Abstract
This study presents a proposition of a low-cost screening method for detection and quantification of adulterations in liquid cow’s milk samples. The studied adulterations were made with water and NaOH. Digital images from the adulterated samples were obtained using a flatbed scanner, and the means of ten color parameters were used to evaluate the information from images: red, green, blue, hue, saturation, value, relative colors (r, g, and b), and intensity. Regression models for water quantification were proposed using multiple linear regression (MLR), principal components regression (PCR), and partial least squares (PLS). The best models were obtained using PCR and PLS, with root mean square error for prediction smaller than 7%. These results were compared with near-infrared (NIR), and the prediction capability was very similar. In the case of adulterations with NaOH, the colors B, S, g, and b presented the highest differences between fresh and adulterated milk samples.
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