An emphatic orthogonal signal correction-support vector machine method for the classification of tissue sections of endometrial carcinoma by near infrared spectroscopy

Abstract

A new application of emphatic orthogonal signal correction (EOSC) for baseline correction of near infrared spectra from reflectance measurements of tissue sections is introduced. EOSC was evaluated and compared with principal component orthogonal signal correction (PC-OSC) by using support vector machine (SVM) classifiers. In addition, some exemplary synthetic data sets were created to characterize EOSC coupled to SVM for classification. Orthogonal experimental design coupled with analysis of variance (ANOVA) was used to determine the significant parameters for optimization, which were the OSC method and number of components for the model. EOSC combined with the SVM gave better predictions with respect to a larger number of components and was not as susceptible to overfitting the data as the classifier built with PC-OSC data. These results were supported by simulations using synthetic data sets. EOSC is a softer signal correction approach that retains more signal variance which was exploited by the SVM. Classification rates of 93±1% were obtained without orthogonal signal correction with the SVM. PC-OSC and EOSC data gave similar peak prediction accuracies of 94±1%. The key advantages demonstrated by EOSC were its resistance to overfitting, fine-tuning capability or softness, and the retention of spectral features after signal correction.