Hong Yunhe, Birse Nicholas, Quinn Brian, Montgomery Holly, Wu Di, Rosas da Silva Gonçalo, van Ruth Saskia M, Elliott Christopher T
ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, UK.
Food Quality and Design Group, Wageningen University and Research, western, the Netherlands.
NPJ Sci Food. 2022 Feb 11;6(1):14. doi: 10.1038/s41538-022-00129-3.
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.
本研究首次使用解吸电喷雾电离质谱法（DESI-MS）对五种不同动植物源的牛奶中的生物标志物进行分析、检测和分类。在牛奶、羊奶、骆驼奶、豆奶和燕麦奶的光谱中观察到一系列特征差异。然后使用化学计量学建模对质谱数据进行分类，从而能够识别出每种奶源独特的或显著的标志物。通过线性判别分析（LDA），不同奶源的分类交叉验证百分率达到100%，对掺假具有高灵敏度（0.1-5% v/v）。对牛奶样品的DESI-MS分析结果表明，该方法具有很高的分类准确性，且无需通常与真实性检测相关的复杂样品净化过程，是一种快速有效的控制牛奶掺假的方法。
