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aIn some applications of diffuse reflectance spectroscopy there may be substantial variability between the spectra from replicate measurements of what is nominally the same sample. A method called error reduction by orthogonal subtraction (EROS) is proposed to ameliorate the effects of this. The first step is to use principal component analysis (PCA) to identify the structure in the variability of replicate measurements. This is followed by subtraction of the modelled effects from the original spectral data matrix X by projection onto the subspace orthogonal to factors derived from the PCA. An application to the clinical diagnosis of colon lesions is presented, in which pre-treatment of spectra using the proposed method is successful in reducing the complexity and increasing both the accuracy and interpretability of the subsequent classification model.

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Elastic scattering spectroscopy (ESS) may be used to detect high-grade dysplasia (HGD) or cancer in Barrett's esophagus (BE). When spectra are measured in vivo by a hand-held optical probe, variability among replicated spectra from the same site can hinder the development of a diagnostic model for cancer risk. An experiment was carried out on excised tissue to investigate how two potential sources of this variability, pressure and angle, influence spectral variability, and the results were compared with the variations observed in spectra collected in vivo from patients with Barrett's NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript esophagus. A statistical method called error removal by orthogonal subtraction (EROS) was applied to model and remove this measurement variability, which accounted for 96.6% of the variation in the spectra, from the in vivo data. Its removal allowed the construction of a diagnostic model with specificity improved from 67% to 82% (with sensitivity fixed at 90%). The improvement was maintained in predictions on an independent in vivo data set. EROS works well as an effective pretreatment for Barrett's in vivo data by identifying measurement variability and ameliorating its effect. The procedure reduces the complexity and increases the accuracy and interpretability of the model for classification and detection of cancer risk in Barrett's esophagus.