Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour
This research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. Five different algori...
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MDPI AG
2025-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/13/2393 |
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| author | Yuling Wang Chen Zhang Xinhua Li Longzhu Xing Mengchao Lv Hongju He Leiqing Pan Xingqi Ou |
| author_facet | Yuling Wang Chen Zhang Xinhua Li Longzhu Xing Mengchao Lv Hongju He Leiqing Pan Xingqi Ou |
| author_sort | Yuling Wang |
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| description | This research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. Five different algorithms were employed to mine the relationship between the full-range spectra (900–1700 nm) and three parameters, with support vector regression (SVR) demonstrating the best prediction performance for all gluten parameters (R<sub>P</sub> = 0.9370–0.9430, RMSEP = 0.3450–0.4043%, and RPD = 3.1348–3.4998). Through a comparative evaluation of five wavelength selection techniques, 25–30 optimal wavelengths were identified, enabling the development of optimized SVR models. The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (R<sub>P</sub> = 0.9190–0.9385, RMSEP = 0.3927–0.5743%, and RPD = 3.0424–3.2509). The model’s robustness was confirmed through external validation and statistical analyses (<i>p</i> > 0.05 for F-test and <i>t</i>-test). The results highlight the effectiveness of micro-NIRS combined with iWOA-SVR for the nondestructive gluten quality assessment of wheat flour, providing a more valuable reference for expanding the use of NIRS technology and developing portable specialized NIRS equipment for industrial-level applications in the future. |
| format | Article |
| id | doaj-art-7130a56cf9d241cf85ba27601d6e351b |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-7130a56cf9d241cf85ba27601d6e351b2025-08-20T03:28:32ZengMDPI AGFoods2304-81582025-07-011413239310.3390/foods14132393Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat FlourYuling Wang0Chen Zhang1Xinhua Li2Longzhu Xing3Mengchao Lv4Hongju He5Leiqing Pan6Xingqi Ou7School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Xinxiang Institute of Engineering, Xinxiang 453700, ChinaSchool of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, ChinaCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, ChinaSchool of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, ChinaThis research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. Five different algorithms were employed to mine the relationship between the full-range spectra (900–1700 nm) and three parameters, with support vector regression (SVR) demonstrating the best prediction performance for all gluten parameters (R<sub>P</sub> = 0.9370–0.9430, RMSEP = 0.3450–0.4043%, and RPD = 3.1348–3.4998). Through a comparative evaluation of five wavelength selection techniques, 25–30 optimal wavelengths were identified, enabling the development of optimized SVR models. The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (R<sub>P</sub> = 0.9190–0.9385, RMSEP = 0.3927–0.5743%, and RPD = 3.0424–3.2509). The model’s robustness was confirmed through external validation and statistical analyses (<i>p</i> > 0.05 for F-test and <i>t</i>-test). The results highlight the effectiveness of micro-NIRS combined with iWOA-SVR for the nondestructive gluten quality assessment of wheat flour, providing a more valuable reference for expanding the use of NIRS technology and developing portable specialized NIRS equipment for industrial-level applications in the future.https://www.mdpi.com/2304-8158/14/13/2393wheat flourNIRSchemometricsglutenmachine learning |
| spellingShingle | Yuling Wang Chen Zhang Xinhua Li Longzhu Xing Mengchao Lv Hongju He Leiqing Pan Xingqi Ou Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour Foods wheat flour NIRS chemometrics gluten machine learning |
| title | Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour |
| title_full | Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour |
| title_fullStr | Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour |
| title_full_unstemmed | Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour |
| title_short | Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour |
| title_sort | miniaturized nirs coupled with machine learning algorithm for noninvasively quantifying gluten quality in wheat flour |
| topic | wheat flour NIRS chemometrics gluten machine learning |
| url | https://www.mdpi.com/2304-8158/14/13/2393 |
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