Optimal modeling pattern of variables selection on analog complex using UVE-PLS regression

This study aimed to determine the composition of chemical complex by partial least square (PLS) regression models combined with uninformative variable elimination (UVE). The near-infrared (NIR) spectra of the forty samples were determined and then UVE was used to compress full NIR spectra from 12011 redundant variables to dozens of variables. Finally, 54, 16, 27, 31 and 42 variables were selected by UVE for 2,2,4-Trimethylpentane, Heptane, Cyclohexane, Ethyl formate and Butyl acetate respectively. Selected variables were used as the inputs of PLS model for quantitative analysis which made the prediction of the model more robust and accurate compared with the conventional PLS.

• Publication year

  2020

• Venue

  IOP SciNotes

• Publication date

  2020-06-04

• Fields of study

  Mathematics, Physics, Chemistry

• Identifiers
  DOI 10.1088/2633-1357/ab8d46
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Prediction of dissolution profiles by non-destructive NIR spectroscopy in bilayer tablets.

  2019cited by this paper
• Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

  2019cited by this paper
• Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices

  2018cited by this paper
• Multispectral Imaging for Plant Food Quality Analysis and Visualization.

  2018cited by this paper
• Using elastic net regression to perform spectrally relevant variable selection

  2018cited by this paper
• Matching portable NIRS instruments for in situ monitoring indicators of milk composition

  2017cited by this paper
• Selection of robust variables for transfer of classification models employing the successive projections algorithm.

  2017cited by this paper
• Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.

  2017cited by this paper
• Understanding the thermal stability of human serum proteins with the related near-infrared spectral variables selected by Monte Carlo-uninformative variable elimination

  2017cited by this paper
• Direct determination of polymerised triacylglycerides in deep-frying vegetable oil by near infrared spectroscopy using Partial Least Squares regression

  2012cited by this paper
• Rapid detection of Rosa laevigata polysaccharide content by near-infrared spectroscopy.

  2011cited by this paper
• Measurement of total flavone content in snow lotus (Saussurea involucrate) using near infrared spectroscopy combined with interval PLS and genetic algorithm.

  2010cited by this paper
• Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy.

  2008cited by this paper
• Near-infrared spectroscopy applications in pharmaceutical analysis.

  2007cited by this paper
• A complete computer monitoring and control system using commercially available, configurable software for laboratory and pilot plantEscherichia coli fermentations

  1996cited by this paper
• Elimination of uninformative variables for multivariate calibration.

  1996cited by this paper

• Study on Color Detection of Korla Fragrant Pears by Near-Infrared Spectroscopy Combined with PLSR

  2025cites this paper
• Characteristic wavelength selection based on multi-strategy fusion zebra optimization algorithm for PLSR

  2025cites this paper
• Blue-light-excited broadband near-infrared garnet Ca3Zr2Ga2SiO12:Cr3+/Yb3+ phosphors for high-performance pc-LED applications

  2025cites this paper
• A rapid calibration method for the discrete element model of straw fodder particle mixtures based on the UVE-PLS-GD algorithm

  2025cites this paper