A novel OSC-KPCA based pattern analysis method was proposed to improve the clustering and predictive performance of the metabolomics.The strong nonlinear pattern recognition power and the predominance in dealing with small high-dimensional data of the Kernel Principal Component Analysis(KPCA)were used here to analyze four genotypes of the important model plant—Arabidopsis thaliana.In order to improve the performance of PR(Pattern Recognition),the Orthogonal Signal Correction(OSC)method was used to filter the original data firstly to eliminate the interference of noise.The PR results showed that the OSC-KPCA based PR method could reveal the underlying relationship between genotypes and metabolites successfully.The paternal genotypes(Co10 and C24)and the two F1 progeny C24×Co10 and Co10×C24 could be 100% correctly discriminated.More importantly the predictability was also as high as 100%.
