RECENT ADVANCES IN MILK ADULTERATION DETECTION USING SPECTROSCOPY TECHNIQUES AND MACHINE LEARNING-A REVIEW

Abstract

Across the globe, milk adulteration is a serious problem for consumers health and food quality. Common adulterants include water, starch urea, flour, and detergents. Traditional methods of detection are labor-intensive, time consuming, and inconvenience in faster assessment and field testing. This paper suggests a rapid, non-destructive, and accurate process of the detection of milk adulterants through spectroscopic procedures and machine learning (ML) models. The Spectral data obtained through the methodology of near-infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy were preprocessed and subjected to analysis to establish the patterns of adulteration. Adulterated or pure milk samples were categorized with the help of ML models like Random Forests (RF), k-Nearest Neighbors (k-NN) and Support Vector machines (SVM). These methods enhance the detection techniques and reduce sample preparation involved in large volumes. The combination of ML and Spectroscopic methods has the potential to enhance the sensitivity and accuracy of signature based adulteration detection using spectral data analyses. The literature is structured by classifying adulterants into intentional and unintentional categories, dividing reported health effects, and assessing the advantages and disadvantages of each analytical method. This work illustrates the significance of integrating spectroscopy and machine learning for the assessment of milk quality by putting the reviewed methodologies within the broader research structure. The review describes important research gaps and future directions, such as multi-spectral data fusion, bigger and more diverse datasets, and portable systems for real-time use. This review provides a robust literature-based foundation and underscores the imperative for continued research into reliable and commercially viable milk adulteration detection systems.