Comparisons of prediction abilities of augmented classical least squares and partial least squares with realistic simulated data : effects of uncorrelated and correlated errors with nonlinearities.
Abstract
A manuscript describing this work summarized below has been submitted to Applied Spectroscopy. Comparisons of prediction models from the new ACLS and PLS multivariate spectral analysis methods were conducted using simulated data with deviations from the idealized model. Simulated uncorrelated concentration errors, and uncorrelated and correlated spectral noise were included to evaluate the methods on situations representative of experimental data. The simulations were based on pure spectral components derived from real nearinfrared spectra of multicomponent dilute aqueous solutions containing glucose, urea, ethanol, and NaCl in the concentration range from 0500 mg/dL. The statistical significance of differences was evaluated using the Wilcoxon signed rank test. The prediction abilities with nonlinearities present were similar for both calibration methods although concentration noise, number of samples, and spectral noise distribution sometimes affected one method more than the other. In the case of ideal errors and in the presence of nonlinear spectral responses, the differences between the standard error of predictions of the two methods were sometimes statistically significant, but the differences were always small in magnitude. Importantly, SRACLS was found to be competitive with PLS when component concentrations were only known for a single component. Thus, SRACLS has a distinct advantage over standard CLSmore »
 Authors:
 Publication Date:
 Research Org.:
 Sandia National Laboratories
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 993880
 Report Number(s):
 SAND20032454J
TRN: US201024%%144
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Proposed for publication in Applied Spectroscopy.
 Country of Publication:
 United States
 Language:
 English
 Subject:
 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; 97 MATHEMATICAL METHODS AND COMPUTING; COMPARATIVE EVALUATIONS; LEAST SQUARE FIT; DATA COVARIANCES; MATHEMATICAL MODELS; FORECASTING; MULTIVARIATE ANALYSIS; INFRARED SPECTRA; GLUCOSE; UREA; ETHANOL; SODIUM CHLORIDES; AQUEOUS SOLUTIONS; SPECTROMETERS; CALIBRATION; SPECTRAL RESPONSE
Citation Formats
Haaland, David Michael, and Melgaard, David Kennett. Comparisons of prediction abilities of augmented classical least squares and partial least squares with realistic simulated data : effects of uncorrelated and correlated errors with nonlinearities.. United States: N. p., 2003.
Web.
Haaland, David Michael, & Melgaard, David Kennett. Comparisons of prediction abilities of augmented classical least squares and partial least squares with realistic simulated data : effects of uncorrelated and correlated errors with nonlinearities.. United States.
Haaland, David Michael, and Melgaard, David Kennett. 2003.
"Comparisons of prediction abilities of augmented classical least squares and partial least squares with realistic simulated data : effects of uncorrelated and correlated errors with nonlinearities.". United States.
doi:.
@article{osti_993880,
title = {Comparisons of prediction abilities of augmented classical least squares and partial least squares with realistic simulated data : effects of uncorrelated and correlated errors with nonlinearities.},
author = {Haaland, David Michael and Melgaard, David Kennett},
abstractNote = {A manuscript describing this work summarized below has been submitted to Applied Spectroscopy. Comparisons of prediction models from the new ACLS and PLS multivariate spectral analysis methods were conducted using simulated data with deviations from the idealized model. Simulated uncorrelated concentration errors, and uncorrelated and correlated spectral noise were included to evaluate the methods on situations representative of experimental data. The simulations were based on pure spectral components derived from real nearinfrared spectra of multicomponent dilute aqueous solutions containing glucose, urea, ethanol, and NaCl in the concentration range from 0500 mg/dL. The statistical significance of differences was evaluated using the Wilcoxon signed rank test. The prediction abilities with nonlinearities present were similar for both calibration methods although concentration noise, number of samples, and spectral noise distribution sometimes affected one method more than the other. In the case of ideal errors and in the presence of nonlinear spectral responses, the differences between the standard error of predictions of the two methods were sometimes statistically significant, but the differences were always small in magnitude. Importantly, SRACLS was found to be competitive with PLS when component concentrations were only known for a single component. Thus, SRACLS has a distinct advantage over standard CLS methods that require that all spectral components be included in the model. In contrast to simulations with ideal error, SRACLS often generated models with superior prediction performance relative to PLS when the simulations were more realistic and included either nonuniform errors and/or correlated errors. Since the generalized ACLS algorithm is compatible with the PACLS method that allows rapid updating of models during prediction, the powerful combination of PACLS with ACLS is very promising for rapidly maintaining and transferring models for system drift, spectrometer differences, and unmodeled components without the need for recalibration. The comparisons under different noise assumptions in the simulations obtained during this investigation emphasize the need to use realistic simulations when making comparisons between various multivariate calibration methods. Clearly, the conclusions of the relative performance of various methods were found to be dependent on how realistic the spectral errors were in the simulated data. Results demonstrating the simplicity and power of ACLS relative to PLS are presented in the following section.},
doi = {},
journal = {Proposed for publication in Applied Spectroscopy.},
number = ,
volume = ,
place = {United States},
year = 2003,
month = 6
}

New predictionaugmented classical least squares (PACLS) methods: Application to unmodeled interferents
A significant improvement to the classical least squares (CLS) multivariate analysis method has been developed. The new method, called predictionaugmented classical least squares (PACLS), removes the restriction for CLS that all interfering spectral species must be known and their concentrations included during the calibration. The authors demonstrate that PACLS can correct inadequate CLS models if spectral components left out of the calibration can be identified and if their spectral shapes can be derived and added during a PACLS prediction step. The new PACLS method is demonstrated for a system of dilute aqueous solutions containing urea, creatinine, and NaCl analytes withmore » 
Partial leastsquares methods for spectral analyses. 2. Application to simulated and glass spectral data
Partial leastsquares (PLS) methods for quantitative spectral analyses are compared with classical leastsquares (CLS) and principal component regression (PCR) methods by using simulated data and infrared spectra from bulk sevencomponent, silicatebased glasses. Analyses of the simulated data sets show the effect of data pretreatment, baseline variations, calibration design, and constrained mixtures on PLS and PCR prediction errors and model complexity. Analyses of the simulated data sets also illustrate some qualitative differences between PSL and PCR. PLS and PCR predicted concentration errors from the simulated data sets and a set of the Fourier transform infrared spectra of silicatebased glasses (Sglass) showmore » 
Use of Classical Least Squares/Partial Least Squares (CLS/PLS) hybrid algorithm for calibration and calibration maintenance of Surface Acoustic Wave (SAW) devices.
Many data analysis algorithms that are currently employed in SAW sensors lack the ability to easily maintain calibration models in the presence of unmodeled interferents or sensor drift. The classical least squares/partial least squares (CLS/PLS) hybrid algorithm is tested in this study for its ability to update calibration models for unmodeled interferents and sensor drift with information from only a single recalibration standard. Use of the CLS/PLS hybrid algorithm for calibration and calibration maintenance of surface acoustic wave (SAW) devices was investigated for synthetic mixtures of isooctanemethanolwater and with synthetic mixtures of nerve agent analogs, diisopropyl methyl phosphonate (DIMP)kerosenewater alongmore »