Holistic evaluation of gamma-irradiation effects on green teas: New linear regression based approach applied to (+/-)ESI/MS and RPLC/UV data and comparison with PCA and CA chemometric methods

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 (+/−ESI/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 25 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.