Quantile BEAST (Bootstrap Error-Adjusted Single-Sample Technique) Attacks the False-Sample Problem in Near-Infrared Reflectance Analysis

Abstract

The multiple linear regression approach typically used in near- infrared calibration yields equations in which any amount of reflectance at the analytical wavelengths leads to a corresponding composition value. As a result, when the sample contains a component not present in the training set, erroneous composition values can arise without any indication of error. The Quantile BEAST (Bootstrap Error-Adjusted Single-sample Technique) is described here as a method of detecting one or more 'false' samples. The BEAST constructs a multidimensional form in space using the reflectance values of each training-set sample at a number of wavelengths. New samples are then projected into this space and a confidence test is executed to determine whether the new sample is part of the training-set form. The method is more robust than other procedures because it relies on few assumptions about the structure of the data; therefore, deviations from assumptions do not affect the results of the confidence test.

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Document Details

Document Type
Technical Report
Publication Date
Jul 11, 1988
Accession Number
ADA198214

Entities

People

  • Gary M. Hieftje
  • Robert A. Lodder

Organizations

  • Indiana University

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Benzoic Acids
  • Chemistry
  • Computers
  • Confidence Limits
  • Data Analysis
  • Data Science
  • Distribution Functions
  • Equations
  • Estimators
  • Information Science
  • Military Research
  • Monte Carlo Method
  • Parallel Computing
  • Statistical Algorithms
  • Two Dimensional
  • United States

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  • Regression Analysis.
  • Spectroscopy.

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  • Space