ArticleNPJ science of food2022
Identification of milk from different animal and plant sources by desorption electrospray ionisation high-resolution mass spectrometry (DESI-MS).
Article in NPJ science of food, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Rapid Authentication of Plant-Based Milk Alternatives by Coupling Portable Raman Spectroscopy With Machine Learning.Journal of AOAC International · 2026Article
- A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods.Molecules (Basel, Switzerland) · 2025Review
- A critical review of conventional and emerging technologies for the detection of contaminants, allergens and adulterants in plant-based milk alternatives.Current research in food science · 2025Review
- [Identifying animal-derived components in camel milk and its products by ultra-high performance liquid chromatography-tandem mass spectrometry].Se pu = Chinese journal of chromatography · 2024Article
- Rapid analysis technologies with chemometrics for food authenticity field: A review.Current research in food science · 2024Review
- Applications of ambient ionization mass spectrometry in 2022: An annual review.Analytical science advances · 2023Review
- Review
- Rapid, Sensitive, and Versatile: A 5-Year Perspective on Applications of Ambient Ionization Mass Spectrometry in Bioanalytical Science.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study used desorption electrospray ionisation mass spectrometry (DESI-MS) to analyse and detect and classify biomarkers in five different animal and plant sources of milk for the first time. A range of differences in terms of features was observed in the spectra of cow milk, goat milk, camel milk, soya milk, and oat milk. Chemometric modelling was then used to classify the mass spectra data, enabling unique or significant markers for each milk source to be identified. The classification of different milk sources was achieved with a cross-validation percentage rate of 100% through linear discriminate analysis (LDA) with high sensitivity to adulteration (0.1-5% v/v). The DESI-MS results from the milk samples analysed show the methodology to have high classification accuracy, and in the absence of complex sample clean-up which is often associated with authenticity testing, to be a rapid and efficient approach for milk fraud control.
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Registered trials
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