir Kazemi Asghar Mahmoudi Seyyed Hossein Fattahi |
| author_sort | Amir Kazemi |
| collection | DOAJ |
| description | Different varieties of wheat as one of the strategic crops are cultivated in Iran based on the specific geographical and climatic conditions of each area. Classification of wheat varieties is important in order to guarantee the final products acquired from wheat flour. Fourier Transform-Mid Infrared (FT-MIR) spectroscopy as a nondestructive approach combined with chemometrics was employed to classify four varieties of Iranian wheat. 160 samples were analyzed and various preprocessing algorithms were used to correct unwanted information. Then, Principal Component Analysis (PCA) as unsupervised and Support Vector Machine (SVM) as supervised models with Max-Relevance Min-Redundancy (MRMR) feature selection algorithm were applied to investigate the classification of these varieties. The best result of SVM model without feature selection was with S-G+D2+MSC preprocessing with 99.4% of accuracy. The output of 100% with SVM model and MRMR feature selection algorithm confirmed the capability of FT-MIR spectroscopy method for classification of Iranian wheat flour varieties. |
| format | Article |
| id | doaj-art-1d0c59e12db54b969ea29ff9343839bf |
| institution | Kabale University |
| issn | 1735-4161 2228-5415 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | مجله پژوهشهای علوم و صنایع غذایی ایران |
| spelling | doaj-art-1d0c59e12db54b969ea29ff9343839bf2025-08-20T03:28:05ZengFerdowsi University of Mashhadمجله پژوهشهای علوم و صنایع غذایی ایران1735-41612228-54152025-07-0121326126910.22067/ifstrj.2024.88624.134246905Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVMAmir Kazemi0Asghar Mahmoudi1Seyyed Hossein Fattahi2Department of Biosystems Engineering, University of Tabriz, Tabriz, IranDepartment of Biosystems Engineering, University of Tabriz, Tabriz, IranDepartment of Biosystems Engineering, University of Maragheh, Maragheh, IranDifferent varieties of wheat as one of the strategic crops are cultivated in Iran based on the specific geographical and climatic conditions of each area. Classification of wheat varieties is important in order to guarantee the final products acquired from wheat flour. Fourier Transform-Mid Infrared (FT-MIR) spectroscopy as a nondestructive approach combined with chemometrics was employed to classify four varieties of Iranian wheat. 160 samples were analyzed and various preprocessing algorithms were used to correct unwanted information. Then, Principal Component Analysis (PCA) as unsupervised and Support Vector Machine (SVM) as supervised models with Max-Relevance Min-Redundancy (MRMR) feature selection algorithm were applied to investigate the classification of these varieties. The best result of SVM model without feature selection was with S-G+D2+MSC preprocessing with 99.4% of accuracy. The output of 100% with SVM model and MRMR feature selection algorithm confirmed the capability of FT-MIR spectroscopy method for classification of Iranian wheat flour varieties.https://ifstrj.um.ac.ir/article_46905_e08e13a6c207438aa6d513bb291771da.pdfclassificationft-mir spectroscopypcapreprocessingwheat flour |
| spellingShingle | Amir Kazemi Asghar Mahmoudi Seyyed Hossein Fattahi Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM مجله پژوهشهای علوم و صنایع غذایی ایران classification ft-mir spectroscopy pca preprocessing wheat flour |
| title | Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM |
| title_full | Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM |
| title_fullStr | Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM |
| title_full_unstemmed | Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM |
| title_short | Classification of Iranian Wheat Flour by FT-MIR Spectroscopy based on Max-Relevance Min-Redundancy Wavelength Selection Coupled with SVM |
| title_sort | classification of iranian wheat flour by ft mir spectroscopy based on max relevance min redundancy wavelength selection coupled with svm |
| topic | classification ft-mir spectroscopy pca preprocessing wheat flour |
| url | https://ifstrj.um.ac.ir/article_46905_e08e13a6c207438aa6d513bb291771da.pdf |
| work_keys_str_mv | AT amirkazemi classificationofiranianwheatflourbyftmirspectroscopybasedonmaxrelevanceminredundancywavelengthselectioncoupledwithsvm AT asgharmahmoudi classificationofiranianwheatflourbyftmirspectroscopybasedonmaxrelevanceminredundancywavelengthselectioncoupledwithsvm AT seyyedhosseinfattahi classificationofiranianwheatflourbyftmirspectroscopybasedonmaxrelevanceminredundancywavelengthselectioncoupledwithsvm |