The Feasibility of Artificial Intelligence and Raman Spectroscopy for Determining the Authenticity of Minced Meat
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| Vydáno v: | Foods vol. 14, no. 17 (2025), p. 3084-3099 |
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| Hlavní autor: | |
| Další autoři: | , , , , , , |
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MDPI AG
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| On-line přístup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/foods14173084 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231462 |2 nlm | ||
| 100 | 1 | |a Nedeljkovic Aleksandar |u Department of Animal Source Food Technology, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia | |
| 245 | 1 | |a The Feasibility of Artificial Intelligence and Raman Spectroscopy for Determining the Authenticity of Minced Meat | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Food fraud in meat products presents serious economic and public health challenges, underscoring the need for rapid and reliable detection methods. This study investigates the potential of Raman spectroscopy combined with machine learning to accurately discriminate between pure and mixed minced meat preparations. We evaluated three classification algorithms: Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forests (RFs). Raman spectra were collected from 19 distinct samples consisting of different ratios of pork, beef, and lamb minced meat. Our findings suggest that homogenization markedly enhances spectral consistency and classification accuracy. In the pure meat samples case, all three models (SVM, ANN, and RF) achieved notable increases in classification accuracies (from 0.50–0.70 to above 0.85), a dramatic improvement over unhomogenized samples. In more complex homogenized mixtures, SVM delivered the highest performance, achieving an accuracy of up to 0.88 for 50:50 mixtures and 0.86 for multi-ratio samples, often outperforming both ANN and RF. While the underlying interpretation of the classification models remains complex, the findings consistently underscore the critical role of homogenization on model performance. This work demonstrates the robust potential of the Raman spectroscopy-coupled machine learning approach for the rapid and accurate identification of minced meat species. | |
| 610 | 4 | |a HORIBA Ltd | |
| 651 | 4 | |a France | |
| 653 | |a Raman spectra | ||
| 653 | |a Beef | ||
| 653 | |a Public health | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Food | ||
| 653 | |a Meat products | ||
| 653 | |a Homogenization | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Pork | ||
| 653 | |a Data processing | ||
| 653 | |a Minced meat | ||
| 653 | |a Machine learning | ||
| 653 | |a Raman spectroscopy | ||
| 653 | |a Meat | ||
| 653 | |a Spectroscopy | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Accuracy | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Support vector machines | ||
| 653 | |a Lasers | ||
| 653 | |a Fraud | ||
| 653 | |a Meat industry | ||
| 653 | |a Mixtures | ||
| 653 | |a Meat quality | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Maggiolino Aristide |u Department of Veterinary Medicine, University of Bari Aldo Moro, Strada Provinciale per Casamassima Km 3, 70010 Valenzano, Italy; aristide.maggiolino@uniba.it | |
| 700 | 1 | |a Rocchetti Gabriele |u Department of Animal Science, Food and Nutrition, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy; gabriele.rocchetti@unicatt.it | |
| 700 | 1 | |a Sun Weizheng |u School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; fewzhsun@scut.edu.cn | |
| 700 | 1 | |a Heinz, Volker |u DIL German Institute of Food Technologies e.V., Prof.-von-Klitzing-Str. 7, 49610 Quakenbrück, Germany; v.heinz@dil-ev.de | |
| 700 | 1 | |a Tomasevic, Ivana D |u Institute of Meat Hygiene and Technology, Kaćanskog 13, 11000 Belgrade, Serbia; ivana.tomasevic@inmes.rs (I.D.T.); | |
| 700 | 1 | |a Djordjevic Vesna |u Institute of Meat Hygiene and Technology, Kaćanskog 13, 11000 Belgrade, Serbia; ivana.tomasevic@inmes.rs (I.D.T.); | |
| 700 | 1 | |a Tomasevic Igor |u Department of Animal Source Food Technology, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade, Serbia | |
| 773 | 0 | |t Foods |g vol. 14, no. 17 (2025), p. 3084-3099 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3249685110/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3249685110/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3249685110/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |