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                            Library & Multimedia
 Workflow for the Supervised Learning of Chemical Data: Efficient Data Reduction-Multivariate Curve Resolution (EDR-MCR)
NCJ Number
              304014
Journal

*Analytical Chemistry* Volume: 93 Issue: 12 Dated: 2021 Pages: 5020-5027
Author(s)
          S. Beyramysoltan;
                        et al
Date Published

2021
Length
              8 pages
Annotation
              The reported project tested the performance of the efficient data reduction (EDR) and supervised multivariate curve resolution (MCR) methods for their ability to enable discrimination between the constituents of two benchmark and two high-dimensional data sets.
Abstract
              A new method termed efficient data reduction-multivariate curve resolution (EDR-MCR) has been devised for classification of high-dimensional data. The method introduces the coupling of EDR and MCR as a new strategy for data splitting, variable selection, and supervised classification of high dimensionality data. The method reduces data dimensionality and selects the training set using principal component analysis (PCA) and convex geometry prior to data classification. Then, the reduced data are categorized using an MCR model, in which numerical constraints are imposed to resolve the data into classes and readily interpretable pure component signal weights. The results of the current project were compared with the output of the application of different data splitting methods, including iterative random selection (IRS), Kennard–Stone (KS), and discrimination methods, including partial least-squares-discriminant analysis (PLS-DA) and the ensemble-learning frameworks of linear discriminant analysis (LDA), k-nearest neighbors (KNN), classification and regression trees (CART), and support vector machine (SVM). Overall, EDR resulted in comparable results with other data splitting methods despite the small size of the training set samples that it created. The proposed MCR approach, in comparison with other commonly used supervised techniques, has the advantages of speed in implementation, tuning of fewer parameters, flexibility in the analysis of data characterized by low sample numbers and class imbalances, improved accuracy from the inclusion of additional system information in the form of numerical constraints, and the ability to resolve pure components signal weights. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2015-DN-BX-K057
                                2018-R2-CX-0012
                                2019-DU-BX-0026


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Award
        Development of Ambient Ionization Mass Spectrometric and Multivariate Statistical Analysis Methods for Rapid High Throughput Analysis and Identification of Psychotropic Plant Species
                                Detection and Identification of Psychoactive Material Based On SPME-facilitated DART-MS-derived Headspace Chemical Signatures and Chemometrics
                                Research to Develop Validated Methods for THC Quantification in Complex Matrices by High-resolution DART-MSFocus on Edibles and Plant Materials
    
  
  
Date Published: January 1, 2021
    
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