Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification
Milk’s biological origin determination, including its adulteration and authenticity, presents serious limitations, highlighting the need for innovative advanced solutions. The utilisation of proteomic technologies combined with personalised algorithms creates great potential for a more comprehensive...
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MDPI AG
2025-04-01
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| author | Achilleas Karamoutsios Emmanouil D. Oikonomou Chrysoula (Chrysa) Voidarou Lampros Hatzizisis Konstantina Fotou Konstantina Nikolaou Evangelia Gouva Evangelia Gkiza Nikolaos Giannakeas Ioannis Skoufos Athina Tzora |
| author_facet | Achilleas Karamoutsios Emmanouil D. Oikonomou Chrysoula (Chrysa) Voidarou Lampros Hatzizisis Konstantina Fotou Konstantina Nikolaou Evangelia Gouva Evangelia Gkiza Nikolaos Giannakeas Ioannis Skoufos Athina Tzora |
| author_sort | Achilleas Karamoutsios |
| collection | DOAJ |
| description | Milk’s biological origin determination, including its adulteration and authenticity, presents serious limitations, highlighting the need for innovative advanced solutions. The utilisation of proteomic technologies combined with personalised algorithms creates great potential for a more comprehensive approach to analysing milk samples effectively. The current study presents an innovative approach utilising proteomics and neural networks to classify and distinguish bovine, ovine and caprine milk samples by employing advanced machine learning techniques; we developed a precise and reliable model capable of distinguishing the unique mass spectral signatures associated with each species. Our dataset includes a diverse range of mass spectra collected from milk samples after MALDI-TOF MS (Matrix-assisted laser desorption/ionization-time of flight mass spectrometry) analysis, which were used to train, validate, and test the neural network model. The results indicate a high level of accuracy in species identification, underscoring the model’s potential applications in dairy product authentication, quality assurance, and food safety. The current research offers a significant contribution to agricultural science, providing a cutting-edge method for species-specific classification through mass spectrometry. The dataset comprises 648, 1554, and 2392 spectra, represented by 16,018, 38,394, and 55,055 eight-dimensional vectors from bovine, caprine, and ovine milk, respectively. |
| format | Article |
| id | doaj-art-3549bdb3dc774e2e9f5c2c804eb597b7 |
| institution | Kabale University |
| issn | 2673-6284 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioTech |
| spelling | doaj-art-3549bdb3dc774e2e9f5c2c804eb597b72025-08-20T03:26:25ZengMDPI AGBioTech2673-62842025-04-011423310.3390/biotech14020033Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin IdentificationAchilleas Karamoutsios0Emmanouil D. Oikonomou1Chrysoula (Chrysa) Voidarou2Lampros Hatzizisis3Konstantina Fotou4Konstantina Nikolaou5Evangelia Gouva6Evangelia Gkiza7Nikolaos Giannakeas8Ioannis Skoufos9Athina Tzora10Laboratory of Animal Health, Hygiene and Food Quality, School of Agriculture, University of Ioannina, 47100 Arta, GreeceHuman Computer Interaction Laboratory, Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Health, Hygiene and Food Quality, School of Agriculture, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Science, Nutrition and Biotechnology, School of Agriculture, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Health, Hygiene and Food Quality, School of Agriculture, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Health, Hygiene and Food Quality, School of Agriculture, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Health, Hygiene and Food Quality, School of Agriculture, University of Ioannina, 47100 Arta, GreeceP.G. Nikas SA, Department of Regulatory Affairs & Quality Assurance, Agios Stefanos, 14565 Attica, GreeceHuman Computer Interaction Laboratory, Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Science, Nutrition and Biotechnology, School of Agriculture, University of Ioannina, 47100 Arta, GreeceLaboratory of Animal Health, Hygiene and Food Quality, School of Agriculture, University of Ioannina, 47100 Arta, GreeceMilk’s biological origin determination, including its adulteration and authenticity, presents serious limitations, highlighting the need for innovative advanced solutions. The utilisation of proteomic technologies combined with personalised algorithms creates great potential for a more comprehensive approach to analysing milk samples effectively. The current study presents an innovative approach utilising proteomics and neural networks to classify and distinguish bovine, ovine and caprine milk samples by employing advanced machine learning techniques; we developed a precise and reliable model capable of distinguishing the unique mass spectral signatures associated with each species. Our dataset includes a diverse range of mass spectra collected from milk samples after MALDI-TOF MS (Matrix-assisted laser desorption/ionization-time of flight mass spectrometry) analysis, which were used to train, validate, and test the neural network model. The results indicate a high level of accuracy in species identification, underscoring the model’s potential applications in dairy product authentication, quality assurance, and food safety. The current research offers a significant contribution to agricultural science, providing a cutting-edge method for species-specific classification through mass spectrometry. The dataset comprises 648, 1554, and 2392 spectra, represented by 16,018, 38,394, and 55,055 eight-dimensional vectors from bovine, caprine, and ovine milk, respectively.https://www.mdpi.com/2673-6284/14/2/33ruminant milkproteomicsmass spectrometryMALDI-TOF MSmachine learningneural networks |
| spellingShingle | Achilleas Karamoutsios Emmanouil D. Oikonomou Chrysoula (Chrysa) Voidarou Lampros Hatzizisis Konstantina Fotou Konstantina Nikolaou Evangelia Gouva Evangelia Gkiza Nikolaos Giannakeas Ioannis Skoufos Athina Tzora Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification BioTech ruminant milk proteomics mass spectrometry MALDI-TOF MS machine learning neural networks |
| title | Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification |
| title_full | Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification |
| title_fullStr | Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification |
| title_full_unstemmed | Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification |
| title_short | Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification |
| title_sort | innovations in proteomic technologies and artificial neural networks unlocking milk origin identification |
| topic | ruminant milk proteomics mass spectrometry MALDI-TOF MS machine learning neural networks |
| url | https://www.mdpi.com/2673-6284/14/2/33 |
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