A novel approach for identifying melamine adulteration in powdered milk with E-nose and AI.

Dairy products are exceptionally nutritious and a key component of our diet. They are utilized in a wide range of food industries due to their significance. However, being highly popular and valuable, they are among the most common products subject to adulteration. Melamine is a chemical compound deliberately added to various food products. It is used to artificially increase the apparent protein content in milk, milk powder, pet food, and other foods. In this study, pure milk powder along with 12 different types of adulterated samples (with melamine content of 1, 2, 3, 4, and 5 ppm) in both dry and wet forms was analyzed using an electronic nose equipped with 8 metal oxide sensors to detect adulteration patterns. Principal Component Analysis (PCA), discriminant analysis (DA), and Support Vector Machine (SVM) methods were employed to analyze sensor response patterns and classification. Quantitative Descriptive Analysis (QDA) yielded a precision of 99.5 %, while Multi-Discriminant Analysis (MDA) achieved a precision of 98.5 %. Therefore, it appears that electronic nose technology with metal oxide sensors, along with chemometric methods, can be a truly effective tool for the rapid detection and classification of pure milk powder from adulterated materials.