Jaafreh, Sawsan: Raman Spectroscopy in Tandem with Chemometric Methods for the Characterization and Analysis of Quality and Shelf Life of Poultry Meat. - Bonn, 2020. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-59468
@phdthesis{handle:20.500.11811/8555,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-59468,
author = {{Sawsan Jaafreh}},
title = {Raman Spectroscopy in Tandem with Chemometric Methods for the Characterization and Analysis of Quality and Shelf Life of Poultry Meat},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2020,
month = aug,

note = {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 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.
Using the same portable fiber-optic Raman spectrometer; the freshness changes in poultry fillets during storage were studied. 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. The complex spectra were analysed using PCA, which resulted in a separation of the samples into three quality classes according to their freshness: fresh, semi-fresh, and spoiled. 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.
Further, the characterization and discrimination of fillets samples from different poultry meat production lines (conventional and alternative) of a German poultry producer were successfully accomplished using portable fiber-optic Raman spectrometer in tandem with chemometric analysis (PCA, Canonical Discriminant Analysis (CDA) and AHCA). The investigations were conducted at five repeated investigation times during storage started 24 h after slaughter at 0, 72, 120, 168 and 240 h of the experiment. A total of 80 fillets were investigated in two repeated storage trials under the same conditions. PCA model was constructed using the 1st storage trial (1st investigation time; 0 h). The model was able to group the poultry samples according to their production line into two classes: conventional and alternative. The testing data points from the 2nd storage trial (1st investigation time; 0 h) were used to validate the model and all have been successfully assigned to the correct cluster. Similar results were also obtained from CDA and AHCA models. The origin of the separation in PCA model was investigated by analysing the loading plots. Moreover, CDA models were constructed for each production line to classify poultry fillets according to their storage time (five investigation times) and their microbial load (three quality classes). The 1st storage trial was used to build the models and the 2nd storage trial was used to validate these models. For both production lines, all constructed CDA models showed good ability to classify poultry fillets according to their storage time and to their microbial load with error rates less than 25.00%. However, the classification ability of the constructed CDA models showed different results when tested with the 2nd storage trial. For the classification according to the storage time, CDA models showed poor classification ability for both production lines. The high error rates could be correlated to the high variations of the bacterial load between the two storage trials for each production line. For the classification according to the microbial load, CDA models classification ability was good for the conventional production line (error rate: 24.60%) and poor for the alternative production line (error rate: 54.33%). The low error rate for the conventional production line indicates that the variations between the two storage trials were low. While the high error rate for alternative production line indicates that the variations between the two storage trials were too high and that the microbial load is not the only factor that has an impact on the collected Raman spectra from the two storage trials.},

url = {http://hdl.handle.net/20.500.11811/8555}
}

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