Abstract
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.
Highlights
The quality of food consumed plays a pivotal role in assuring the health of a society
RGB PHOTOGRAPHY The premise for the application of image analysis is that the changes in the oil properties are observable by the spectral properties of the oil sample
The true-colour image under RGB photography is given in Fig. 4a of the same oil batch for different reheat cycle count classes
Summary
The quality of food consumed plays a pivotal role in assuring the health of a society. Rapid screening of food and beverages, including edible oils, has become a key focus among scientists and industrialists because contamination and adulteration of food compromise the quality of food [1]–[3]. In RGB photography, recorded images are reconstructed by mixing available ground-truths for the three spectral regions in various proportions This technique has been used in a wide range of applications: detection of skin defects in citrus fruits [10], development of sorting and grading mechanisms [11], [12], and dietary assessment via food image analysis with deep learning [13]. Spectroscopy and SI techniques are superior to RGB photography at sens-
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
