Multiset Independent Component Regression (MsICR) Based Statistical Data Analysis and Calibration Modeling

In the present work, multiple mixture spectra data sets are given an integrated analysis for the estimation of substance compositions. Different spectral profiles are collected for the same mixtures resulting from the influences of "interference factors", such as the variations in environmental, instrumental, and sample conditions. A multiset independent component regression (MsICR) algorithm is developed, where the relationship across multiple spectral spaces is studied from a quality-relevant viewpoint for both source extraction and regression modeling. The common systematic information shared by all spectral data sets is separated from the part that is influenced by "interference factors". Since the common structure reflects information that is not influenced by "interference factors", it is more informative and responsible to estimate substance compositions in the mixtures. A calibration model is then developed based on the decomposition result for composition estimation. This approach presents a new viewpoint of spectral data analysis and provides comprehensive statistical explanations of inherent information of multiple mixture spectral data. The feasibility and performance of the proposed method are illustratively compared with other methods based on spectral data from laboratory experiments. © 2013 American Chemical Society