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<p>Goat milk is a nutrient-rich food and due to its higher commercial value, it is not uncommon for it to be adulterated by the addition of water and cow milk. In this work, ternary mixtures of goat milk, cow milk and water were analyzed by FT-NIR(S), PLS and Logistic Regression. Exploratory investigation showed three ranges of spectral values most strongly associated with separation between the mixtures analyzed, 10800 to 9800, 8800 to 7200 and 6400 to 5600&#xa0;cm<sup>−1</sup>. The PLS models showed good predictive capacity to quantify the percentages of water, goat milk and cow milk in the ternary mixtures over a wide range of values. Logistical Regression was also good at classifying samples into two adulteration groups. FT-NIR spectroscopy, together with multivariate techniques, proved to be very promising as an analytical methodology for the rapid identification of adulteration and economic fraud in goat milk.</p>

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Detection and quantification of water adulteration in goat milk using FT-NIR spectroscopy combined with partial least squares and logistic regression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月25日

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  • Paula Giarolla Silveira,
  • Clara Mariana Gonçalves Lima,
  • Dariush Khademi Shurmasti,
  • Carlos Ramon de Paula Silva,
  • José Luis Contado,
  • Virgílio de Carvalho dos Anjos,
  • Maria José Valenzuela Bell,
  • Silvani Verruck,
  • Roney Alves da Rocha

摘要

Goat milk is a nutrient-rich food and due to its higher commercial value, it is not uncommon for it to be adulterated by the addition of water and cow milk. In this work, ternary mixtures of goat milk, cow milk and water were analyzed by FT-NIR(S), PLS and Logistic Regression. Exploratory investigation showed three ranges of spectral values most strongly associated with separation between the mixtures analyzed, 10800 to 9800, 8800 to 7200 and 6400 to 5600 cm−1. The PLS models showed good predictive capacity to quantify the percentages of water, goat milk and cow milk in the ternary mixtures over a wide range of values. Logistical Regression was also good at classifying samples into two adulteration groups. FT-NIR spectroscopy, together with multivariate techniques, proved to be very promising as an analytical methodology for the rapid identification of adulteration and economic fraud in goat milk.