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Review
. 2019 Oct;16(10):712-722.
doi: 10.1089/fpd.2018.2617. Epub 2019 Jul 15.

Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization

Ernest Bonah  1   2 , Xingyi Huang  1 , Joshua Harrington Aheto  1 , Richard Osae  1
Affiliations
Review

Application of Hyperspectral Imaging as a Nondestructive Technique for Foodborne Pathogen Detection and Characterization

Ernest Bonah et al. Foodborne Pathog Dis. 2019 Oct.

Abstract

Microbial food safety is a persistent and exacting global issue due to the multiplicity and complexity of foods and food production systems. Foodborne illnesses caused by foodborne bacterial pathogens frequently occur, thus endangering the safety and health of human beings. Factors such as pretreatments, that is, culturing, enrichment, amplification make the traditional routine identification and enumeration of large numbers of bacteria in a complex microbial consortium complex, expensive, and time-consuming. Therefore, the need for rapid point-of-use detection systems for foodborne bacterial pathogens with high sensitivity and specificity is crucial in food safety control. Hyperspectral imaging (HSI) as a powerful testing technology provides a rapid, nondestructive approach for pathogen detection. This article reviews some fundamental information about HSI, including instrumentation, data acquisition, image processing, and data analysis-the current application of HSI for the detection, classification, and discrimination of various foodborne pathogens. The merits and demerits of HSI for pathogen detection as well as current and future trends are discussed. Therefore, the purpose of this review is to provide a brief overview of HSI, and further lay emphasis on the emerging trend and importance of this technique for foodborne pathogen detection.

Keywords: chemometrics; foodborne pathogen; hyperspectral; imaging; nondestructive; rapid detection.

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Conflict of interest statement

No competing financial interests exist.

Figures

<b>FIG. 1.</b>
FIG. 1.
Schematic view of hyperspectral imaging system by Li et al. (2016).
<b>FIG. 2.</b>
FIG. 2.
Hyperspectral image acquisition approaches and image sensing modes (labels a–g) adopted from Wu et al. (2013c).
<b>FIG. 3.</b>
FIG. 3.
Classification of chemometric modeling algorithms in hyperspectral data analysis adopted from Pan et al. (2016).
See this image and copyright information in PMC

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