Volltext-Downloads (blau) und Frontdoor-Views (grau)

Raman Spectroscopy in Tandem with Chemometric Methods for the Characterization and Analysis of Quality and Shelf Life of Poultry Meat

  • 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.

Export metadata

Additional Services

Search Google Scholar Check availability


Show usage statistics
Document Type:Doctoral Thesis
Author:Sawsan Jaafreh
Referee:Klaus Günther, Matthias Wüst
Place of publication:Bonn
Date of exam:2020/08/12
Contributing Corporation:Rheinische Friedrich-Wilhelms-Universität Bonn
Date of first publication:2020/08/27
Departments, institutes and facilities:Fachbereich Angewandte Naturwissenschaften
Dewey Decimal Classification (DDC):5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften
Entry in this database:2020/08/29