This study used the chemometric analysis of colloid profiles to classify milk. Colloidal nanosystems were separated by Field Flow Fractionation (FFF) working in saline carrier. Rather than downstream detailed analysis of their composition, the FFF signals were measured directly. There was minimal preprocessing . A set of 47 bovine milk samples was analyzed: a single analysis yielded a characteristic multidimensional colloidal dataset, that once processed with multivariate tools allowed simultaneously four different discriminations: fat content, thermal treatment, brand and manufacturing plant. The work represents the first attempt to identify milk sub-typologies based on colloidal profiles, and the most complete study concerning multivariate analysis of FFF fingerprint. The authors recommend this as a sustainable technique, with limited pretreatment, non-toxic chemicals, and high throughput results. They conclude that the analytical methodology is fast, green, simple, and inexpensive and could offer great help in the field of quality control and fraud identification.
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