Abstract
Milk adulteration is a common phenomenon in many countries, which draws extensive attention from humans due to health hazards that might result in some fatal diseases. In this study, a portable near-infrared (NIR) spectrometer combined with multivariate analysis was used to detect and quantify milk adulteration. Fresh cow milk samples were collected from eight dairy farms in Beijing and Hebei province of China. Water, urea, starch and goat milk were used to adulterate milk at 11 different concentrations. The data driven soft independent modeling of class analogy (DD-SIMCA) method was employed for qualitative analysis. Partial least squares regression (PLSR) was applied for statistical analysis of the obtained NIR spectral data. The results showed that the DD-SIMCA approach achieved satisfactory classification. By the PLSR model, standard error of prediction (SEP) values of 4.35, 0.34, 4.74 and 5.56 g/L were obtained for water, urea, starch and goat milk, respectively. These results demonstrated the feasibility and reliability of NIR spectroscopy combined with multivariate analysis in the prediction of the total contents of the investigated adulterants in cow milk. Key words: Portable near-infrared (NIR)-spectroscopy, milk adulteration, DD-SIMCA, Partial least squares (PLS) regression.
Highlights
Milk adulteration is usually conducted to meet the regulatory requirements while lowering the milk quality by substitution of cheap substances, admixture or extraction of valuable milk components (Poonia et al, 2017)
Samples adulterated with urea demonstrated strong absorption at 1063 nm associated with NH4+ deformation, indicating the decomposition of urea, while C═O was absorbed at 1255 nm (Mortland, 1996)
The DD-soft independent modeling of class analogy (SIMCA) method was applied in this study to distinguish between natural and adulterated milk samples
Summary
Milk adulteration is usually conducted to meet the regulatory requirements while lowering the milk quality by substitution of cheap substances, admixture or extraction of valuable milk components (Poonia et al, 2017). It poses serious threats to human health and becomes a global concern, in developing countries. The possible reasons for milk adulteration might be a high demand of milk by all ages, easy adulteration operations and lack of feasible and accurate detection tools (Kamthania et al, 2014). As a nutritionally balanced mixture and perishable food, milk has attracted the interest of many researchers.
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