Fast detection of bacterial contamination in fresh produce using FTIR and spectral classification

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Screening for microbial contaminants in fresh produce is a lengthy process relative to their short shelf-life. The aim of this study is to develop a comprehensive assay which employs FTIR and spectral classification algorithm for detection of bacterial contamination of fresh produce.The procedure starts by soaking a sample of the fresh produce in broth for 5 h. Then, magnetic nanoparticles are added to capture bacteria which are then collected and prepared for FTIR scanning. The generated FTIR spectra are compared against an in-house database of different bacterial species (n = 6). The ability of the database to discriminate contaminated and uncontaminated samples and to identify the bacterial species was assessed. The compatibility of the FTIR procedures with subsequent DNA extraction and PCR was tested. The developed procedure was applied for assessment of bacterial contamination in fresh produce samples from the market (n = 78). Results were compared to the conventional culture methods.The generated FTIR database coupled to spectral classification was able to detect bacterial contamination with overall accuracy exceeding 90%. The sample processing did not alter the integrity of the bacterial DNA which was suitable for PCR. On application to fresh produce samples collected from the market, the developed method was able to detect bacterial contamination with 94% concordance with the culture method.In conclusion, the developed procedure can be applied for fast detection of microbial contamination in fresh produce with comparable accuracy to conventional microbiological assays and is compatible with subsequent molecular assays.