Skip to main content
SpringerLink
Log in

AOTF Hyperspectral Imaging for Foodborne Pathogen Detection

  • Chapter
Hyperspectral Imaging Technology in Food and Agriculture

Part of the book series: Food Engineering Series ((FSES))

Abstract

Food safety is an important public health issue worldwide. Researchers have developed many different methods for detecting foodborne pathogens; however, most technologies currently being used have limitations, in terms of speed, sensitivity and selectivity, for practical use in the food industry. Acousto-optic tunable filter (AOTF)-based hyperspectral microscope imaging (HMI) is an optical method for rapidly identifying foodborne pathogenic bacterial at the single cell level. In conjunction with dark-field illumination, HMI method is able to acquire spectral signatures from the scattering intensity of bacterial cells. Researchers have successfully developed the method to acquire quality hyperspectral microscopic images from various foodborne pathogenic bacterial live cells. From the contiguous spectral images over the visible and near-infrared electromagnetic spectral bands, the spectral scattering signature from different bacterial species can be observed at the selected wavelengths. Since scattering peak intensity at various wavelengths depends on bacterial serotypes as well as species, statistical models can be developed to classify different foodborne bacteria. Herein, we introduce a hyperspectral microscope imaging system, immobilization of live bacterial cell for image acquisition, spectral characteristics of bacteria, and classification methods. We demonstrate classification of three bacteria species including Salmonella, Staphylococcus, and Escherichia coli. High classification accuracy is obtained from classification models with scattering intensity data from bacterial cells. The performance of the classification models has been validated with bacterial cultures from food matrices, so that latex agglutination or polymerase chain reaction (PCR) tests can confirm positively identified colonies of bacterial samples using a rapid hyperspectral microscope imaging method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Burger J, Geladi P (2006) Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Analyst 131:1152–1160

    Article  CAS  Google Scholar 

  • Carrasco O, Gomez RB, Chainani A, Roper WE (2003) Hyperspectral imaging applied to medical diagnoses and food safety. In: Proceedings of SPIE 5097, geo-spatial and temporal image and data exploitation III 215. doi:10.1117/12.502589

    Google Scholar 

  • CDC (2010) FoodNet surveillance report for 2008

    Google Scholar 

  • Center for Disease Control and Prevention (CDC) (2012) National Center for Emerging and Zoonotic Infectious Diseases, Division of Foodborne, Waterborne and Environmental Diseases

    Google Scholar 

  • Correa AA, Toso J, Albarnaz JD, Simoes CMO, Barardi CRM (2006) Detection of Salmonella typhimurium in oysters by PCR and molecular hybridization. J Food Qual 29(5):458–469

    Article  CAS  Google Scholar 

  • De Beule P, Owen DM, Manning HB, Talbot CB, Requejo-Isidro J (2007) Rapid hyperspectral fluorescence lifetime imaging. Microsc Res Tech 70:481–484

    Article  Google Scholar 

  • De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The Mahalanobis distance. Chemom Intell Lab Syst 50:1–18

    Article  Google Scholar 

  • Dicker DT, Lerner J, Van Belle P, Herlyn M, Elder DE (2006) Differentiation of normal skin and melanoma using high resolution hyperspectral imaging. Cancer Biol Ther 5(8):1003–1008

    Article  Google Scholar 

  • Dixon SJ, Brereton RG (2009) Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemom Intell Lab Syst 95:1–17

    Article  CAS  Google Scholar 

  • Feng YZ, Sun DW (2012) Application of hyperspectral imaging in food safety inspection and control: a review. Crit Rev Food Sci Nutr 52(11):1039–1058

    Article  Google Scholar 

  • Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinfomatics 16:906–914

    Article  CAS  Google Scholar 

  • Gehm ME, Brady DJ (2009) High-throughput hyperspectral microscopy. In: Proceedings of SPIE 6090

    Google Scholar 

  • Gerner-Smidt P, Hise K, Kincaid J, Hunter S, Rolando S, Hyytia-Trees E, Ribot EM, Swaminathan B (2006) Pulsenet Taskforce. PulseNet USA: a five-year update. Foodborne Pathog Dis 3:9–19

    Article  CAS  Google Scholar 

  • Gowen AA, O’Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18(12):590–598

    Article  CAS  Google Scholar 

  • Huebschman ML, Schultz RA, Garner HR (2002) Characteristics and capabilities of the hyperspectral imaging microscope. IEEE Eng Med Biol Mag 21(4):104–117

    Article  Google Scholar 

  • Ibraheem I, Leitner R, Mairer H, Cerroni L, Smolle J (2006) Hyperspectral analysis of stained histological preparations for the detection of melanoma. In: Proceedings of 3rd international spectral imaging workshop, Graz, Austria, 13 May 2001, pp 24–32

    Google Scholar 

  • ISO 6579 (2002) Microbiology of food and animal feeding stuffs—horizontal method for the detection of Salmonella spp. Geneva, Switzerland

