Calibration transfer employing univariate correction and robust regression

Galvão, Roberto Kawakami Harrop; Soares, Sófacles Figueredo Carreiro; Martins, Marcelo Nascimento; Pimentel, Maria Fernanda; Araújo, Mário César Ugulino de

doi:10.1016/j.aca.2014.10.001

Cited by 20 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method was reported to compare favorably with PDS as a method of transfer for octane and naphthenes in gasoline and for oil in corn. 27…”

Section: Filter Instrument Calibration Transfermentioning

confidence: 99%

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Workman

2017

Appl Spectrosc

140

View full text Add to dashboard Cite

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.

show abstract

“…The proposed method was reported to compare favorably with PDS as a method of transfer for octane and naphthenes in gasoline and for oil in corn. 27…”

Section: Filter Instrument Calibration Transfermentioning

confidence: 99%

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Workman

2017

Appl Spectrosc

140

View full text Add to dashboard Cite

show abstract

“…Calibration transfer is one of the most commonly applied means of compensating for spectral variations, which may be caused by many factors such as the surface texture, granularity, and change or aging of instruments [ 17 ]. The multivariate transfer calibration technology of direct standardization (DS) has been successfully applied to the quantitative analysis of creatinine in serum samples by transferring the calibrations of two image analysis instruments [ 18 ] and spectral matching and combining of nuclear magnetic resonance spectral data set from different instruments [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative visualization of lignocellulose components in transverse sections of moso bamboo based on FTIR macro- and micro-spectroscopy coupled with chemometrics

Liu

Wei

et al. 2018

Biotechnol Biofuels

110

View full text Add to dashboard Cite

BackgroundDue to the increasing demands of energy and depletion of fossil fuel, bamboo is considered to be one of the most important renewable biological resources on the basis of its advantages of rapid growth ability and rich reserves. Cellulose, hemicellulose, and lignin are the three most important constituents in moso bamboo. Their concentrations and, especially, their microscopic distributions greatly affect their utilization efficiency and other physical properties as a biomass resource. However, no studies have achieved a quantitative visualization of the distribution of lignocellulose concentrations in transverse sections of bamboo. Therefore, this study proposed the use of quantitative multivariate spectral analysis to reveal the micro-chemical distribution of lignocelluloses in bamboo based on an integration of FTIR macro- and micro-spectroscopic imaging techniques.ResultsMultivariate calibration models for the quantitative determination of lignocelluloses of bamboo were developed based on FTIR macro-spectroscopy, and the quantitative calibration models based on the FTIR characteristic bands showed an excellent performance with determination coefficients of 0.933, 0.878, and 0.912 for cellulose, hemicellulose, and lignin, respectively. These quantitative models were then utilized to the FTIR micro-spectroscopy of bamboo transverse sections which were corrected using a direct standardization algorithm. Subsequently, the micro-chemical distributions of cellulose, hemicellulose, and lignin were obtained based on the integration of the multivariate calibration models and corrected FTIR micro-spectroscopy. The combination of the multivariate calibration models and calibration transfer algorithm resulted in a final quantitative visualization of the chemical distributions of lignocelluloses in moso bamboos.ConclusionsIntegration of the FTIR macro- and micro-spectroscopic imaging techniques can provide comprehensive information that can be used to exploit the resource of moso bamboo to develop biofuels and biosynthetic materials.

show abstract

“…33 Other methods, such as calibration transfer functions, have been tested to move calibration models from one location/instrument to another without requiring the expensive, time-consuming, and laborious task of running the entire training and validation sets under the new conditions. 34,35 This is especially challenging when the instrument used for the data acquisition is not the same as the one used for the calibration model and analytical conditions (e.g., temperature and humidity) are not comparable. Thus, we hypothesize that successfully implementing calibration transfer functions in hot cell environments may be unlikely.…”

Section: Introductionmentioning

confidence: 99%

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

et al. 2021

View full text Add to dashboard Cite

Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0–1 M) and nitric acid (0.1–10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

show abstract

Calibration transfer employing univariate correction and robust regression

Cited by 20 publications

References 23 publications

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Quantitative visualization of lignocellulose components in transverse sections of moso bamboo based on FTIR macro- and micro-spectroscopy coupled with chemometrics

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

Contact Info

Product

Resources

About