Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 21, 2011
Publication Date: January 19, 2012
Citation: Sundaram, J., Park, B., Yoon, S.C., Hinton Jr, A., Windham, W.R., Lawrence, K.C. 2012. Classification and structural analysis of live and dead salmonella cells using fourier transform infrared (FT-IR) spectroscopy and principle component analysis (PCA). Journal of Agricultural and Food Chemistry. 60(4):991-1004. Interpretive Summary: Detection, identification and differentiation of different bacteria and their serotypes takes a long time in conventional microbiological methods. Rapid detection and identification methods of foodborne pathogens are also demanding in food safety and quality control practices. In recent years, physicochemical methods of identifying organisms and their subsequent characterization are getting significant attraction for rapid detection. Fourier transform infrared spectroscopy (FT-IR) method was investigated to detect and differentiate Salmonella foodborne pathogen. Salmonella typhimurium and Salmonella entritidis with live and dead cells were used for this study. The Salmonella cells were scanned using FT-IR spectroscopy and spectral data were collected. Statistical analysis was carried out for differentiating the live and dead cells. Live cells of both Salmonella typhimurium and Salmonella entritidis were classified with 100 % accuracy. Thus, FT-IR spectroscopy method can distinguish the two different serotypes of Salmonella cells and also live cells from dead cells rapidly and without extensive sample preparation.
Technical Abstract: Fourier Transform Infrared Spectroscopy (FT-IR) was used to detect Salmonella typhimurium and Salmonella enteritidis foodborne bacteria and distinguish between live and dead cells of both serotypes. Bacteria were loaded individually on the ZnSe Attenuated Total Reflection (ATR) crystal surface and scanned, and then spectra were recorded from 4000 cm-1 to 650 cm-1 wave number. Analysis of spectral signatures of Salmonella isolates were conducted using Principle Component Analysis (PCA). PCA models were developed to differentiate the serotypes and viability of the cells. Based on the variability of spectral signatures of bacteria, spectral data were divided into three spectral ranges of 900 -1300 cm-1, 1300 -1800 cm-1, and 3000 - 2200 cm-1. From the Soft Independent Modeling of Class Analogy (SIMCA) analysis using PCA model, maximum classification of 100 % was obtained. Calculation of Mahalanobis distance showed higher variation between live and cells as well as between the serotypes at the spectral range of 1300-1800 cm-1. This indicates that there are more differences in nucleic acids, DNA/RNA backbone structures, protein and amide I and amide II bands between the Salmonella serotypes and between live and dead bacterial cells of each serotype. Therefore scanning the samples in the spectral range 1300-1800 cm-1 itself can able to differentiate the live and dead cells and also differentiate the Salmonella serotypes.