    Google Scholar 

  • Jun W, Kim MS, Lee K, Millner P, Chao K (2009) Assessment of bacterial biofilm on stainless steel by hyperspectral fluorescence imaging. J Food Meas Charact 3(1):41–48

    Google Scholar 

  • Kim MS, Chen YR, Mehl PM (2001) Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Trans ASABE 44(3):721–729

    Google Scholar 

  • Kong SG, Martin ME, Vo-Dinh T (2006) Hyperspectral fluorescence imaging for mouse skin tumor detection. ETRI J 28(6):770–776

    Article  Google Scholar 

  • Lawlor J, Fletcher-Holmes DW, Harvey AR, McNaught AI (2002) In vivo hyperspectral imaging of human retina and optic disc. Invest Ophthalmol Vis Sci 43:4350

    Google Scholar 

  • Li G, Tam LK, Tan JX (2008) Amplified effect of Brownian motion in bacterial near-surface swimming. Proc Natl Acad Sci U S A 105(47):18355–18359

    Article  CAS  Google Scholar 

  • Liu Z, Li Q, Yan J, Tang Q (2007a) A novel hyperspectral medical sensor for tongue diagnosis. Sens Rev 27(1):57–60

    Article  Google Scholar 

  • Liu Y, Chen YR, Kim MS, Chan DE, Lefcourt AM (2007b) Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging. J Food Eng 81(2):412–418

    Article  CAS  Google Scholar 

  • Mehl PM, Chen YR, Kim MS, Chan DE (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J Food Eng 61(1):67–81

    Article  Google Scholar 

  • Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Geosci Remote Sens 42(8):1778–1790

    Article  Google Scholar 

  • Munson TE, Schrade JP, Bisciello NB Jr, Fantasia LD, Hartung WH, O’Connor JJ (1976) Evaluation of an automated fluorescent antibody procedure for detection of salmonella in foods and feeds. Appl Environ Microbiol 31(4):514–521

    CAS  Google Scholar 

  • Nagata M, Tallada JG, Kobayahi T (2006) Bruise detection using NIR hyperspectral imaging for strawberry (Fragaria * ananassa Duch.). Environ Control Biol 44(2):133–142

    Article  Google Scholar 

  • Park B, Lawrrence KC, Windham WR, Smith DP, Feldner PW (2002) Hyperspectral imaging for food processing automation. In: Proceedings of SPIE 4816, imaging spectrometry VIII 308. doi:10.1117/12.447917

    Google Scholar 

  • Park B, Lawrence KC, Windham WR, Smith DP (2006) Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. J Food Eng 75(3):340–348

    Article  Google Scholar 

  • Park B, Windham WR, Lawrence KC, Smith DP (2007a) Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm. Biosyst Eng 96(3):323–333

    Article  Google Scholar 

  • Park B, Yoon SC, Lawrence KC, Windham WR (2007b) Fisher Linear discriminant analysis for improving fecal detection accuracy with hyperspectral images. Trans ASABE 50:2275–2283

    Article  Google Scholar 

  • Park B, Lee S, Yoon SC, Sundaram J, Windham WR, Hinton Jr A, Lawrence KC (2011a) AOTF hyperspectral microscope imaging for foodborne pathogenic bacteria detection. In: Proceedings of SPIE defense, security and sensing, vol 8027, pp 1–11

    Google Scholar 

  • Park B, Yoon SC, Lee S, Sundaram J, Windham WR, Hinton A Jr, Lawrence KC (2012a) Acousto-optic tunable filter hyperspectral microscope imaging for identifying foodborne pathogens. Trans ASABE 55:1997–2006

    Article  Google Scholar 

  • Park B, Windham WR, Ladely AR, Gurram P, Kwon H, Yoon SC, Lawrence KC, Narrang N, Cray WC (2012b) Classification of Shiga toxin-producing Escherichia coli (STEC) serotypes with hyperspectral microscope imagery. In: Proceedings of SPIE defense, security and sensing 83690L, pp 1–13

    Google Scholar 

  • Park B, Windham WR, Ladely SR, Gurram P, Kwon H, Yoon SC, Lawrence KC, Narang N, Cray WC (2014) Detection of non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups with hyperspectral microscope imaging technology. Trans ASABE 57:973–986

    Google Scholar 

  • Peng Y, Zhang J, Wang W, Li Y, Wu J, Huang H, Gao X, Jiang W (2011) Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles. J Food Eng 102(2):163–169

    Article  Google Scholar 

  • Scharff R (2012) Economic burden from health losses due to foodborne illness in the United States. J Food Prot 75:123–131

    Article  Google Scholar 

  • Sorg BS, Moeller BJ, Donovan O, Cao Y, Dewhirst MW (2005) Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development. J Biomed Opt 5694(1):74–81

