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Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis

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Abstract

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%.

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Authors and Affiliations

  1. Instituto de Química – Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, Bairro Agronomia, Porto Alegre, RS, CEP 91501-970, Brazil

    Lucas Hansen & Marco Flôres Ferrão

  2. Laboratório Nacional Agropecuário – RS, Estr. Ponta Grossa 3036, Ponta Grossa, Porto Alegre, RS, CEP 91780-580, Brazil

    Lucas Hansen

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  1. Lucas Hansen
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Hansen, L., Ferrão, M.F. Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis. Food Anal. Methods 11, 1994–2003 (2018). https://doi.org/10.1007/s12161-018-1181-6

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