Improved classification of fused data: Synergetic effect of partial least squares discriminant analysis (PLS-DA) and common components and specific weights analysis (CCSWA) combination as applied to tomato profiles (NMR, IR and IRMS)

摘要

Discriminant analysis (DA) methods are well-known chemometric approaches for solving classification problems in chemistry. Recently, specific multiblock methods, such as common components and specific weights analysis (CCSWA), have been developed which make it possible to enhance the quality of the classification models, by combining data from different analytical platforms.