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Spectral identification on flavonoid classes based on PCA technique
International Islamic University Malaysia
xv, 106 leaves : colour illustrations ; 30cm.
Bioactive compounds, the natural products that mostly found in fruits and vegetables, has shown an increasing demand as one of the important sources especially for medicinal, pharmaceutical and food application. Their molecular structures shown to provide numerous biological activities such as antioxidant, anti-inflammatory and antiviral that increase the nutritional value of food. Hence, act as therapeutic agent for medicinal application and leads to discoveries of new candidates for drug formulation for pharmaceutical application. Increasing numbers of researches have been performed on the extraction, isolation and identification of bioactive compounds; however none has explored on the classification of flavonoid classes. Currently, Fourier Transform Infrared spectroscopy (FTIR) was used for the identification of numerous molecules due to its advantages such as rapid and sensitive technique for the sample analysis. The multidimensional data provided by the FTIR spectra however requires chemometric techniques; the multivariate data analysis to extract the underlying features. Principal component analysis (PCA) is one of multivariate data analysis that shown to provide successive discrimination of numerous samples. Therefore, the aim of this study was to develop a classification algorithm for rapid identification of a number of flavonoid subclasses based on their FTIR spectral data using the PCA application. The use of unfolding data matrix (multistaged PCA) of the spectral data provides the successive application of PCA to obtain the variability within the flavonoids under particular spectral region that significant for their classification. The results showed that the segmentation of the spectral band differentiates each class of the flavonoids according to two factors; their subgroups focusing on heterocyclic ring C (1) and also its molecular structure activity (2). The functional group comprises C=O (carbonyl group), C=C-C (saturation group), C-H (alkane group). Fingerprint region were found to be significant classification based on their heterocyclic ring C; and O-H (hydroxyl group) for molecular structure activity relationship. This selected functional group that shows the explained variance higher than 80% and clear clustering on score plot using the PCA algorithm constitute to the significant factors for the classification of flavonoids classes hence constitute to the spectral signature of flavonoid classes. The study concluded that developed algorithm provides a rapid, simple and less expensive technique for the sample analysis of other bioactive compounds.
Thesis (MSBTE)--International Islamic University Malaysia, 2018.
Includes bibliographical references (leaves 96-103).