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Discrimination and classification of eight strains related to meat spoilage microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal Components Analysis (PCA) and Multi-Class Support Vector Machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial genera and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC’s from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.
The freshness changes in poultry fillets during storage were studied using a portable fiber-optic Raman spectrometer. Poultry fillets with the same storage life (9 days) and expiry date were purchased from a local store and stored at 4 °C. Their Raman spectra were measured on a daily basis up to day 21 using a QE Pro-Raman spectrometer with a laser excitation wavelength of 785 nm. The complex spectra were analyzed using Principal Components Analysis (PCA), which resulted in a separation of the samples into three quality classes according to their freshness: fresh, semi-fresh, and spoiled. These classes were based on and similar to the information inferred from the product label on the packages of poultry fillets. The PCA loadings revealed a decrease in the protein content of the poultry meat during spoilage, an increase in the formation of free amino acids, an increase in oxidation of amino acid residues, and an increase in microbial growth on the surface of the poultry fillets, as well as revealing information about hydrophobic interaction around the aliphatic residues. Similar groupings (fresh, semi-fresh, and spoiled) were also obtained from the results of an Agglomerative Hierarchical Cluster Analysis (AHCA) of the first five principal components. The results allow the conclusion that the portable fiber-optic Raman spectrometer can be used as a reliable and fast method for real-time freshness evaluation of poultry during storage.
