Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

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.

A comprehensive review on applications of artificial intelligence and spectroscopy for coconut oil

“…The efficacy of support vector regression in CPO price forecasting is emphasized, and it is suggested that by employing feature selection techniques and adding more pertinent attributes, future research can improve accuracy. Zhao, 2022 [10] The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration 19 Rapid detection of edible oils type and adulteration.…”

The Impact of Satisfaction, Trust, and Destination Image on Tourist Loyalty to Tourism Destinations and Hotels in the Covid-19 Era: Applied on Hurghada, Luxor and Aswan

“…The Covid-19 pandemic has caused an unparalleled crisis in the tourism industry economy (Gerszon et al, 2020). Due to the negative impact of the corona virus, some repercussions were recorded, most notably: biggest drop ever in the numbers of the employees working in tourism and hospitality sector (workforce reduction), Massive job cuts and pay cuts, encouragement to from home, reservations have been cancelled, giving employees mandatory leave, making employees to perform extra works ‫والفنادق‬ ‫السياحة‬ ‫كلية‬ ‫جملة‬ -‫عدد‬ 41 -‫ديسمرب‬ 0202 (Ranasinghe et al, 2021). The corona virus has caused severe economic damage in various countries of the world, which requires governments to act immediately to support people and businesses.…”