Journal
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
Volume 109, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jfca.2022.104515
Keywords
Whey; Tryptophan; Multivariate analysis; Authenticity; Adulteration; Screening tool
Categories
Funding
- Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ) Brazil [E-26/203.049/2017, E-26/200.721/2021, E-26/200.358/2021, E-26/200.691/2021]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [313119/2020-1, 163480/2020-6]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) Brazil [88887.518753/2020-00, 001]
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Fluorescence spectroscopy coupled with chemometric tools is used to detect milk authenticity and prevent fraudulent adulterations. Different levels of whey adulteration in milk lead to changes in fluorescence spectra, with supervised methods showing more efficient performance in distinguishing between genuine and adulterated milk.
Fluorescence spectroscopy with chemometric tools was used to check the milk authentication against the fraudulent addition of whey. Different commercial kinds of milk were adulterated with whey at five different levels. The fluorescence spectra of the adulterated samples showed different emission profiles when excited at the wavelength associated with tryptophan residues (295 nm). The addition of whey caused a reduction in fluorescence intensity. Nevertheless, visual inspection was not enough to discriminate between adulterations or milk types. Initial principal component analysis (PCA) explained 98.21% of the variance, PC1 (96.23%) enabled discrimination between genuine and adulterated milk, and PC2 (1.98%) discriminated by fat content in milk. However, supervised methods allowed for more efficient performances. Among the tested techniques, partial least squares-discriminant analysis (PLS-DA) allowed better classification than most evaluated approaches to soft independent modeling of class analogy (SIMCA). Overall, PLS-DA also showed better accuracy than SIMCA for classifying genuine and adulterated milk, with 100% predictive accuracy, sensitivity and specificity for whole and skim milk. Despite demonstrating suitable classification (>80%) of genuine and adulterated milk, it was not possible to classify the samples according to the level of adulteration.
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