Efficient machine learning models for the detection of coconut milk adulteration

Abstract

Coconut milk adulteration detection involves the use of analytical methods, including machine learning, to detect the presence of impurities or additives in coconut milk. Adulteration can occur when substances such as water or other cheaper ingredients are added to coconut milk, compromising its quality, nutritional value, and authenticity. Identifying adulteration is crucial for ensuring consumer safety, maintaining product quality, and upholding industry standards. The aim of this study was to propose a machine learning model to detect adulteration in coconut milk. To implement the proposed work, a coconut milk adulteration dataset is collected from a standard source. The amount of available data is limited; hence, synthetic data is generated by applying the CTGAN algorithm. The proposed framework employs three different Feature Extraction (FE) strategies, i.e., Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Autoencoder (AE). Then, the feature-extracted dataset is classified through four effective machine learning algorithms, such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The machine learning algorithms outcomes are evaluated using four performance metrics, i.e., Accuracy, Precision, Recall, and F1-score. RF provides the highest accuracy of 99.17%, precision value of 97.29%, recall value of 96.71%, and F1 Score of 97% for the LDA techniques.