Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis

• Published in: Food analytical methods Vol. 11; no. 7; pp. 1994 - 2003
• Main Authors: Hansen, Lucas, Ferrão, Marco Flôres
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2018; Springer Nature B.V
• Subjects: Acidity; Analytical Chemistry; Animal products; Chemical composition; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Data processing; Dry matter; Economics; Food quality; Food Science; Freezing; Freezing point; Inspection; Melting points; Mental depression; Microbiology; Milk; Multivariate analysis; Principal components analysis; Profitability; Sugar; SVM-DA; SIMCA; PLS-DA; kNN; Multivariate analysis; Adulteration; Milk; PCA
• ISSN: 1936-9751; 1936-976X
• DOI: 10.1007/s12161-018-1181-6

Milk is one the most widely consumed foods in the world, with an average annual production of 723 million tons in the second half of the past decade. However, to increase milk’s profitability, some actors in the dairy chain may adulterate it, altering its chemical composition, and reducing the nutritional value of this food. The quality of milk is therefore assessed through chemical and physical analyses such as total dry matter, total Kjeldahl nitrogen, ash, acidity, fat, reducing sugars, depression of freezing point, and relative density. In this work, we have used Principal Components Analysis (PCA) and supervised methods (PLS-DA, SIMCA, kNN, and SVM-DA) to explore physicochemical data from milk and to classify and discriminate between samples that were compliant or not to the parameters set in the Brazilian Regulation for the Inspection of Animal Products and other national regulations. Classification results regarding specificity and selectivity for PLS-DA, SIMCA, kNN, and SVM-DA for noncompliant samples in the test group were, respectively, the following: 91, 88; 100, 97; 78, 67; 78, and 71%.