Detection of poultry meat specific bacteria using FTIR spectroscopy and chemometrics

FTIR spectra of poultry meat specific bacteria viz. Salmonella enteritidis, Pseudomonas ludensis, Listeria monocytogenes and Escherichia coli were collected and investigated for identification of spectral windows capable of bacterial classification and quantification. Two separate datasets obtained at different times were used in the study to check reproducibility of results. Multivariate data analysis techniques viz. principal component analysis (PCA), partial least-squares discriminant analysis (PLSDA) and soft independent modelling of class analogy (SIMCA) were used in the analysis. Using full cross-validation and separate calibration and prediction datasets, the highest correct classification results for SIMCA and PLSDA were achieved in spectral window (1800-1200 cm-1) for both datasets. The window was also tested then for quantification of different bacteria and it had been observed that PLS models had better R values for classification (R = 0.984) than predicting various concentration levels (R = 0.939) of all four poultry specific bacteria inoculated in distilled water. The identified spectral window 1800-1200 cm-1 also demonstrated potential for 100% correct classification of chicken salami samples contaminated with S. enteritidis and P. ludensis from control using SIMCA. However, this wavenumber range yielded few misclassifications using PLS-DA approach. Thus FTIR spectroscopy in combination with chemometrics is a powerful technique that can be developed further to differentiate bacteria directly on poultry meat surface.