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Quantile BEAST Attacks the False-Sample Problem in Near-Infrared Reflectance Analysis

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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.

Keywords: False sample; Near-infrared; Qualitative analysis

Document Type: Research Article


Affiliations: 1: Department of Chemistry, Indiana University, Bloomington, Indiana 47405-4001; present address: College of Pharmacy, University of Kentucky, Lexington, KY 40536-0082 2: Department of Chemistry, Indiana University, Bloomington, Indiana 47405-4001

Publication date: November 1, 1988

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