Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8251341 | Radiation Physics and Chemistry | 2018 | 8 Pages |
Abstract
The evaluation of the γ-irradiation effects on green teas was holistically achieved by means of a novel algorithm based on linear regression (LRA). This algorithm was compared to the discrimination power of Principal Component Analysis (PCA) and Cluster Analysis (CA). The holistic evaluation was based on positive or negative ion monitoring Electrospray Mass Spectrometry (+/âESI/MS) data and Reversed Phase Liquid Chromatography with Ultraviolet Spectrometry detection (RPLC/UV) chromatograms, without involving any structural attribution and/or assay of the existing components. Five types of green teas (receiving irradiation doses of 0, 10 and 25â¯kGy) were considered. Extraction in ethanol and heated water was used. To ensure an increased definition of the profiles being compared, the LRA approach was applied on pairs of large experimental data series resulting from high frequency/high resolution acquisition rates, the resulting slopes, intercepts and correlation coefficients being considered as variables retaining the information contained in the raw data. The discrimination ability varied in the following order: LRA > CA > PCA. The information contained by the input data varied as following: (+)ESI/MS spectra > (-)ESI/MS spectra > RPLC/UV chromatograms.
Keywords
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Radiation
Authors
Elena Iorgulescu, Victor A. Voicu, Costel Sârbu, Florentin Tache, Florin Albu, Ioana StÄnculescu, Andrei Medvedovici,