The multivariate calibration problem in chemistry solved by the PLS method

1983 ◽  
pp. 286-293 ◽  
Author(s):  
S. Wold ◽  
H. Martens ◽  
H. Wold
Keyword(s):  
Multivariate Calibration ◽  
Calibration Problem ◽  
Pls Method

Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets

2000 ◽  
Vol 54 (4) ◽  
pp. 608-623 ◽  
Author(s):  
Vítézslav Centner ◽  
Jorge Verdú-Andrés ◽  
Beata Walczak ◽  
Delphine Jouan-Rimbaud ◽  
Frédéric Despagne ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Method ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Calibration Data ◽  
Calibration Problem ◽  
Calibration Techniques ◽  
Selection Of ◽  
Infrared Data

The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A flexible methodology is proposed to allow selection of an appropriate calibration technique for a given calibration problem.

The successive projections algorithm for interval selection in partial least squares discriminant analysis

2016 ◽  
Vol 8 (41) ◽  
pp. 7522-7530 ◽  
Author(s):  
David Douglas de Sousa Fernandes ◽  
Valber Elias Almeida ◽  
Licarion Pinto ◽  
Germano Véras ◽  
Roberto Kawakami Harrop Galvão ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Successive Projections Algorithm ◽  
Selection Approach ◽  
Interval Selection ◽  
Successive Projections ◽  
Pls Method

This paper proposes a new interval selection approach for PLS-DA modelling, which is developed as an extension of the recently introduced iSPA-PLS method for multivariate calibration.

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.

Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem

2014 ◽  
Vol 4 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Lauro C. M. de Paula ◽  
Anderson S. Soares ◽  
Telma W. L. Soares ◽  
Alexandre C. B. Delbem ◽  
Clarimar J. Coelho ◽  
...  
Keyword(s):  
Variable Selection ◽  
Graphics Processing Units ◽  
Firefly Algorithm ◽  
Multivariate Calibration ◽  
Selection Problem ◽  
Calibration Problem ◽  
New Strategy ◽  
Graphics Processing ◽  
Processing Platform ◽  
Variable Selection Problem

The recent improvements of Graphics Processing Units (GPU) have provided to the bio-inspired algorithms a powerful processing platform. Indeed, a lot of highly parallelizable problems can be significantly accelerated using GPU architecture. Among these algorithms, the Firefly Algorithm (FA) is a newly proposed method with potential application in several real world problems such as variable selection problem in multivariate calibration. The main drawback of this task lies in its computation burden, as it grows polynomially with the number of variables available. In this context, this paper proposes a GPU-based FA for variable selection in a multivariate calibration problem. Such implementation is aimed at improving the computational efficiency of the algorithm. For this purpose, a new strategy of regression coefficients calculation is employed. The advantage of the proposed implementation is demonstrated in an example involving a large number of variables. In such example, gains of speedup were obtained. Additionally the authors also demonstrate that the FA, in comparison with traditional algorithms, can be a relevant contribution for the variable selection problem.

Multivariate calibration with robust signal regression

2018 ◽  
Vol 19 (5) ◽  
pp. 524-544
Author(s):  
Bin Li ◽  
Brian D Marx ◽  
Somsubhra Chakraborty ◽  
David C Weindorf
Keyword(s):  
Loss Function ◽  
Efficient Algorithm ◽  
Multivariate Calibration ◽  
Soil Characterization ◽  
Computationally Efficient ◽  
Standard Methods ◽  
Characterization Study ◽  
Calibration Problem ◽  
Convex Problem ◽  
Heavy Tailed

Motivated by a multivariate calibration problem from a soil characterization study, we proposed tractable and robust variants of penalized signal regression (PSR) using a class of non-convex Huber-like criteria as the loss function. Standard methods may fail to produce a reliable estimator, especially when there are heavy-tailed errors. We present a computationally efficient algorithm to solve this non-convex problem. Simulation and empirical examples are extremely promising and show that the proposed algorithm substantially improves the PSR performance under heavy-tailed errors.

scholarly journals Dispersive Raman spectroscopy for the invitroidentification and quantification of injected vancomycin intra-vitreous

2011 ◽  
Vol 25 (2) ◽  
pp. 103-112 ◽  
Author(s):  
Roney Carlos Lora ◽  
Landulfo Silveira ◽  
Stella Regina Zamuner ◽  
Marcos Tadeu Tavares Pacheco
Keyword(s):  
Raman Spectroscopy ◽  
Latent Variable ◽  
Multivariate Calibration ◽  
Ccd Camera ◽  
Carbon Chain ◽  
Control Group ◽  
Calibration Model ◽  
Time Intervals ◽  
Imaging Spectrograph ◽  
Pls Method

This work presents a study which aimed to quantify the intravitreally-injected vancomycin antibiotic using dispersive Raman spectroscopy and Partial Least Squares multivariate calibration. Eyes of New Zealand rabbits were injected with 5.0 mg vancomycin at different time intervals (8, 24 and 48 h groups). Distilled water was injected into the eyes of rabbits in the control group. The vitreous was then removed from each eyeball, placed into a quartz cuvette and submitted for dispersive Raman spectroscopy. The Raman equipment used a laser (80 mW, 830 nm), an imaging spectrograph and a CCD camera with exposure time of 50 s. A calibration curve was developed using the PLS method with vancomycin diluted in water in the range of 1.0–8.0 mg/ml. The spectrum of vancomycin was present in bands at the positions of C=C, amide I, CH3, amide III and vibrations of the carbon chain (C–C, aromatic ring breathing and C–O–C). The spectra of the injected vitreous bodies showed weak bands which were correlated with the pure drug. The PLS calibration model had a standard error of prediction of 0.8 mg/ml using one latent variable. The PLS showed changes in the predicted concentrations of the injected vitreous bodies depending on the time of injection, with an amount near zero for the water-injected group and ranging between 2 and 3 mg/ml for the drug-injected groups. Raman spectroscopy could be employed in the quantitative evaluation of intravitreally-injected vancomycin in future invivoapplications.

scholarly journals Combining Independent Information in a Multivariate Calibration Problem

1997 ◽  
Vol 61 (2) ◽  
pp. 171-186 ◽  
Author(s):  
K. Krishnamoorthy ◽  
Darren J. Johnson
Keyword(s):  
Multivariate Calibration ◽  
Independent Information ◽  
Calibration Problem

A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables

1983 ◽  
Vol 150 ◽  
pp. 61-70 ◽  
Author(s):  
Michael Sjöström ◽  
Svante Wold ◽  
Walter Lindberg ◽  
Jan-Åke Persson ◽  
Harald Martens
Keyword(s):  
Analytical Chemistry ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Multivariate Calibration ◽  
Calibration Problem
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