Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 42,
  • Issue 8,
  • pp. 1351-1365
  • (1988)

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

Not Accessible

Your library or personal account may give you access

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 (<u>B</u>ootstrap <u>E</u>rror-<u>A</u>djusted <u>S</u>ingle-sample <u>T</u>echnique) 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.

PDF Article
More Like This
Multispectral system for reflectance reconstruction in the near-infrared region

Meritxell Vilaseca, Jaume Pujol, Montserrat Arjona, and Marta de Lasarte
Appl. Opt. 45(18) 4241-4253 (2006)

Multicomponent blood analysis by near-infrared Raman spectroscopy

Andrew J. Berger, Tae-Woong Koo, Irving Itzkan, Gary Horowitz, and Michael S. Feld
Appl. Opt. 38(13) 2916-2926 (1999)

Near infrared spectroscopic analysis of single malt Scotch whisky on an optofluidic chip

Praveen C. Ashok, Bavishna B. Praveen, and K. Dholakia
Opt. Express 19(23) 22982-22992 (2011)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved