scholarly journals Multivariate calibration transfer between two different types of multisensor systems

2017 ◽  
Vol 246 ◽  
pp. 994-1000 ◽  
Author(s):  
Maria Khaydukova ◽  
Cristina Medina-Plaza ◽  
Maria Luz Rodriguez-Mendez ◽  
Vitaly Panchuk ◽  
Dmitry Kirsanov ◽  
...  
Keyword(s):  
Multivariate Calibration ◽  
Calibration Transfer ◽  
Multisensor Systems ◽  
Different Types

Multivariate Calibration Transfer between two Potentiometric Multisensor Systems

2017 ◽  
Vol 29 (9) ◽  
pp. 2161-2166 ◽  
Author(s):  
Maria Khaydukova ◽  
Vitaly Panchuk ◽  
Dmitry Kirsanov ◽  
Andrey Legin
Keyword(s):  
Multivariate Calibration ◽  
Calibration Transfer ◽  
Multisensor Systems

Multisensor systems based on phthalocyanines for monitoring the quality of grapes

2016 ◽  
Vol 20 (08n11) ◽  
pp. 889-894 ◽  
Author(s):  
Maria Luz Rodriguez-Mendez ◽  
Celia García-Hernandez ◽  
Cristina Medina-Plaza ◽  
Cristina García-Cabezón ◽  
Jose Antonio de Saja
Keyword(s):  
State Of The Art ◽  
Layer By Layer ◽  
Carbon Paste ◽  
Electron Mediator ◽  
Multisensor Systems ◽  
Langmuir Blodgett ◽  
Different Types ◽  
Production Stages ◽  
Nanostructured Sensors

Arrays of phthalocyanine-based sensors with complementary activity have been used to develop voltammetric electronic tongues. Such systems have demonstrated to be useful in enology for the evaluation of quality of wines in different production stages, from grapes to bottles. In this paper, the state of the art of multisensor systems based on phthalocyanines dedicated to the analysis of musts (juices obtained from crushed grapes) is described. Such multisensor systems cover different types of sensors from simple Carbon Paste Electrodes, to sophiticated nanostructured sensors, including Langmuir–Blodgett or Layer by Layer thin films and biomimetic biosensors where phthalocyanines play a crucial role as electron mediator between enzymes and electrodes. In all cases, multisensor systems based on phthalocyanines have been able to discriminate musts prepared from different varieties of grapes. The performance of these systems can be improved by combining non-specific sensors with biosensors containing enzymes selective to phenols. In this case, excellent relationships have been found between the responses provided by the array and the content in phenols and acids provided by traditional chemical analysis.

Calibration Transfer for LED-Based Optical Multisensor Systems

ACS Sensors ◽  
2020 ◽  
Vol 5 (8) ◽  
pp. 2587-2595
Author(s):  
Anastasiia Surkova ◽  
Andrey Bogomolov ◽  
Andrey Legin ◽  
Dmitry Kirsanov
Keyword(s):  
Calibration Transfer ◽  
Multisensor Systems

New Hybrid Algorithm for Transferring Multivariate Quantitative Calibrations of Intra-Vendor Near-Infrared Spectrometers

2002 ◽  
Vol 56 (7) ◽  
pp. 877-886 ◽  
Author(s):  
Christine M. Wehlburg ◽  
David M. Haaland ◽  
David K. Melgaard
Keyword(s):  
Least Squares ◽  
Hybrid Algorithm ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Correct Prediction ◽  
Calibration Transfer ◽  
Calibration Models ◽  
Secondary Instrument ◽  
Instrument Response ◽  
Instrumental Drift

A new prediction-augmented classical least-squares/partial least-squares (PACLS/PLS) hybrid algorithm is ideally suited for use in transferring multivariate calibrations between spectrometers. Spectral variations such as instrument response differences can be explicitly incorporated into the algorithm through the use of subset sample spectra collected on both spectrometers. Two current calibration transfer methods, subset recalibration and piecewise direct standardization (PDS), also utilize subset sample spectra to facilitate transfer of calibration. The three methods were applied to the transfer of quantitative multivariate calibration models for near-infrared (NIR) data of organic samples containing chlorobenzene, heptane, and toluene between a primary and three secondary spectrometers that were all the same model, called intra-vendor transfer of calibration. The hybrid PACLS/PLS method outperformed subset recalibration and provided predictions equivalent to PDS with additive background correction on the two secondary spectrometers whose instrument drift appeared to be dominated by simple linear baseline variations. One of the secondary spectrometers had complex instrument drift that was captured by repeatedly measuring the spectrum of a single repeat sample. In calculating a transfer function to correct prediction spectra, PDS assumes no instrumental drift on the secondary spectrometer. Therefore, PDS was unable to directly accommodate both the subset samples and the use of a single repeat sample to transfer and maintain a calibration on that secondary instrument. In order to implement the transfer of calibration with PDS in the presence of complex instrument drift, recalibrated PLS models that included the repeat spectra from the secondary spectrometer were used to predict the spectra transformed by PDS. The importance of correcting for drift on the secondary spectrometer during calibration transfer was illustrated by the improvements in prediction for all three methods vs. using only the instrument response differences derived from the subset sample spectra. When the effects of instrument drift were complex on the secondary spectrometer, the PACLS/PLS hybrid algorithm outperformed both PDS and subset recalibration. Through the explicit incorporation of spectral variations, due to instrument response differences and drift on the secondary spectrometer, the PACLS/PLS algorithm was successful at intra-vendor transfer of calibrations between NIR spectrometers.

Sampling Error Profile Analysis for calibration transfer in multivariate calibration

2017 ◽  
Vol 171 ◽  
pp. 234-240 ◽  
Author(s):  
Feiyu Zhang ◽  
Wanchao Chen ◽  
Ruoqiu Zhang ◽  
Boyang Ding ◽  
Heming Yao ◽  
...  
Keyword(s):  
Multivariate Calibration ◽  
Profile Analysis ◽  
Sampling Error ◽  
Calibration Transfer ◽  
Error Profile

scholarly journals Multivariate calibration transfer employing variable selection and subagging

2010 ◽  
Vol 21 (1) ◽  
pp. 127-134 ◽  
Author(s):  
Marcelo N. Martins ◽  
Roberto K. H. Galvão ◽  
Maria Fernanda Pimentel
Keyword(s):  
Variable Selection ◽  
Multivariate Calibration ◽  
Calibration Transfer

Importance of Prediction Outlier Diagnostics in Determining a Successful Inter-Vendor Multivariate Calibration Model Transfer

2007 ◽  
Vol 61 (7) ◽  
pp. 747-754 ◽  
Author(s):  
Robert D. Guenard ◽  
Christine M. Wehlburg ◽  
Randy J. Pell ◽  
David M. Haaland
Keyword(s):  
Least Squares ◽  
Outlier Detection ◽  
Prediction Error ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Subset Selection ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Significant Difference ◽  
Pls Method

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.

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