Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization

The effect of frying mechanism on cooking oil produces toxic compounds which destroy the essential vitamins in the oil and increase the risks of various diseases. The inconvenient and inconsistency of evaluating techniques at hand are attributing factor of the diseases risk. The inuence of the nume...

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Main Author:	Jalo, Abdulmumini Salihu
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Mathematical optimization Particles (Nuclear physics) Swarm intelligence
Online Access:	http://psasir.upm.edu.my/id/eprint/70518/1/FK%202016%2094%20-%20IR.pdf
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id	my-upm-ir.70518
record_format	uketd_dc
spelling	my-upm-ir.705182019-08-21T06:26:36Z Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization 2016-01 Jalo, Abdulmumini Salihu The effect of frying mechanism on cooking oil produces toxic compounds which destroy the essential vitamins in the oil and increase the risks of various diseases. The inconvenient and inconsistency of evaluating techniques at hand are attributing factor of the diseases risk. The inuence of the numerous variable that accelerates the properties deterioration during the frying process made it diffcult to evaluate the oil quality. The aim of this work was to provide a safe and automatic means of evaluating the used oil quality. Through assessing theefect of frying mechanism on the oil properties at a spot and their significance. To established the interrelationship between the oil properties and to find the optimum effect of the frying mechanism on the oil properties. The effect of the frying process on the oil properties is mainly inuenced by frying temperature and time (cycles) as revealed in literature. The above objective is achieved using statistical tools namely, correlation, analysis of variance (ANOVA), and regression in conjunction with computational intelligent; particle swarm optimization (PSO) and genetic algorithm (GA). The effect of frying mechanism on the total polar compound, free fatty acid, pH value, viscosity, and the red color of the oil was studied, through frying of chicken meat (1±0.05kg) at regulated temperatures of (100, 150, and 200)±10oC. The statistical analysis reveals the correlation between the oil properties were significant at p<0.05 and the relationship straightened with an increase in frying temperature except for the pH value and red color which decline with an increase in frying temperature. The analysis of variance portrays TPC, FFA, pH value, viscosity and red color were statistically significant at p<0.05 with an increase in frying cycle and straighten to p<0.01 at 200oC frying temperature. The regression analysis reveals that the pH value, FFA, and red color are statistically significance at p<0.01 then viscosity at p<0.05 and TPC had not been significant at p>0.05. The optimization finding showed free fatty acid and red color had the better performance fitness of 0.0001 and 0.0019 respectively while viscosity had the worst fitness of 0.0588. The compared results between PSO and GA algorithm shows negligible differences between their results in all the properties. Therefore, the outcome of the study portrayed the effect frying mechanism deteriorate the oil quality, causes changes in the oil properties, and it enhances the relationship between the oil properties. The studied further reveals both PSO and GA are potential tools for evaluating the quality of the cooking oil. Mathematical optimization Particles (Nuclear physics) Swarm intelligence 2016-01 Thesis http://psasir.upm.edu.my/id/eprint/70518/ http://psasir.upm.edu.my/id/eprint/70518/1/FK%202016%2094%20-%20IR.pdf text en public masters Universiti Putra Malaysia Mathematical optimization Particles (Nuclear physics) Swarm intelligence
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Mathematical optimization Particles (Nuclear physics) Swarm intelligence
spellingShingle	Mathematical optimization Particles (Nuclear physics) Swarm intelligence Jalo, Abdulmumini Salihu Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
description	The effect of frying mechanism on cooking oil produces toxic compounds which destroy the essential vitamins in the oil and increase the risks of various diseases. The inconvenient and inconsistency of evaluating techniques at hand are attributing factor of the diseases risk. The inuence of the numerous variable that accelerates the properties deterioration during the frying process made it diffcult to evaluate the oil quality. The aim of this work was to provide a safe and automatic means of evaluating the used oil quality. Through assessing theefect of frying mechanism on the oil properties at a spot and their significance. To established the interrelationship between the oil properties and to find the optimum effect of the frying mechanism on the oil properties. The effect of the frying process on the oil properties is mainly inuenced by frying temperature and time (cycles) as revealed in literature. The above objective is achieved using statistical tools namely, correlation, analysis of variance (ANOVA), and regression in conjunction with computational intelligent; particle swarm optimization (PSO) and genetic algorithm (GA). The effect of frying mechanism on the total polar compound, free fatty acid, pH value, viscosity, and the red color of the oil was studied, through frying of chicken meat (1±0.05kg) at regulated temperatures of (100, 150, and 200)±10oC. The statistical analysis reveals the correlation between the oil properties were significant at p<0.05 and the relationship straightened with an increase in frying temperature except for the pH value and red color which decline with an increase in frying temperature. The analysis of variance portrays TPC, FFA, pH value, viscosity and red color were statistically significant at p<0.05 with an increase in frying cycle and straighten to p<0.01 at 200oC frying temperature. The regression analysis reveals that the pH value, FFA, and red color are statistically significance at p<0.01 then viscosity at p<0.05 and TPC had not been significant at p>0.05. The optimization finding showed free fatty acid and red color had the better performance fitness of 0.0001 and 0.0019 respectively while viscosity had the worst fitness of 0.0588. The compared results between PSO and GA algorithm shows negligible differences between their results in all the properties. Therefore, the outcome of the study portrayed the effect frying mechanism deteriorate the oil quality, causes changes in the oil properties, and it enhances the relationship between the oil properties. The studied further reveals both PSO and GA are potential tools for evaluating the quality of the cooking oil.
format	Thesis
qualification_level	Master's degree
author	Jalo, Abdulmumini Salihu
author_facet	Jalo, Abdulmumini Salihu
author_sort	Jalo, Abdulmumini Salihu
title	Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
title_short	Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
title_full	Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
title_fullStr	Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
title_full_unstemmed	Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
title_sort	optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/70518/1/FK%202016%2094%20-%20IR.pdf
_version_	1747812857258442752

Optimizing physicochemical properties of recycled cooking palm oil using particle swarm optimization

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