Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour
The safety and quality of wheat flour, a staple in daily life, directly affect people's health. The illegal use of additives such as azodicarbonamide, talcum and gypsum powders can lead to serious health risks. Traditional detection methods are time-consuming and unsuitable for routine screenin...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | Applied Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772502225005682 |
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| author | Xinyi Dong Ying Dong Jinming Liu Siting Wu |
| author_facet | Xinyi Dong Ying Dong Jinming Liu Siting Wu |
| author_sort | Xinyi Dong |
| collection | DOAJ |
| description | The safety and quality of wheat flour, a staple in daily life, directly affect people's health. The illegal use of additives such as azodicarbonamide, talcum and gypsum powders can lead to serious health risks. Traditional detection methods are time-consuming and unsuitable for routine screening, creating an urgent need for rapid, non-destructive techniques. To identify multiple illicit additives within wheat flour, a simultaneous detection model for three additives—azodicarbonamide, talcum and gypsum powders—was constructed using near-infrared spectroscopy and chemometrics methods. The model combines long short-term memory network (LSTM) data dimensionality reduction with partial least squares to detect multiple illicit additives in wheat flour. The Bayesian optimization algorithm was used to optimize the LSTM parameters. Compared to regression models built with competitive adaptive reweighted sampling and genetic algorithm for feature wavelength selection, the performance improved significantly, enhancing generalization capability. The validation set’s coefficients of determination were 0.9828, 0.9771, 0.9765, with root mean square errors of 0.0008 %, 0.2915 %, 0.2822 %, and residual prediction errors of 7.4067, 6.4020, and 6.2159, respectively. Combining near-infrared spectroscopy with LSTM dimensionality reduction and partial least squares enables quick, simultaneous detection of various illicit additives in wheat flour, providing a novel approach for efficient and precise inspection. |
| format | Article |
| id | doaj-art-830e791df5e3414db576b59b62103d62 |
| institution | Kabale University |
| issn | 2772-5022 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Food Research |
| spelling | doaj-art-830e791df5e3414db576b59b62103d622025-08-20T05:08:15ZengElsevierApplied Food Research2772-50222025-12-015210126310.1016/j.afres.2025.101263Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flourXinyi Dong0Ying Dong1Jinming Liu2Siting Wu3College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China; Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, ChinaGuangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China; Huangpu Customs Technology Center, Dongguan 523000, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China; Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R. China, Guangzhou 510700, China; Corresponding author at: College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang 163319, China.College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaThe safety and quality of wheat flour, a staple in daily life, directly affect people's health. The illegal use of additives such as azodicarbonamide, talcum and gypsum powders can lead to serious health risks. Traditional detection methods are time-consuming and unsuitable for routine screening, creating an urgent need for rapid, non-destructive techniques. To identify multiple illicit additives within wheat flour, a simultaneous detection model for three additives—azodicarbonamide, talcum and gypsum powders—was constructed using near-infrared spectroscopy and chemometrics methods. The model combines long short-term memory network (LSTM) data dimensionality reduction with partial least squares to detect multiple illicit additives in wheat flour. The Bayesian optimization algorithm was used to optimize the LSTM parameters. Compared to regression models built with competitive adaptive reweighted sampling and genetic algorithm for feature wavelength selection, the performance improved significantly, enhancing generalization capability. The validation set’s coefficients of determination were 0.9828, 0.9771, 0.9765, with root mean square errors of 0.0008 %, 0.2915 %, 0.2822 %, and residual prediction errors of 7.4067, 6.4020, and 6.2159, respectively. Combining near-infrared spectroscopy with LSTM dimensionality reduction and partial least squares enables quick, simultaneous detection of various illicit additives in wheat flour, providing a novel approach for efficient and precise inspection.http://www.sciencedirect.com/science/article/pii/S2772502225005682Illicit additives in flourNear-infrared spectroscopyPartial least squaresFeature extractionLong short-term memory networkBayesian optimization algorithm |
| spellingShingle | Xinyi Dong Ying Dong Jinming Liu Siting Wu Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour Applied Food Research Illicit additives in flour Near-infrared spectroscopy Partial least squares Feature extraction Long short-term memory network Bayesian optimization algorithm |
| title | Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour |
| title_full | Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour |
| title_fullStr | Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour |
| title_full_unstemmed | Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour |
| title_short | Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour |
| title_sort | combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour |
| topic | Illicit additives in flour Near-infrared spectroscopy Partial least squares Feature extraction Long short-term memory network Bayesian optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2772502225005682 |
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