Literature DB >> 31305129

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

Ernest Bonah1,2, Xingyi Huang1, Joshua Harrington Aheto1, Richard Osae1.   

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.

Entities:  

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

Year:  2019        PMID: 31305129      PMCID: PMC6785170          DOI: 10.1089/fpd.2018.2617

Source DB:  PubMed          Journal:  Foodborne Pathog Dis        ISSN: 1535-3141            Impact factor:   3.171


  23 in total

1.  Detection by hyperspectral imaging of shiga toxin-producing Escherichia coli serogroups O26, O45, O103, O111, O121, and O145 on rainbow agar.

Authors:  William R Windham; Seung-Chul Yoon; Scott R Ladely; Jennifer A Haley; Jerry W Heitschmidt; Kurt C Lawrence; Bosoon Park; Neelam Narrang; William C Cray
Journal:  J Food Prot       Date:  2013-07       Impact factor: 2.077

2.  An application based on the decision tree to classify the marbling of beef by hyperspectral imaging.

Authors:  Lía Velásquez; J P Cruz-Tirado; Raúl Siche; Roberto Quevedo
Journal:  Meat Sci       Date:  2017-06-06       Impact factor: 5.209

3.  FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Identification of Pathogenic Bacteria.

Authors:  Peter Lasch; Maren Stämmler; Miao Zhang; Malgorzata Baranska; Alejandra Bosch; Katarzyna Majzner
Journal:  Anal Chem       Date:  2018-07-11       Impact factor: 6.986

Review 4.  Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat.

Authors:  Marlon M Reis; Robbe Van Beers; Mahmoud Al-Sarayreh; Paul Shorten; Wei Qi Yan; Wouter Saeys; Reinhard Klette; Cameron Craigie
Journal:  Meat Sci       Date:  2018-05-30       Impact factor: 5.209

Review 5.  Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications.

Authors:  Ji Ma; Da-Wen Sun; Hongbin Pu; Jun-Hu Cheng; Qingyi Wei
Journal:  Annu Rev Food Sci Technol       Date:  2019-01-11

6.  Detection of Salmonella from chicken rinsate with visible/near-infrared hyperspectral microscope imaging compared against RT-PCR.

Authors:  Matthew Eady; Gayatri Setia; Bosoon Park
Journal:  Talanta       Date:  2018-11-23       Impact factor: 6.057

7.  Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging.

Authors:  Chuanqi Xie; Bingquan Chu; Yong He
Journal:  Food Chem       Date:  2017-10-16       Impact factor: 7.514

8.  Classification of Salmonella enterica serotypes with selective bands using visible/NIR hyperspectral microscope images.

Authors:  M Eady; B Park
Journal:  J Microsc       Date:  2015-12-23       Impact factor: 1.758

9.  Electroanalytical sensors and devices for multiplexed detection of foodborne pathogen microorganisms.

Authors:  María Pedrero; Susana Campuzano; José M Pingarrón
Journal:  Sensors (Basel)       Date:  2009-07-13       Impact factor: 3.576

Review 10.  Detection of Foodborne Pathogens by Surface Enhanced Raman Spectroscopy.

Authors:  Xihong Zhao; Mei Li; Zhenbo Xu
Journal:  Front Microbiol       Date:  2018-06-12       Impact factor: 5.640

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  2 in total

1.  Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses.

Authors:  Ahyeong Lee; Saetbyeol Park; Jinyoung Yoo; Jungsook Kang; Jongguk Lim; Youngwook Seo; Balgeum Kim; Giyoung Kim
Journal:  Sensors (Basel)       Date:  2021-03-22       Impact factor: 3.576

2.  Nondestructive monitoring, kinetics and antimicrobial properties of ultrasound technology applied for surface decontamination of bacterial foodborne pathogen in pork.

Authors:  Ernest Bonah; Xingyi Huang; Yang Hongying; Joshua Harrington Aheto; Ren Yi; Shanshan Yu; Hongyang Tu
Journal:  Ultrason Sonochem       Date:  2020-09-10       Impact factor: 7.491

  2 in total

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