Differentiation of Salmonella enterica Serovars and Strains in Cultures and Food Using Infrared Spectroscopic and Microspectroscopic Techniques Combined with Soft Independent Modeling of Class Analogy Pattern Recognition Analysis

Abstract

Detection of pathogenic microorganisms in food is often a tedious and time-consuming exercise. Developing rapid and cost-effective techniques for identifying pathogens to subspecies is critical for tracking causes of foodborne disease outbreaks. The objective of this study was to develop a method for rapid identification and differentiation of Salmonella serovars and strains within these serovars through isolation on hydrophobic grid membrane filters (HGMFs), examination by infrared (IR) spectroscopy and microspectroscopy, and data analysis by multivariate statistical techniques. Salmonella serovars (Anatum, Enteritidis, Heidelberg, Kentucky, Muenchen, and Typhimurium), most of which were represented by multiple strains, were grown in tryptic soy broth (24 h at 42 degrees C), diluted to 10(2) to 10(3) CFU/ml, and filtered using HGMFs. The membranes were incubated on Miller-Mallinson agar (24 h at 42 degrees C), and typical Salmonella colonies were sonicated in 50% acetonitrile and centrifuged. Resulting pellets were vacuum dried on a ZnSe crystal and analyzed using IR spectroscopy. Alternatively, the membranes containing Salmonella growth were removed from the agar, vacuum dried, and colonies were analyzed directly by IR microspectroscopy. Soft independent modeling of class analogy (SIMCA) models were developed from spectra. The method was validated by analyzing Salmonella-inoculated tomato juice, eggs, milk, and chicken. Salmonella serovars exhibited distinctive and reproducible spectra in the fingerprint region (1,200 to 900 cm(-1)) of the IR spectrum. SIMCA permitted distinguishing Salmonella strains from each other through differences in bacterial lipopolysaccharides and other membrane components. The model correctly predicted Salmonella in foods at serovar (100%) and strain (90%) levels. Isolation of Salmonella on HGMF and selective agar followed by IR spectroscopic analysis resulted in rapid and efficient isolation, identification, and differentiation of Salmonella serovars and strains.

Modern analytics for synthetically derived complex drug substances: NMR, AFFF–MALS, and MS tests for glatiramer acetate

“…Lower values indicate a closer relationship, and values greater than three are typically thought to designate significant differences between groups. Thus, generally, interclass distances greater than three are considered good indicators of wellseparated classes [22,23]. For these data, the interclass distances associated with Copolymer-1 ranged from 3.63 to 4.29, while the distances between different GA lots ranged from 0.58 to 1.49 (Table 5).…”

IR and Raman Spectroscopy for Pathogen Detection

“…A further option to concentrate bacteria and remove as much water or media components as possible from the samples is the collection of bacteria on filter material or with the help of magnetic NPs (immunomagnetic separation; IMS). Recent publications took advantage of these harvesting techniques to detect, differentiate, or enumerate targeted bacteria from cultivation media or complex matrices, especially foodstuffs such as fruit juice, milk, or chicken breast [31,[46][47][48]. For instance, E. coli O157:H7 was isolated from artificially contaminated ground beef by filtration or IMS to eliminate spectral interferences due to the meat.…”

Participation in the Center for Advanced Processing and Packaging Studies

“…FT-IR analysis of treated spore samples was modified from Subramanian et al 188 (2006) and Männig et al (2008). Aliquots (500 µl) were centrifuged at 13,000 rpm and 189 4°C for 4.5 min.…”

“…Hydrophobic grid membrane filters (HGMFs) overlaid on a selective medium was 287 previously used to isolate a single colony of Salmonella serovars (Männig et al, 2008). In 288 this current study, a protocol was developed for the classification of bacterial spores 289 treated by various processing methods by combining a hydrophobic grid membrane 290 filters with infrared spectroscopy (Subramanian et al, 2006).…”