Ensemble Machine Learning Approaches for Reliable Palm Oil Adulteration Detection

Palm oil adulteration poses a major challenge to food quality, safety, and public health. Conventional analytical techniques, including chemical and spectroscopic methods, though reliable, are often resource-intensive and time-consuming. This work investigates the application of machine learning algorithms for predicting adulteration in palm oil using its physicochemical characteristics. K-Nearest Neighbors (KNN), Random Forest, Gradient Boosting, AdaBoost, Bagging, XGBoost, Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Logistic Regression were evaluated for the purpose of detecting palm oil adulteration. Their performances were evaluated using accuracy, precision, recall, and F1-score. Results indicate that ensemble-based methods, particularly Random Forest, Gradient Boosting, and AdaBoost, achieved superior outcomes with a maximum accuracy of 94.6% and consistently strong precision and recall values. These findings demonstrate that machine learning can provide a rapid, reliable, and cost-efficient alternative for detecting adulteration in palm oil.