ArticleBiotech (Basel (Switzerland))2025
Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification.
Article in Biotech (Basel (Switzerland)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Research Progress in Multi-Omics Analysis of Dairy Products: Nutritional Quality, Safety Evaluation, and Health Functions.Foods (Basel, Switzerland) · 2026Review
- Assessing Milk Authenticity Using Protein and Peptide Biomarkers: A Decade of Progress in Species Differentiation and Fraud Detection.Foods (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.