Milk adulteration identification using hyperspectral imaging and machine learning
ABSTRACT: Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe h...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Dairy Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0022030224012992 |
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| author | Muhammad Aqeel Ahmed Sohaib Muhammad Iqbal Syed Sajid Ullah |
| author_facet | Muhammad Aqeel Ahmed Sohaib Muhammad Iqbal Syed Sajid Ullah |
| author_sort | Muhammad Aqeel |
| collection | DOAJ |
| description | ABSTRACT: Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe health risks, potentially leading to fatal diseases. Detecting and categorizing milk adulteration is crucial for consumer safety and the dairy industry. This research is divided into 2 breakthroughs, destructive and nondestructive methods. In the destructive method, the Lactoscan system was used for qualitative analysis: SNF, density, fat, lactose, conductivity, solids, protein, temperature, and pH level. The research also examines nondistractive hyperspectral imaging (HSI) through HSI Specim Fx-10 (397–1,003 nm; Specim, Spectral Imaging Ltd., Oulu, Finland) analysis to detect various phases of milk adulteration for accurate and user-friendly imaging-based adulterant detection and categorization. Preprocessing involves radiometric correction, image resizing, region of interest selection for feature extraction, and empirical line method to calculate spectral reflectance signature. Machine learning techniques (logistic regression, decision tree, support vector machine, and linear discriminant analysis [LDA]), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration dataset. Training, testing, and validation accuracy, precision, recall, F1 score, kappa, and Matthew's correlation coefficient metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment. |
| format | Article |
| id | doaj-art-0b2bd7328e2d43c3837b439148cd5de4 |
| institution | Kabale University |
| issn | 0022-0302 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dairy Science |
| spelling | doaj-art-0b2bd7328e2d43c3837b439148cd5de42025-01-23T05:25:18ZengElsevierJournal of Dairy Science0022-03022025-02-01108213011314Milk adulteration identification using hyperspectral imaging and machine learningMuhammad Aqeel0Ahmed Sohaib1Muhammad Iqbal2Syed Sajid Ullah3Advanced Image Processing Research Lab (AIPRL), Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanAdvanced Image Processing Research Lab (AIPRL), Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanAdvanced Image Processing Research Lab (AIPRL), Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan; School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; Corresponding authorsDepartment of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway; Corresponding authorsABSTRACT: Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe health risks, potentially leading to fatal diseases. Detecting and categorizing milk adulteration is crucial for consumer safety and the dairy industry. This research is divided into 2 breakthroughs, destructive and nondestructive methods. In the destructive method, the Lactoscan system was used for qualitative analysis: SNF, density, fat, lactose, conductivity, solids, protein, temperature, and pH level. The research also examines nondistractive hyperspectral imaging (HSI) through HSI Specim Fx-10 (397–1,003 nm; Specim, Spectral Imaging Ltd., Oulu, Finland) analysis to detect various phases of milk adulteration for accurate and user-friendly imaging-based adulterant detection and categorization. Preprocessing involves radiometric correction, image resizing, region of interest selection for feature extraction, and empirical line method to calculate spectral reflectance signature. Machine learning techniques (logistic regression, decision tree, support vector machine, and linear discriminant analysis [LDA]), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration dataset. Training, testing, and validation accuracy, precision, recall, F1 score, kappa, and Matthew's correlation coefficient metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment.http://www.sciencedirect.com/science/article/pii/S0022030224012992milk adulterationhyperspectral imagingmachine learningnondestructive analysisfood quality assessment |
| spellingShingle | Muhammad Aqeel Ahmed Sohaib Muhammad Iqbal Syed Sajid Ullah Milk adulteration identification using hyperspectral imaging and machine learning Journal of Dairy Science milk adulteration hyperspectral imaging machine learning nondestructive analysis food quality assessment |
| title | Milk adulteration identification using hyperspectral imaging and machine learning |
| title_full | Milk adulteration identification using hyperspectral imaging and machine learning |
| title_fullStr | Milk adulteration identification using hyperspectral imaging and machine learning |
| title_full_unstemmed | Milk adulteration identification using hyperspectral imaging and machine learning |
| title_short | Milk adulteration identification using hyperspectral imaging and machine learning |
| title_sort | milk adulteration identification using hyperspectral imaging and machine learning |
| topic | milk adulteration hyperspectral imaging machine learning nondestructive analysis food quality assessment |
| url | http://www.sciencedirect.com/science/article/pii/S0022030224012992 |
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