Identifying Adulteration in Food Product Using Fumigation Method and Machine Learning

Food safety and quality depend on being able to spot food that has been tampered with. Combining spraying methods with machine learning methods is a new way that this study suggests to find additives in food items. Chemical research is often used in traditional ways, but it can take a long time and cost a lot of money. Fumigation, on the other hand, is a quick and safe way to get toxic chemicals out of food samples. During the process, the food sample is put in a controlled fumigation setting where flammable substances are released and gathered. Then, gas chromatography combined with mass spectrometry (GC-MS) is used to look at the volatile chemicals that have been collected. In this step, a detailed chemical record of the food sample is made. To make the process of finding adulterants faster and more accurate, machine learning methods are used to look at the GC-MS data. Support vector machines (SVM) or neural networks are two types of supervised learning models that are taught using named data sets of known food samples that have been tampered with and those that have not. These models learn to tell the difference between different types of adulteration by looking for trends in the chemical profiles of the products. The suggested method is better than current methods in a number of ways. It lets a lot of different food items be quickly checked for possible tampering without having to do a lot of work on the samples. By using machine learning, the method makes identification more sensitive and can adapt to new kinds of adulterants that haven't been seen before.