Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning
Food adulteration poses significant health risks globally and is rigorously monitored by safety authorities. In developing nations, where milk is highly prone to contamination (with Brazil, India, China, and Pakistan producing half of the world’s milk), stringent detection and classificat...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10759634/ |
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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 | Food adulteration poses significant health risks globally and is rigorously monitored by safety authorities. In developing nations, where milk is highly prone to contamination (with Brazil, India, China, and Pakistan producing half of the world’s milk), stringent detection and classification techniques are essential. This study employs both destructive and non-destructive methods for milk adulteration analysis. The destructive method uses Lactoscan for comprehensive qualitative measurements, including temperature, pH, conductivity, solids, protein, density, fat content, and SNF. The non-destructive method utilizes hyperspectral imaging (HSI) with the Specim Fx-10 (397–1003 nm) for image-based analysis, involving preprocessing steps like image scaling, ROI selection, radiometric correction, and spectral reflectance extraction using the empirical line method (ELM). Advanced deep learning models, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU), are employed to predict and classify pure and adulterated milk spectra. CNNs showed superior performance in identifying adulteration trends. The proposed pipeline, validated with a 97% accuracy, outperforms state-of-the-art techniques based on metrics such as Kappa, accuracy, precision, recall, F1-score, MCC, and Jaccard Index. |
| format | Article |
| id | doaj-art-c072d1c08dda4dd18a7f2ee3b3781861 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c072d1c08dda4dd18a7f2ee3b37818612025-08-20T02:48:46ZengIEEEIEEE Access2169-35362024-01-011217496517498210.1109/ACCESS.2024.350433410759634Hyperspectral Identification of Milk Adulteration Using Advance Deep LearningMuhammad Aqeel0https://orcid.org/0000-0002-7936-598XAhmed Sohaib1https://orcid.org/0000-0001-5831-3139Muhammad Iqbal2https://orcid.org/0000-0001-9587-3311Syed Sajid Ullah3https://orcid.org/0000-0002-5406-0389Advance Image Processing Research Laboratory (AIPRL), Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanAdvance Image Processing Research Laboratory (AIPRL), Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanAdvance Image Processing Research Laboratory (AIPRL), Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Information and Communication Technology, University of Agder (UiA), Grimstad, NorwayFood adulteration poses significant health risks globally and is rigorously monitored by safety authorities. In developing nations, where milk is highly prone to contamination (with Brazil, India, China, and Pakistan producing half of the world’s milk), stringent detection and classification techniques are essential. This study employs both destructive and non-destructive methods for milk adulteration analysis. The destructive method uses Lactoscan for comprehensive qualitative measurements, including temperature, pH, conductivity, solids, protein, density, fat content, and SNF. The non-destructive method utilizes hyperspectral imaging (HSI) with the Specim Fx-10 (397–1003 nm) for image-based analysis, involving preprocessing steps like image scaling, ROI selection, radiometric correction, and spectral reflectance extraction using the empirical line method (ELM). Advanced deep learning models, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU), are employed to predict and classify pure and adulterated milk spectra. CNNs showed superior performance in identifying adulteration trends. The proposed pipeline, validated with a 97% accuracy, outperforms state-of-the-art techniques based on metrics such as Kappa, accuracy, precision, recall, F1-score, MCC, and Jaccard Index.https://ieeexplore.ieee.org/document/10759634/Milk adulterationhyperspectral imagingdeep learningfood quality controlspectral reflectance signature |
| spellingShingle | Muhammad Aqeel Ahmed Sohaib Muhammad Iqbal Syed Sajid Ullah Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning IEEE Access Milk adulteration hyperspectral imaging deep learning food quality control spectral reflectance signature |
| title | Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning |
| title_full | Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning |
| title_fullStr | Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning |
| title_full_unstemmed | Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning |
| title_short | Hyperspectral Identification of Milk Adulteration Using Advance Deep Learning |
| title_sort | hyperspectral identification of milk adulteration using advance deep learning |
| topic | Milk adulteration hyperspectral imaging deep learning food quality control spectral reflectance signature |
| url | https://ieeexplore.ieee.org/document/10759634/ |
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