Waste cooking oil classification using artificial intelligence technology / Lau Kar Sin - Lau, Kar Sin (2020)
Palm oil – one of the most common edible oil consumed in Malaysia. It is because Malaysia is one of the countries which supply palm oil to the global market and it is cheap to obtain for the consumer in Malaysia. Most of the Malaysian consume it via food preparation such as deep-frying and cooking. However, due to widely available for Malaysians, consumers also lacking awareness in dealing after using the edible oil. Most of the household consumers discard excess waste cooking oil (WCO) into sewage and with courtesy, some of them stored them in containers and sell to NGOs. Fortunately, artisan soap making is getting on-trend in the Malaysian market plus more people are keen to do business online and thus involve more in Do-It-Yourself (DIY) soap making for small businesses. This is an opportunity to promote the WCO to be reused in terms of soap making. Although there is a minority industry taking part in dealing this WCO recycle and reuse, but for this study is to promote all domestic artisan soap makers to realize using vegetarian used WCO is as good as using fresh palm oil. So, to distinguish between WCO into vegetarian used and non-vegetarian used, a simple Artificial Intelligence (A.I.) system is developed to aid them in distinguish WCO. To develop an A.I. system, a few crucial parameters are chosen after performing literature reviews - total iron content and peroxide value (PV). After getting samples and performed characterization on those samples, total iron content does accumulate in the WCO when the WCO is deep-fried with meat products that contain iron in haemoglobin. While PV does increases when the WCO is stored in a container for a long time. For this study, the hypothesis of vegetarians used WCO should be higher PV due to the lacking of iron in the WCO which catalyses the decomposition of hydroperoxide to alkyl radicals by oxidation-reduction mechanism is not applicable. This is due to the WCO's stored life iv span factor overshadow it. Lastly, for A.I. development, 2 simple hypothesis sets - Perceptron and Multi-layered Perceptron with Back Propagation (MLP-BP) are chosen to compare the accuracy of each model. The reason for choosing simple models is because of limited data points (10 points). Programming on these 2 models via MATLAB software. Validation on both hypothesis sets is performed using cross-validation, "Leave One Out" method and minimal Eout is chosen. After performing the development, Perceptron has minimal Eout, 0% while MLP-BP has 3%. This is because of Perceptron is the simplest model and minimal overfitting error which can cause deterministic noise on the result. Hence, to improve this study, more data points are recommended so can develop a more robust A.I. system to tackle more complicated situations for the WCO.