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The Rametrix™ PRO Toolbox v1.0 for MATLAB®.

Ryan S Senger1,2,3, John L Robertson3,4,5

  • 1Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, United States of America.

Peerj
|January 15, 2020
PubMed
Summary

The Rametrix™ PRO Toolbox enhances chemometric analysis of spectral data by evaluating model predictive capabilities. This new tool achieved 100% accuracy in detecting chronic kidney disease (CKD) using Raman spectroscopy.

Keywords:
Discriminant analysisMATLABNephrologyPredictionPrincipal component analysisRaman spectroscopySpectral data analysisUrine

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Data Analysis

Background:

  • Vibrational spectroscopy tools analyze molecular composition.
  • Rametrix™ LITE Toolbox uses discriminant analysis of principal components (DAPC) for spectral classification.
  • Existing tools lack robust evaluation of predictive capabilities for unknown samples.

Purpose of the Study:

  • Introduce Rametrix™ PRO Toolbox v1.0 for enhanced chemometric analysis.
  • Provide functionality to evaluate predictive capabilities of DAPC models with unknown samples.
  • Validate Rametrix™ LITE models for chronic kidney disease (CKD) detection.

Main Methods:

  • Rametrix™ PRO Toolbox v1.0 built for MATLAB®, compatible with Rametrix™ LITE Toolbox v1.0.
  • Performs leave-one-out analysis of chemometric DAPC models.
  • Reports accuracy, sensitivity, and specificity for model evaluation.
  • Used to validate Rametrix™ LITE models for CKD detection in urine Raman spectra.

Main Results:

  • The number of spectral principal components (PCs) significantly impacted model performance.
  • Using 35 PCs in the DAPC model achieved 100% accuracy, sensitivity, and specificity.
  • Models with fewer or more PCs showed reduced performance.
  • Rametrix™ PRO demonstrated value in evaluating Rametrix™ LITE models.

Conclusions:

  • Rametrix™ PRO Toolbox effectively evaluates chemometric DAPC models.
  • Optimal PC selection is crucial for accurate spectral classification.
  • The validated models show promise for non-invasive CKD detection using Raman spectroscopy.