The Influence of Environmental Growth Conditions on Salmonella Spectra Obtained from Hyperspectral Microscope Images

  • Matthew Eady
  • Bosoon ParkEmail author


Salmonella causes illness in millions of people each year with severe cases resulting in death. Traditional detection methods are well established but are associated with disadvantages such as time or reoccurring sample cost. Hyperspectral microscope imaging (HMI) has shown potential as an early and rapid detection method, identifying bacteria based on spectral signatures unique to the microorganism. Bacteria undergo physiological changes when introduced to environmental stresses. Understanding how these physiological changes impact the resulting cellular spectra is critical to developing robust HMI methodologies for early and rapid detection of bacteria. Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST) were incubated on plate agars of brilliant green sulfa, tryptic soy agar, xylose lysine deoxycholate, and xylose lysine tergitol 4, at incubation pH ranging from 4.7 to 8.3, and at incubation temperatures between 27 and 47 °C. All samples were incubated for 24 h. A principal component analysis (PCA) was performed for each experiment, with Mahalanobis distances (MD) calculated for each sample to its respective cluster’s center, quantifying intraclass variation. One-way ANOVA of MD values showed no significant difference for SE and ST grown on four agars (P > 0.05), or at varying incubation temperatures (P > 0.05). SE and ST incubated at five pH values showed significantly different spectra (P < 0.01). Results suggest that future HMI tools for identifying Salmonella can be developed regardless of the growth media or temperature but should take into consideration acidic growth environments.


Salmonella Hyperspectral microscopy Rapid detection Foodborne bacteria Food safety 



The authors would like to thank Drs. Nasreen Bano and Jing Chen, of the Quality and Safety Assessment Research Unit located at the U.S. National Poultry Research Center in Athens, GA, for their assistance with hyperspectral image collection and experiments.

Compliance with Ethical Standards

Conflict of Interest

Matthew Eady declares that he has no conflict of interest. Bosoon Park declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Supplementary material

12161_2019_1618_MOESM1_ESM.pdf (348 kb)
ESM 1 (PDF 348 kb)


  1. Abee T, Wouters JA (1999) Microbial stress response in minimal processing. Inter J Food Microbiol 50:65–91Google Scholar
  2. Anderson J, Reynolds C, Ringelberg D, Edwards J, Foley K (2008) Differentiation of live-viable versus dead bacterial endospores by calibrated hyperspectral reflectance microscopy. J Microsc 232:130–136CrossRefGoogle Scholar
  3. Brereton R, Lloyd G (2016) Re-evaluating the role of the Mahlanobis distance measure. J Chemom 30:134–143CrossRefGoogle Scholar
  4. CDC (Centers for Disease Control and Prevention) (2019a) Salmonella: reports of selected Salmonella outbreak investigations. 25 January 2019. Accessed 08.03.19
  5. CDC (Centers for Disease Control and Prevention) (2019b) Foodborne diseases active surveillance network (FoodNet): FoodNet surveillance report. 16 July 2019.
  6. Cotter PD, Hill C (2003) Surviving the acid test: response of gram-positive bacteria to low pH. Microbiol Mol Biol Rev 67:429–453CrossRefGoogle Scholar
  7. Eady M, Park B (2016) Classification of Salmonella enterica serotypes with selective bands using visible/NIR hyperspectral microscope images. J Microsc 263(1):10–19CrossRefGoogle Scholar
  8. Eady M, Park B, Choi S (2015) Rapid and early detection of Salmonella serotypes with hyperspectral microscopy and multivariate detection. J Food Prot 78:668–674CrossRefGoogle Scholar
  9. Eady MB, Park B, Yoon SC, Haidekker MA, Lawrence KC (2018) Methods for hyperspectral microscope calibration and spectra normalization from images of bacteria cells. Trans ASABE 61(2):437–448CrossRefGoogle Scholar
  10. Feng YZ, Sun DW (2013) Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. Talanta 105:244–249CrossRefGoogle Scholar
  11. Kim H, Doh IJ, Sturgis J, Bhunia A, Robinson JP, Bae E (2016) Reflected scatterometry for noninvasive interrogation of bacterial colonies. J Biomed Opt 21:107004CrossRefGoogle Scholar
  12. Leyer GJ, Johnson EA (1993) Acid adaptation induces cross-protection against environmental stresses in Salmonella typhimurium. Appl Environ Microbiol 59(6):1842–1847Google Scholar
  13. Li H, Wang H, D’Aoust JY, Maurer J (2013) Salmonella species. In: Doyle MP, Buchannon RL (eds) Food microbiology: fundamentals and frontiers 4th edn. ASM Press, Washington, DC, pp 225–262Google Scholar
  14. Lu R, Park B (2015) Image and spectral analysis techniques: introduction. In: Park B, Lu R (eds) Hyperspectral imaging technology in food and agriculture. Springer, New York, pp 3–8CrossRefGoogle Scholar
  15. Moats W, Kinner J (1974) Factors effecting selectivity of brilliant green-phenol red agar for salmonellae. Appl Microbiol 27:118–123Google Scholar
  16. Montville J, Matthews K (2013) Physiology, growth, and inhibition of microbes in foods. In: Doyle MP, Buchannon RL (eds) Food microbiology: fundamentals and frontiers 4th edn. ASM Press, Washington, DC, pp 3–18Google Scholar
  17. Mullis K, Faloona F (1987) Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol 155:335–350CrossRefGoogle Scholar
  18. Park B, Eady M (2016) New applications of hyperspectral imaging for bacterial cell classification. Nir News 27:4–6CrossRefGoogle Scholar
  19. Park B, Seo Y, Yoon SC, Hinton A Jr, Windham W, Lawrence K (2015) Hyperspectral microscope imaging method to classify gram-positive and gram-negative foodborne pathogenic bacteria. Trans ASABE 58:5–16Google Scholar
  20. Tang Y, Kim H, Singh AK, Aroonnual A, Bae E, Rajwa B, Fratamico PM, Bhunia A (2014) Light scattering sensor for direct identification of colonies of Escherichia coli O26, O45, O103, O145, and O157. PLoS One 9:1–15Google Scholar
  21. Varmuza K, Filzmoser P (2007) Introduction to multivariate statistical analysis in chemometrics. CRC Press, Boca RatonGoogle Scholar
  22. Wesche AM, Gurtler JB, Marks BP, Ryser ET (2009) Stress, sublethal injury, resuscitation, and virulence of bacterial foodborne pathogens. Journal of Food Protection 72(5):1121–1138Google Scholar
  23. Williams P, Kammies TL, Gauws PA, Manley M (2019) Effects of colony age on near infrared images of foodborne bacteria. J Spectral Imaging 8a5:1–12Google Scholar
  24. Windham W, Yoon SC, Ladely S, Haley J, Heitschmidt J, Lawrence K, Narrang N, Cray W (2013) Detection by hyperspectral imaging of shiga toxin-producing Escherichia coli serogroups O26, O45, O103, O111, O121, and O145 on rainbow agar. J Food Prot 76:1129–1136CrossRefGoogle Scholar
  25. Zhang L, Cleveland McEntire J, Newsome R, Wang H (2013) Antimicrobial resistance. In: Doyle MP, Buchannon RL (eds) Food microbiology: fundamentals and frontiers 4th edn. ASM Press, Washington, DC, pp 19–44Google Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.U.S. Department of Agriculture – Agricultural Research Services, Quality and Safety Assessment Research UnitU.S. National Poultry Research CenterAthensUSA

Personalised recommendations