Effectiveness of Random Forest Classifier in Food Adulteration Detection

Abstract - Food adulteration has become one of the most pressing issues in public health, as it compromises food safety, nutrition, and consumer trust. Various real-time examples highlight how adulterants such as synthetic colors, chemical preservatives, and harmful substitutes are intentionally added to food products for profit, posing serious health risks. To address this challenge, advanced computational techniques, including Natural Language Processing (NLP) and Deep Learning, have been increasingly explored for the detection, prediction, and prevention of food adulteration. By leveraging machine learning models, datasets related to food quality and adulteration patterns can be analyzed to identify hidden correlations and risk indicators. The study further incorporates supervised learning algorithms and confusion matrix evaluation to measure the classification performance in predicting adulterated versus non-adulterated samples. The presented confusion matrix illustrates the predictive accuracy and misclassifications across multiple classes, providing a clear performance insight into the model. Overall, this research emphasizes how Artificial Intelligence (AI) and Machine Learning (ML) techniques can significantly strengthen food adulteration detection, thereby ensure public safety and contribute to a healthier society.

Food Adulteration, Machine Learning, Random Forest Classification, Predictive Analytics

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