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.

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. Althoug

SUBMITTER: Bonah E 

PROVIDER: S-EPMC8357911 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature