NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat
This study investigated the utilization of near-infrared spectroscopy (NIRS) and machine learning methodologies to differentiate cooked broiler and duck meat. Nearinfrared spectral data were acquired using a portable spectrometer (700–1100 nm) from 40 samples (20 broilers and 20 duck breasts). The d...
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Elsevier
2025-06-01
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| Series: | Applied Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772502225002926 |
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| author | Kamrunnahar Khan Bristy Dip Ghosh Md. Abul Hashem |
| author_facet | Kamrunnahar Khan Bristy Dip Ghosh Md. Abul Hashem |
| author_sort | Kamrunnahar Khan Bristy |
| collection | DOAJ |
| description | This study investigated the utilization of near-infrared spectroscopy (NIRS) and machine learning methodologies to differentiate cooked broiler and duck meat. Nearinfrared spectral data were acquired using a portable spectrometer (700–1100 nm) from 40 samples (20 broilers and 20 duck breasts). The data were preprocessed and analyzed using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). PCA revealed 87.97 % variance, demonstrating distinct groupings. Various machine learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, Decision Tree, Naive Bayes, Neural Network, and XGB, were evaluated. Hyperparameter tuning and 5-fold cross-validation were implemented to mitigate overfitting and enhance the generalization. Logistic Regression, SVM, and LDA achieved the highest accuracy (84.13 %, 84 %, and 83.50 %, respectively) and ROC AUC (92.99 %, 92.63 %, and 91.71 %, respectively), indicating robust discriminative power. Random Forest and Gradient Boosting also exhibited satisfactory performance, with consistent cross-validation scores (∼0.824). XGB demonstrated marginally superior recall compared to K-Nearest Neighbors, while Neural Network excelled in recall (90.74 %), a critical factor for detecting true positives. Decision Tree and Naive Bayes, although interpretable, underperformed relative to other algorithms. Logistic Regression and SVM have emerged as the most efficacious methods, attaining high accuracy and balanced precision and recall. This study underscores the potential of integrating spectroscopic data with advanced machinelearning techniques for meat classification and fraud detection. |
| format | Article |
| id | doaj-art-ed7db82c22a844099bb9c3c9b2a8b164 |
| institution | DOAJ |
| issn | 2772-5022 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Food Research |
| spelling | doaj-art-ed7db82c22a844099bb9c3c9b2a8b1642025-08-20T03:20:12ZengElsevierApplied Food Research2772-50222025-06-015110098410.1016/j.afres.2025.100984NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meatKamrunnahar Khan Bristy0Dip Ghosh1Md. Abul Hashem2Department of Animal Science, Faculty of Animal Husbandry, Bangladesh Agricultural University, Mymensigh 2202, BangladeshDepartment of Animal Science, Faculty of Animal Husbandry, Bangladesh Agricultural University, Mymensigh 2202, BangladeshCorresponding author.; Department of Animal Science, Faculty of Animal Husbandry, Bangladesh Agricultural University, Mymensigh 2202, BangladeshThis study investigated the utilization of near-infrared spectroscopy (NIRS) and machine learning methodologies to differentiate cooked broiler and duck meat. Nearinfrared spectral data were acquired using a portable spectrometer (700–1100 nm) from 40 samples (20 broilers and 20 duck breasts). The data were preprocessed and analyzed using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). PCA revealed 87.97 % variance, demonstrating distinct groupings. Various machine learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, Decision Tree, Naive Bayes, Neural Network, and XGB, were evaluated. Hyperparameter tuning and 5-fold cross-validation were implemented to mitigate overfitting and enhance the generalization. Logistic Regression, SVM, and LDA achieved the highest accuracy (84.13 %, 84 %, and 83.50 %, respectively) and ROC AUC (92.99 %, 92.63 %, and 91.71 %, respectively), indicating robust discriminative power. Random Forest and Gradient Boosting also exhibited satisfactory performance, with consistent cross-validation scores (∼0.824). XGB demonstrated marginally superior recall compared to K-Nearest Neighbors, while Neural Network excelled in recall (90.74 %), a critical factor for detecting true positives. Decision Tree and Naive Bayes, although interpretable, underperformed relative to other algorithms. Logistic Regression and SVM have emerged as the most efficacious methods, attaining high accuracy and balanced precision and recall. This study underscores the potential of integrating spectroscopic data with advanced machinelearning techniques for meat classification and fraud detection.http://www.sciencedirect.com/science/article/pii/S2772502225002926Nir spectroscopyMachine learning algorithmsPrincipal component analysis;Hyperparameter OptimizationEnsemble Model |
| spellingShingle | Kamrunnahar Khan Bristy Dip Ghosh Md. Abul Hashem NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat Applied Food Research Nir spectroscopy Machine learning algorithms Principal component analysis;Hyperparameter Optimization Ensemble Model |
| title | NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat |
| title_full | NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat |
| title_fullStr | NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat |
| title_full_unstemmed | NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat |
| title_short | NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat |
| title_sort | nirs and machine learning algorithms as a non invasive technique to discriminate and classify cooked broiler and duck meat |
| topic | Nir spectroscopy Machine learning algorithms Principal component analysis;Hyperparameter Optimization Ensemble Model |
| url | http://www.sciencedirect.com/science/article/pii/S2772502225002926 |
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