Can Near-infrared Spectroscopy Detect and Differentiate Implant-associated Biofilms?

BACKGROUND: Established bacterial diagnostic techniques for orthopaedic-related infections rely on a combination of imperfect tests that often can lead to negative culture results. Spectroscopy is a tool that potentially could aid in rapid detection and differentiation of bacteria in implant-associated infections. QUESTIONS/PURPOSES: We asked: (1) Can principal component analysis explain variation in spectral curves for biofilm obtained from Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa? (2) What is the accuracy of Fourier transformed-near infrared (FT-NIR)/multivariate data analysis in identifying the specific species associated with biofilm? METHODS: Three clinical isolates, S aureus, S epidermidis, and P aeruginosa were cultured to create biofilm on surgical grade stainless steel. At least 52 samples were analyzed per group using a FT-NIR spectrometer. Multivariate and principal component analyses were performed on the spectral data to allow for modeling and identification of the bacterial species. RESULTS: Spectral analysis was able to correctly identify 86% (37/43) of S aureus, 89% (16/18) of S epidermidis, and 70% (28/40) of P aeruginosa samples with minimal error. Overall, models developed using spectral data preprocessed using a combination of standard normal variant and first-derivative transformations performed much better than models developed with the raw spectral data in discriminating between the three classes of bacteria because of its low Type 1 error and large intermodel distinction. CONCLUSIONS: The use of spectroscopic methods to identify and classify bacterial biofilms on orthopaedic implant material is possible and improves with advanced modeling that can be obtained rapidly with little error. The sensitivity for identification was 97% for S aureus (95% CI, 88-99%), 100% for S epidermidis (95% CI, 95-100%), and 77% for P aeruginosa (95% CI, 65-86%). The specificity of the S aureus was 86% (95% CI, 3-93%), S epidermidis was 89% (95% CI, 67-97%), and P aeruginosa was 70% (95% CI, 55-82%). CLINICAL RELEVANCE: This technique of spectral data acquisition and advanced modeling should continue to be explored as a method for bacterial biofilm identification. A spectral databank of bacterial and potentially contaminating tissues should be acquired initially through an in vivo animal model and quickly transition to explanted devices and the clinical arena.

Rapid characterization of biomass using near infrared spectroscopy coupled with multivariate data analysis: Part 1. Yellow-poplar (Liriodendron tulipifera L.).

This paper is the first of a two series papers on the use of near infrared (NIR) coupled with multivariate data analysis (MVDA) as a process analytical technology (PAT) tool for the rapid characterization of physical and chemical properties of two common West Virginian hardwood species, northern red oak (Quercus rubra) and yellow-poplar (Liriodendron tulipifera L.). These two wood species are potential feed stock for the bio-refinery industry. In Part 1, we report our results on yellow-poplar. The results of this study demonstrated that some preprocessing operations on the NIR spectra (first derivative) greatly improved all the prediction models developed in the study. Predictive PLS1 models developed using selective spectra regions, 1300-1800 nm and the full NIR region (800-2400 nm), were similar. The selective spectra region, 1300-1800 nm, included the first and second overtone of the NIR spectrum (1300-1800 nm). Measured and predicted physical and chemical properties of yellow-poplar yielded moderate to high correlation (R2).