Abstract
In the pursuit of enhancing food safety protocols, this article explores a cutting-edge approach to quality control in the coconut oil industry. We present a multispectral image processing system designed specifically for the detection of reheated coconut oil, leveraging advancements in machine learning. Machine learning algorithms, fused with image classification techniques, provide a robust framework for accurately identifying reheated coconut oil. It is proposed to develop a spectral clustering-based classifier to determine the effect of reheating and reuse of coconut oil. Post-processing methods refine classification results, while validation ensures the system's adaptability to diverse datasets.
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