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. Sampling was carried out directly from the surface to be tested, without the need for sample preparation such as purification, singulation, centrifugation and washing steps, as an efficient and inexpensive blotting technique using the sample carrier. IR spectra were recorded directly after the blotting from the surface of the sample carrier without any further pretreatments. A combination of data preprocessing, principal component analysis and canonical discriminant analysis was found to be suitable. The spectral range from 400 to 1750 cm of the IR-microspectrosopic data was determined to be highly sensitive to the time after incubation and sample thickness, resulting in a high standard deviation. Therefore, this area was excluded from the evaluation in favor of the meaningfulness of the chemometric model and, thus, only the spectral range of specific -CH/-NH/-OH excitations (2815-3680 cm) was used for model development. This study showed that the differentiation of food-related microorganisms on genera, species and strain level is feasible. A leave-one-out cross-validation of the training data set showed 100% accuracy. The classification of the ungrouped test data showed with an accuracy of 94.5% that, despite the large biological variance of the analytes such as different times after incubation and the presented sampling (including its variance), a robust and meaningful model for the differentiation of food-related bacteria could be developed by data preprocessing and subsequent chemometric evaluation.