Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/−)ESI/MS and RPLC/UV data: Application on green tea extracts - Iorgulescu, E.; Voicu, V.A.; Sârbu, C.; Tache, F.; Albu, F.; Medvedovici, A. (Talanta (Oxford), 2016)
Keywords: (+/−) ESI-MS; Algorithms; atmospheric pressure; chemometrics; Chromatography, High Pressure Liquid; Chromatography, Reverse-Phase; Cluster Analysis; Correlation coefficients; Data processing; Discrimination; ethanol; Green tea; Green teas; Influence of data pre-processing; Influence of the experimental variability; Informatics; ionization; Linear Models; Linear regression analysis; liquids; Mass spectra; mass spectrometry; monitoring; Multivariate methods; Plant Extracts - chemistry; Principal Component Analysis; Regression; Regression analysis; reversed-phase liquid chromatography; RPLC-UV; Slopes; Spectrometry, Mass, Electrospray Ionization; Spectrophotometry, Ultraviolet; Statistics as Topic - methods; Tea - chemistry; Linear regression analysis; Green teas; Multivariate methods; RPLC-UV; (+/−) ESI-MS; Influence of the experimental variability; Influence of data pre-processing