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Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p < 0.05). Accordingly, the proposed work closes the gap for using multispectral imaging for oil reheating and proposes a novel algorithm for unsupervised detection of critical property changes in the oil. Hence, the proposed research work is significant in its practical implications, contribution to food image analysis, and unsupervised classification mechanisms.

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