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. 2021 Oct;58(10):3861-3870.
doi: 10.1007/s13197-020-04847-y. Epub 2020 Oct 16.

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 Bonah  1   2 , Xingyi Huang  1 , Yang Hongying  1 , Joshua Harrington Aheto  1 , Ren Yi  1   3 , Shanshan Yu  1 , Hongyang Tu  1
Affiliations

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 Bonah et al. J Food Sci Technol. 2021 Oct.

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; RMSEP = 0.137; RPD = 14.93) > PSO-SVMR (R P 2 = 0.986; RMSEP = 0.145; RPD = 14.11) > GS-SVMR (R P 2 = 0.966; RMSEP = 0.148; RPD = 13.82) > SVMR (R P 2 = 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: Chemometric algorithms; Electronic nose; Foodborne pathogens; Longissimus pork muscle; Metaheuristic algorithms; Salmonella.

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Conflict of interest statement

Conflict of interestAuthors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Radar plot of sensor responses S. Typhimurium inoculated pork samples
Fig. 2
Fig. 2
Fingerprint comparison of volatile organic compounds (VOCs) by different concentrations of S. Typhimurium inoculated pork samples
Fig. 3
Fig. 3
Principal Component Analysis (PCA) of final classification model based on data fusion a 2logCFU/ml, b 4logCFU/ml c 7logCFU/ml
Fig. 4
Fig. 4
Graph for optimizing SVM parameters by genetic algorithm, particle swarm algorithm and Grid search
See this image and copyright information in PMC

References

    1. Aheto JH, Huang X, Tian X, Ren Y, Ernest B, Alenyorege EA, Dai C, Hongyang T, Xiaorui Z, Wang P. Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat. Anal Bioanal Chem. 2020;412:1169–1179. doi: 10.1007/s00216-019-02345-5. - DOI - PubMed
    1. Andrew AM (2001) An introduction to support vector machines and other kernel-based learning methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii + 189 pp., ISBN 0-521-78019-5.Robotica 18:687-689. 10.1017/s0263574700232827
    1. Audrain B, Farag MA, Ryu C-M, Ghigo J-M. Role of bacterial volatile compounds in bacterial biology. FEMS Microbiol Rev. 2015;39:222–233. doi: 10.1093/femsre/fuu013. - DOI - PubMed
    1. Balbin JR, Sese JT, Babaan CVR, Poblete DMM, Panganiban RP, Poblete JG (2017) Detection and classification of bacteria in common street foods using electronic nose and support vector machine. In: 2017 7th IEEE international conference on control system, computing and engineering (ICCSCE), pp 247–252. 10.1109/iccsce.2017.8284413
    1. Bao Y, Hu Z, Xiong T. A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing. 2013;117:98–106. doi: 10.1016/j.neucom.2013.01.027. - DOI

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