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-Reference-Cited by-同舟云学术

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

Published:2020-10-16 Issue:10 Volume:58 Page:3861-3870
ISSN:0022-1155
Container-title:Journal of Food Science and Technology
language:en
Short-container-title:J Food Sci Technol

Author:

Bonah Ernest,Huang Xingyi^ORCID,Hongying Yang,Aheto Joshua Harrington,Yi Ren,Yu Shanshan,Tu Hongyang

Funder

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Subject

Food Science

Link

https://link.springer.com/content/pdf/10.1007/s13197-020-04847-y.pdf

Reference35 articles.

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3. Audrain B, Farag MA, Ryu C-M, Ghigo J-M (2015) Role of bacterial volatile compounds in bacterial biology. FEMS Microbiol Rev 39:222–233. https://doi.org/10.1093/femsre/fuu013

4. 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. https://doi.org/10.1109/iccsce.2017.8284413

5. Bao Y, Hu Z, Xiong T (2013) A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 117:98–106. https://doi.org/10.1016/j.neucom.2013.01.027

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