Detection of Salmonella Typhimurium contamination levels in fresh pork samples using electronic nose smellprints in tand
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ORIGINAL ARTICLE
Detection of Salmonella Typhimurium contamination levels in fresh pork samples using electronic nose smellprints in tandem with support vector machine regression and metaheuristic optimization algorithms Ernest Bonah1,2 • Xingyi Huang1 • Yang Hongying1 • Joshua Harrington Aheto1 Ren Yi1,3 • Shanshan Yu1 • Hongyang Tu1
•
Revised: 29 August 2020 / Accepted: 8 October 2020 Ó Association of Food Scientists & Technologists (India) 2020
Abstract Rapid detection and quantification of bacterial foodborne pathogens are crucial in reducing the incidence of diseases associated with meat products contaminated with pathogens. For the identification, discrimination and quantification of Salmonella Typhimurium contamination in pork samples, a commercial electronic nose with ten (10) metal oxide semiconductor sensor array is applied. Principal component analysis was successfully applied for discrimination of inoculated samples and inoculated samples at different contaminant levels. Support vector machine regression (SVMR) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms were applied for S. Typhimurium quantification. Although SVMR results were satisfactory, SVMR hyperparameter tuning (c and g) by PSO, GA and GS showed superior performance of the models. The order of the prediction accuracy based on the prediction set was GASVMR (R2P = 0.989; RMSEP = 0.137; RPD = 14.93) [ PSO-SVMR (R2P = 0.986; RMSEP = 0.145; RPD = 14.11)
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13197-020-04847-y) contains supplementary material, which is available to authorized users. & Xingyi Huang [email protected] 1
School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, Jiangsu, People’s Republic of China
2
Laboratory Services Department, Food and Drugs Authority, P. O. Box CT 2783, Cantonments, Accra, Ghana
3
School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, XiYuan Road 279, Suzhou 215000, People’s Republic of China
[ GS-SVMR (R2P = 0.966; RMSEP = 0.148; RPD = 13.82) [ SVMR (R2P = 0.949; RMSEP = 0.162; RPD = 12.63). GA-SVMR’s proposed approach was fairly more effective and retained an excellent prediction accuracy. A clear relationship was identified between odor analysis results, and reference traditional microbial test, indicating that the electronic nose is useful for accurate microbial volatile organic compound evaluation in the quantification of S. Typhimurium in a food matrix. Keywords Salmonella Foodborne pathogens Electronic nose Chemometric algorithms Longissimus pork muscle Metaheuristic algorithms
Introduction Foodborne bacterial infections continue to be one of the world’s major causes of disease and death. Despite stringent inactivation control measures, such as pasteurization, and ultra-high temperature (UHT) treatment, numerous outbreaks of foodborne diseases have been reported due to the
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