Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory system: A novel method

Abstract

One of the common concerns in quality assurance of raw milk is the use of antimicrobial agents for reducing the microbial population. For this purpose, different kinds of agents may be added to raw milk like detergents. This illegal practice is harmful to human health and has ethical and serious sanitary consequences. In this study, an artificial olfactory machine (electronic nose) was developed based on eight metal oxide semiconductor sensors (MOS) and its ability to detect the presence of detergent powder in raw milk was investigated. Three features (area under the curve, relative response, and slope) were extracted from each sensor response and three baseline manipulation techniques (differential, relative and fractional) were used to correct the sensor responses. The multivariate analysis of variance (MANOVA) was employed to optimize the data matrix. MANOVA showed that the feature of “area under the curve” along with differential baseline correction method is the best combination for distinguishing different levels of the adulteration in milk. Based on the results, principal component analysis (PCA) with the first two PCs explains 91% of the variations. Linear discriminant analysis (LDA), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) method were employed for further qualitative classification. The result showed that the best performance (90%) was achieved by using the nu-SVM with Radial Basis Function (RBF) kernel function when the data collected from independent experiments were used for validation. The study demonstrated the potential of an electronic nose as a fast, effective and feasible method to detect detergent powder in raw milk.