The Rametrix ™ PRO Toolbox v1.0 for MATLAB ®

PeerJ

article

Ryan S. Senger¹  John L. Robertson³ 

[1] Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University;Department of Chemical Engineering, Virginia Polytechnic Institute and State University;DialySensors, Inc.;Department of Biomedical Engineering and Mechanics, Virginia Polytechnic Institute and State University;Carilion School of Medicine and Research Institute, Virginia Polytechnic Institute and State University

关键词: Raman spectroscopy;    MATLAB;    Principal component analysis;    Discriminant analysis;    Spectral data analysis;    Prediction;    Urine;    Nephrology;   

DOI  :  10.7717/peerj.8179

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundExisting tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix™ LITE Toolbox v1.0 for MATLAB® is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when “unknown” samples are introduced. Here, the Rametrix™ PRO Toolbox v1.0 is introduced to provide this capability.MethodsThe Rametrix™ PRO Toolbox v1.0 was constructed for MATLAB® and works with the Rametrix™ LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix™PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix™ PRO was used to validate Rametrix™ LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD.ResultsThe number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix™ PRO in evaluating chemometric models constructed with Rametrix™ LITE.

【 授权许可】

CC BY