Classification and Structural Analysis of Live and Dead Salmonella Cells Using Fourier Transform Infrared Spectroscopy and Principal Component Analysis

• Published in: Journal of agricultural and food chemistry 2012-02, Vol.60 (4), p.991-1004
• Main Authors: Sundaram, Jaya, Park, Bosoon, Hinton, Arthur, Yoon, Seung Chul, Windham, William R, Lawrence, Kurt C
• Format: Article
• Language: English
• Subjects: amides; bacteria; Biological and medical sciences; cell differentiation; computer software; crystals; data collection; DNA; Food industries; Food microbiology; Food Microbiology - methods; Foodborne Diseases - microbiology; Foodborne Diseases - prevention & control; foodborne infections; Fourier transform infrared spectroscopy; Fundamental and applied biological sciences. Psychology; General aspects; Hygiene and safety; model validation; principal component analysis; proteins; RNA; Salmonella enteritidis; Salmonella enteritidis - chemistry; Salmonella enteritidis - classification; Salmonella enteritidis - cytology; Salmonella typhimurium; Salmonella typhimurium - chemistry; Salmonella typhimurium - classification; Salmonella typhimurium - cytology; serotypes; Serotyping; spectral analysis; Spectroscopy, Fourier Transform Infrared; wavelengths
• ISSN: 0021-8561; 1520-5118
• DOI: 10.1021/jf204081g

Fourier transform infrared spectroscopy (FT-IR) was used to detect Salmonella Typhimurium and Salmonella Enteritidis food-borne bacteria and to distinguish between live and dead cells of both serotypes. Bacteria cells were prepared in 108 cfu/mL concentration, and 1 mL of each bacterium was loaded individually on the ZnSe attenuated total reflection (ATR) crystal surface (45° ZnSe, 10 bounces, and 48 mm × 5 mm effective area of analysis on the crystal) and scanned for spectral data collection from 4000 to 650 cm–1 wavenumber. Analysis of spectral signatures of Salmonella isolates was conducted using principal component analysis (PCA). Spectral data were divided into three regions such as 900–1300, 1300–1800, and 3000–2200 cm–1 based on their spectral signatures. PCA models were developed to differentiate the serotypes and live and dead cells of each serotype. Maximum classification accuracy of 100% was obtained for serotype differentiation as well as for live and dead cells differentiation. Soft independent modeling of class analogy (SIMCA) analysis was carried out on the PCA model and applied to validation sample sets. It gave a predicted classification accuracy of 100% for both the serotypes and its live and dead cells differentiation. The Mahalanobis distance calculated in three different spectral regions showed maximum distance for the 1800–1300 cm–1 region, followed by the 3000–2200 cm–1 region, and then by the 1300–900 cm–1 region. It showed that both of the serotypes have maximum differences in their nucleic acids, DNA/RNA backbone structures, protein, and amide I and amide II bands.