Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy.

As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models.

Development of Gold Nanoparticle-Based SERS Substrates on TiO-Coating to Reduce the Coffee Ring Effect.

Hydrophilic surface-enhanced Raman spectroscopy (SERS) substrates were prepared by a combination of TiO-coatings of aluminium plates through a direct titanium tetraisopropoxide (TTIP) coating and drop coated by synthesised gold nanoparticles (AuNPs). Differences between the wettability of the untreated substrates, the slowly dried Ti(OH) substrates and calcinated as well as plasma treated TiO substrates were analysed by water contact angle (WCA) measurements. The hydrophilic behaviour of the developed substrates helped to improve the distribution of the AuNPs, which reflects in overall higher lateral SERS enhancement.

Investigation and Rapid Discrimination of Food-Related Bacteria under Stress Treatments Using IR Microspectroscopy.

Because the robust and rapid determination of spoilage microorganisms is becoming increasingly important in industry, the use of IR microspectroscopy, and the establishment of robust and versatile chemometric models for data processing and classification, is gaining importance. To further improve the chemometric models, bacterial stress responses were induced, to study the effect on the IR spectra and to improve the chemometric model. Thus, in this work, nine important food-relevant microorganisms were subjected to eight stress conditions, besides the regular culturing as a reference.

Rapid detection and discrimination of food-related bacteria using IR-microspectroscopy in combination with multivariate statistical analysis.

Spoilage microorganisms are of great concern for the food industry. While traditional culturing methods for spoilage microorganism detection are laborious and time-consuming, the development of early detection methods has gained a lot of interest in the last decades. In this work a rapid and non-destructive detection and discrimination method of eight important food-related microorganisms (Bacillus subtilis DSM 10, Bacillus coagulans DSM 1, Escherichia coli K12 DSM 498, Escherichia coli TOP10, Micrococcus luteus DSM 20030, Pseudomonas fluorescens DSM 4358, Pseudomonas fluorescens DSM 50090 and Bacillus thuringiensis israelensis DSM 5724) based on IR-microspectroscopy and chemometric evaluation was developed.

Detection of spoilage associated bacteria using Raman-microspectroscopy combined with multivariate statistical analysis.

Raman-Microspectroscopy with subsequent chemometric evaluation was used for the rapid and non-destructive differentiation of seven important spoilage related microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Pseudomonas fluorescens DSM 50090, Micrococcus luteus, Escherichia coli HB101, Escherichia coli TOP10 and Bacillus thuringiensis israelensis DSM 5724. Therefore fast collected spectra directly from rapid surface blots without any pretreatments like purification or singulation steps were used. To estimate and classify the Raman-spectroscopic data at genera and strain level an adequate preprocessing together with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis was used.