Multivariate data analysis and bivariate regression studies applied to comparison of two multi-elemental methods for analysing wine samples
Two inductively coupled plasma mass spectrometry (ICP-MS) methods which permit multielemental analysis in wine samples have been compared following two strategies. First, a multivariate tool based on principal component analysis (PCA) was employed for a global (all analytes) qualitative comparison of the two methods. A single plot based on the confidence limits of the Q and T-2 PCA model statistics corresponding to the 'standard' method results (calibration set) was used to check the comparability of the 'candidate' method (test samples). The residual matrix (after test matrix interpolation into the PCA model) gives qualitative information about the nature of the main errors. This approach is compatible with the presence of missing data. Second, comparison of the methods, analyte by analyte, based on bivariate least squares (BLS) regression was used. This approach, which uses the uncertainty information from both methods and performs a joint test for the intercept and slope parameters, permits appropriate comparison when the two methods have non-constant standard deviations. Copyright (C) 2002 John Wiley Sons, Ltd.
icp-ms principal component analysis method validation comparison of two methods wine analysis linear regression