    CAS  Google Scholar 

  • Swaminathan B, Gerner-Smidt P, Ng LK, Lukinmaa S, Kam KM, Rolando S, Gutierrez EP, Binsztein N (2006) Building PulseNet International: an interconnected system of laboratory networks to facilitate timely public health recognition and response to foodborne disease outbreaks and emerging foodborne diseases. Foodborne Pathog Dis 3:36–50

    Article  Google Scholar 

  • Tatzer P, Wolf M, Panner T (2005) Industrial application for inline material sorting using hyperspectral imaging in the NIR range. Real-Time Imaging 11(2):99–107

    Article  Google Scholar 

  • Terajima J, Izumiya H, Iyoda S, Mitobe J, Miura M, Watanabe H (2006) Effectiveness of pulsed-field gel electrophoresis for the early detection of diffuse outbreaks due to Shiga toxin producing Escherichia coli in Japan. Foodborne Pathog Dis 3:68–73

    Article  CAS  Google Scholar 

  • Tian H, Miyamoto T, Okabe T, Kuramitsu Y, Honjoh KI, Hatano S (1996) Rapid detection of Salmonella spp. In foods by combination of a new selective enrichment and a sandwich ELISA using two monoclonal antibodies against Dulcitol1-phosphate dehydrogenase. J Food Prot 59(11):1158–1163

    CAS  Google Scholar 

  • Vandeginste BMG, Massart DL, Buydens LMC, De Jong S, Lewi PJ, Smeyers-Verbeke J (1998) Handbook of chemometrics and qualimetrics: part B. Elsevier, Amsterdam

    Google Scholar 

  • Vargas AM, Kim MS, Tao Y, Lefcourt AM, Chen YR, Luo Y, Song Y, Buchanan R (2005) Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery. J Food Sci 70(8):471–476

    Article  Google Scholar 

  • Velusamy V, Arshak K, Korostynska O, Oliwa K, Adley C (2010) An overview of foodborne pathogen detection: in the perspective of biosensors. Biotechol Adv 28:232–254

    Article  CAS  Google Scholar 

  • Vermaas WFJ, Timlin JA, Jones T, Sinclair MB, Nieman LT (2008) In vivo hyperspecral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells. Proc Natl Acad Sci 105(10):4050–4055

    Article  CAS  Google Scholar 

  • Windham WR, Yoon SC, Ladely SR, Heitschmidt GW, Lawrence KC, Park B, Narang N, Cray WC (2012) The effect of regions of interest and spectral pre-processing on the detection of non-O157 Shiga toxin-producing Escherichia coli serogroups on agar media by hyperspectral imaging. J Near Infrared Spectrosc 20:547–558

    Article  CAS  Google Scholar 

  • Wu W, Mallet Y, Walczak B, Penninckx W, Massart DL, Heurding S, Erini F (1996) Comparison of regularised discriminant analysis, linear discriminant analysis and quadratic discriminant analysis, applied to NIR data. Anal Chim Acta 329:257–265

    Article  CAS  Google Scholar 

  • Yao H, Hruska Z, Brown RL, Cleveland TE (2006) Hyperspectral bright greenish-yellow fluorescence (BGYF) imaging of aflatoxin contaminated corn kernels. In: Proceedings of SPIE 6381, optics for natural resources, agriculture, and foods 63810B. doi:10.1117/12.686217

    Google Scholar 

  • Yoon SC, Lawrence KC, Siragusa G, Line J, Park B, Feldner P (2009) Hyperspectral reflectance imaging for detecting a foodborne pathogen: Campylobacter. Trans ASABE 52:651–662

    Article  Google Scholar 

  • Yoon SC, Park B, Lawrence KC, Windham WR, Heitschmidt GW (2011) Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta. Comput Electron Agric 79:159–168

    Article  Google Scholar 

  • Yoon SC, Windham WR, Ladely SR, Heitschmidt GW, Lawrence KC, Park B, Narang N, Cray WC (2013) Hyperspectral imaging for differentiating colonies of non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups on spread plates of pure cultures. J Near Infrared Spectrosc 21:81–95

    Article  CAS  Google Scholar 

  • Zimmerman T, Reitdorf J, Pepperkok R (2003) Spectral imaging and its applications in live cell microscopy. FEBS Lett 546(1):87–92

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. U.S. Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA, 30605, USA

    Bosoon Park

Authors
  1. Bosoon Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bosoon Park .

Editor information

Editors and Affiliations

  1. Agricultural Research Service, U.S. Department of Agriculture, Athens, Georgia, USA

    Bosoon Park

  2. Agricultural Research Service, U.S. Department of Agriculture, East Lansing, Michigan, USA

    Renfu Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Park, B. (2015). AOTF Hyperspectral Imaging for Foodborne Pathogen Detection. In: Park, B., Lu, R. (eds) Hyperspectral Imaging Technology in Food and Agriculture. Food Engineering Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2836-1_15

Download citation

Publish with us

Policies and ethics

Search

Navigation