Reliable and rapid identification of Listeria monocytogenes and Listeria species by artificial neural network-based Fourier transform infrared spectroscopy

Article Abstract:

Artificial neural network technology (ANN), an advanced multivariate data-processing method of pattern analysis is adopted to identify Listeria infrared spectra at the species level, being a reliable method for the identification of genus Listeria. ANN-based analysis enhanced differentiation success to 96% for all Listeria species including a success rate of 99.2% of L. monocytogenes strains proving to be a cost-efficient and rapid technique, suitable for use in food microbiological laboratory.

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Rapid and reliable identification of food-borne yeasts by Fourier-transform infrared spectroscopy

Article Abstract:

Fourier-transform infrared spectroscopy (FT-IR) is used to identify 722 unknown yeast isolates with a 97.5% accuracy based on a reference spectrum library constructed from 332 defined yeast strains. The FT-IR signature of dried yeast suspensions were used to derive informative windows in the spectrum that formed the basis of the reference spectrum library. FT-IR offers an easy, rapid and accurate method of food-borne yeast identification.

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Inhibition of Listeria monocytogenes by food-borne yeasts

Article Abstract:

A study is conducted to know whether food-borne yeast has an inhibitory potential for the growth of pathogenic bacteria in food. It is observed that inhibition of Listeria monocytogenes simulates the conditions and contamination rates present on smear cheese surface.

Food preparations, not elsewhere classified, All Other Miscellaneous Food Manufacturing, Yeast, Bacteria, Pathogenic, Pathogenic bacteria

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