Literature DB >> 28929781

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy.

Jerome J Workman1,2.   

Abstract

Calibration transfer for use with spectroscopic instruments, particularly for near-infrared, infrared, and Raman analysis, has been the subject of multiple articles, research papers, book chapters, and technical reviews. There has been a myriad of approaches published and claims made for resolving the problems associated with transferring calibrations; however, the capability of attaining identical results over time from two or more instruments using an identical calibration still eludes technologists. Calibration transfer, in a precise definition, refers to a series of analytical approaches or chemometric techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, for two or more instruments, with statistically retained accuracy and precision. Ideally, one would develop a single calibration for any particular application, and move it indiscriminately across instruments and achieve identical analysis or prediction results. There are many technical aspects involved in such precision calibration transfer, related to the measuring instrument reproducibility and repeatability, the reference chemical values used for the calibration, the multivariate mathematics used for calibration, and sample presentation repeatability and reproducibility. Ideally, a multivariate model developed on a single instrument would provide a statistically identical analysis when used on other instruments following transfer. This paper reviews common calibration transfer techniques, mostly related to instrument differences, and the mathematics of the uncertainty between instruments when making spectroscopic measurements of identical samples. It does not specifically address calibration maintenance or reference laboratory differences.

Keywords:  Calibration; alignment; bias and slope; method comparison; multivariate; transfer; uncertainty

Year:  2017        PMID: 28929781     DOI: 10.1177/0003702817736064

Source DB:  PubMed          Journal:  Appl Spectrosc        ISSN: 0003-7028            Impact factor:   2.388


  6 in total

1.  Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards.

Authors:  Yuhui Zhao; Ziheng Zhao; Peng Shan; Silong Peng; Jinlong Yu; Shuli Gao
Journal:  Molecules       Date:  2019-05-09       Impact factor: 4.411

2.  Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?

Authors:  Shree R S Dangal; Jonathan Sanderman
Journal:  Sensors (Basel)       Date:  2020-11-25       Impact factor: 3.576

Review 3.  The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing.

Authors:  Karen A Esmonde-White; Maryann Cuellar; Ian R Lewis
Journal:  Anal Bioanal Chem       Date:  2021-10-20       Impact factor: 4.142

4.  Prediction approach of larch wood density from visible-near-infrared spectroscopy based on parameter calibrating and transfer learning.

Authors:  Zheyu Zhang; Yaoxiang Li; Ying Li
Journal:  Front Plant Sci       Date:  2022-10-04       Impact factor: 6.627

5.  On-site substrate characterization in the anaerobic digestion context: A dataset of near infrared spectra acquired with four different optical systems on freeze-dried and ground organic waste.

Authors:  Margaud Pérémé; Alexandre Mallet; Lorraine Awhangbo; Cyrille Charnier; Jean-Michel Roger; Jean-Philippe Steyer; Éric Latrille; Ryad Bendoula
Journal:  Data Brief       Date:  2021-05-11

6.  SmartSpectrometer-Embedded Optical Spectroscopy for Applications in Agriculture and Industry.

Authors:  Julius Krause; Heinrich Grüger; Lucie Gebauer; Xiaorong Zheng; Jens Knobbe; Tino Pügner; Anna Kicherer; Robin Gruna; Thomas Längle; Jürgen Beyerer
Journal:  Sensors (Basel)       Date:  2021-06-30       Impact factor: 3.576

  6 in total

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