Detection of foodborne pathogenic bacteria based on hyperspectral imaging

Authors

DUAN Peng-sheng, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China; Fujian Colleges and Universities Engineering Research Center of Modern Agricultural Equipment, Fuzhou, Fujian 350002, China
XU Jin-chai, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China; Fujian Colleges and Universities Engineering Research Center of Modern Agricultural Equipment, Fuzhou, Fujian 350002, China
CHEN Yi-lei, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
YE Da-peng, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China; Fujian Colleges and Universities Engineering Research Center of Modern Agricultural Equipment, Fuzhou, Fujian 350002, China
WENG Hai-yong, College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China; Fujian Colleges and Universities Engineering Research Center of Modern Agricultural Equipment, Fuzhou, Fujian 350002, China

Abstract

Aiming at the problem of foodborne pathogenic bacteria detection process is time-consuming, composed a application highlights like technology obtained 12,18,24 h 3 phase 5 kinds of pathogenic bacteria [Staphylococcus aureus (S. aureus), Listeria monocytogenes (LM), Diarrheagenic e.coli (DEC), Salmonella enteritidis (SE) and Shigella spp flexneri (S. flexneri)] of hyperspectral image, using successive projections algorithm (SPA) in combination with relevant analysis (CA) to extract various growing pathogenic bacteria sensitive wave bands and established the least squares support vector machines (LS-SVM) discriminant model. The results showed that the reflectivity of the five types of pathogens to different wavelengths of light was different. The reflectance of the five sensitive bands (462,498,649,853,979 nm) screened by SPA-CA could well reflect the spectral characteristics of the five types of pathogens at different growth stages. The LS-SVM model based on the reflectance of this sensitive band can effectively identify S. aureus, LM and DEC, while SE and S. flexneri are easily misjudged by each other. The probability of SE being misjudged as S. flexneri is 11.2%, and the probability of S. flexneri being misjudged as SE is 19.9%. The overall recognition accuracy of LS-SVM model was 90.9% for the five types of pathogenic bacteria. In conclusion, hyperspectral imaging combined with stoichiometry has the ability of rapid diagnosis of foodborne pathogens.

Publication Date

4-28-2021

First Page

63

Last Page

67,86

DOI

10.13652/j.issn.1003-5788.2021.04.011

Recommended Citation

Peng-sheng, DUAN; Jin-chai, XU; Yi-lei, CHEN; Da-peng, YE; and Hai-yong, WENG (2021) "Detection of foodborne pathogenic bacteria based on hyperspectral imaging," Food and Machinery: Vol. 37: Iss. 4, Article 11.
DOI: 10.13652/j.issn.1003-5788.2021.04.011
Available at: https://www.ifoodmm.cn/journal/vol37/iss4/11

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