Detection of contamination levels in fresh pork samples using electronic nose smellprints in tandem with support vector machine regression and metaheuristic optimization algorithms.

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 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. 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 GA-SVMR (  = 0.989; RMSE = 0.137; RPD = 14.93) > PSO-SVMR (  = 0.986; RMSE = 0.145; RPD = 14.11) > GS-SVMR (  = 0.966; RMSE = 0.148; RPD = 13.82) > SVMR (  = 0.949; RMSE = 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. in a food matrix.