Study On Detection Of Listeria Monocytogenes By Surface Enhanced Raman Spectroscopy

Listeria monocytogenes can cause bacterial food poisoning by meat, dairy products and other. It is harmful to human health and poses an important threat to public security. The development of fast and reliable sensing techniques to detect food-borne microorganisms is an indispensable key to food industry and healthcare. In recent years, surface enhanced Raman spectroscopy has been widely used in agriculture, food industry, chemistry industry. A standard protocol was established for detection of food-borne microorganisms by Raman spectroscopic technique.Before the experiment, all the tested strains were identified accurately based on their 16SrDNA gene sequences. After performing HCA we determined SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium. Unsupervised PCA and HCA models and supervised DA-based chemometric models were used to differentiate four common food-borne microorganisms (Staphylococcus strain, E.coli type strain, Salmonella type strain and Listeria strains) between Raman spectra of Gram-positive and Gram-negative bacteria, at also differentiates bacteria at genus and species level. Then, we evaluated Raman spectroscopy in combination with chemometric analysis to identify six closely related species within the Listeria genus (including the human pathogen Listeria monocytogenes) from broth and directly from milk. Here, we also used partial least-squares regression model to discriminate and quantify the actual concentration of a specific Listeria strain in a bacterial mixture. The results show that, culture conditions had greater influence on the Raman spectroscopy of bacteria; Raman spectroscopy in combination with DA-based chemometric model (Distance discriminant analysis,97.4%; and Bayes discriminant analysis,98.8%) can classified four common food-borne microorganisms at three levels; Raman spectroscopy in combination with chemometric analysis to identify six closely related species within the Listeria genus (including the human pathogen Listeria monocytogenes) directly from milk. Average identification accuracies of 97.78%,98.33% for Listeria genus from medium and 95.28%,96.11% for Listeria genus from milk were obtained while analyzing the single-cell Raman spectra via two DA-based chemometric models respectively. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify(regression coefficient,> 0.97; root mean square error,< 0.6) the actual concentration of a specific Listeria strain in a bacterial mixture.