Milk Adulteration Detection Through Statistical Analysis, Spectroscopy, & AI Integration Footsteps

Milk adulteration, particularly by adding water, is a common problem that impacts both the quality and safety of milk. This study focuses on detecting water adulteration in milk using Mid-Infrared (MIR) spectroscopy and AI-based analysis. A total of 100 milk samples were prepared, with 1 mL of water added to each sample. The MIR sensor was used to collect data by measuring how the milk absorbed light at various wavelengths. To ensure accuracy, all tests were conducted in a controlled environment. The collected data showed distinct patterns for each milk sample, which were then analyzed using statistical methods to detect water adulteration levels. The method was tested for accuracy, with promising results showing that it can reliably identify adulteration. This approach provides a quick and efficient way to detect milk adulteration, which could enhance quality control in the dairy industry. Additionally, the success rate of this technique was evaluated, proving the method's accuracy and effectiveness in identifying even small amounts of added water. The method is straightforward and easy to implement for practical use. Some Artificial Intelligence (AI) based methods are discussed which the next extension for the adulteration detection.