| Title: | 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 |
| Author(s): | Bonah E; Huang X; Hongying Y; Aheto JH; Yi R; Yu S; Tu H; |
| Address: | "School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People's Republic of China. GRID: grid.440785.a. ISNI: 0000 0001 0743 511X Laboratory Services Department, Food and Drugs Authority, P. O. Box CT 2783, Cantonments, Accra, Ghana. School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, XiYuan Road 279, Suzhou, 215000 People's Republic of China. GRID: grid.495872.5. ISNI: 0000 0004 1762 707X" |
| DOI: | 10.1007/s13197-020-04847-y |
| ISSN/ISBN: | 0022-1155 (Print) 0975-8402 (Electronic) 0022-1155 (Linking) |
| 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 GA-SVMR (R (P) (2) = 0.989; RMSE(P) = 0.137; RPD = 14.93) > PSO-SVMR (R (P) (2) = 0.986; RMSE(P) = 0.145; RPD = 14.11) > GS-SVMR (R (P) (2) = 0.966; RMSE(P) = 0.148; RPD = 13.82) > SVMR (R (P) (2) = 0.949; RMSE(P) = 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: | Chemometric algorithms Electronic nose Foodborne pathogens Longissimus pork muscle Metaheuristic algorithms Salmonella; |
| Notes: | "PubMed-not-MEDLINEBonah, Ernest Huang, Xingyi Hongying, Yang Aheto, Joshua Harrington Yi, Ren Yu, Shanshan Tu, Hongyang eng India 2021/09/03 J Food Sci Technol. 2021 Oct; 58(10):3861-3870. doi: 10.1007/s13197-020-04847-y. Epub 2020 Oct 16" |
