AfroPALM - Afrocentric palm oil adulteration learning models: An end-to-end deep learning approach for detection of palm oil adulteration

Abstract

In Africa, rapid population growth and a focus on increasing food production have often overlooked the crucial aspect of food safety, leading to the highest per capita incidences of foodborne illness globally. This underscores the imperative to address the food safety challenges on the continent. A critical concern is the widespread adulteration of red palm oil across West Africa, often involving harmful red azo dyes, posing significant health risks. Current detection methods, reliant on laboratory procedures, are not only time-consuming and expensive but also impractical for broad implementation in local markets. To address these limitations, we propose an end-to-end deep learning approach that bypasses the need for manual feature extraction and laboratory-based analyses. Utilizing high-resolution imaging technology, our approach can objectively and reliably detect palm oil adulteration directly from raw image data. In our study, we developed a deep convolutional neural network dubbed AfroPALM-Custom, specifically designed for detecting palm oil adulteration in African markets. AfroPALM-Custom was pivotal in refining our deep learning approach, achieving a test accuracy of 90.63% and an F1 score of 90.98%. We later adapted mobile-efficient pretrained models, namely SqueezeNet1.1 and GhostNetV1—mobile—fine-tuned and as well dubbed AfroPALM-GhostNet and AfroPALM-SqueezeNet. In performance, AfroPALM-GhostNet and AfroPALM-SqueezeNet achieved test accuracies of 96.29% and 91.16%, respectively, and F1-scores of 96.57% and 91.95%. This proficiency in identifying adulterated palm oil demonstrates a significant advancement in food safety solutions for African markets, offering a practical and scalable approach for implementation within local marketplaces where resources are limited and laboratory methods are impractical.

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