Open Access
1 October 2012 Reagent-free bacterial identification using multivariate analysis of transmission spectra
Jennifer M. Smith, Debra E. Huffman, Dayanis Acosta, Yulia Serebrennikova, Luis H. Garcia-Rubio, German F. Leparc
Author Affiliations +
Abstract
The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2012/$25.00 © 2012 SPIE
Jennifer M. Smith, Debra E. Huffman, Dayanis Acosta, Yulia Serebrennikova, Luis H. Garcia-Rubio, and German F. Leparc "Reagent-free bacterial identification using multivariate analysis of transmission spectra," Journal of Biomedical Optics 17(10), 107002 (1 October 2012). https://doi.org/10.1117/1.JBO.17.10.107002
Published: 1 October 2012
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Organisms

Pathogens

Statistical modeling

Bacteria

Absorbance

Biological research

Blood

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