Partial least squares models accuracy

NIR spectroscopy became much more useful when the principle of multiple-wavelength spectroscopy was combined with the deconvolution methods of factor and principal component analysis. In typical applications, partial least squares regression is used to model the relation between composition and the NIR spectra of an appropriately chosen series of calibration samples, and an optimal model is ultimately chosen by a procedure of cross-testing. The performance of the optimal model is then evaluated using the normal analytical performance parameters of accuracy, precision, and linearity. Since its inception, NIR spectroscopy has been viewed primarily as a technique of quantitative analysis and has found major use in the determination of water in many pharmaceutical materials. 

First, a partial least squares (PLS) model was built based on the FTIR spectra of 37 lab-prepared calibration samples, covering amine concentration of 1.5 to 2.5 mol/liter, CO2 concentrations of 0.1 to 1.1 mol/liter, and SO2 concentrations of 0.0 to 0.2 mol/liter. The predictive performance of this model was subsequently assessed by applying it to the FTIR spectra of 15 separate lab-prepared validation samples within the same concentration ranges, yielding a predictive accuracy of 0.05 mol/liter for the three components (95% confidence interval). 

Research using sets of Australian and New Zealand commercial flour samples augmented with samples produced on a 650 kg/h pilot mill resulted in a calibration with an =. 940 and SECV = 0.41 % (Figure 19.4) [ 15]. In this study, the improvement to the accuracy of starch damage calibration depended on a more precise reference method than that used previously the use of a research monochromator instrument (NIRSystems Model 5000) and its associated software (specifically. Modified Partial Least Squares regression) and careful control of sample temperature. In particular. 

We have applied kNN (Zheng and Tropsha 2000) and simulated annealing - partial least squares (SA-PLS) (Cho et al. 1998) QSAR approaches to a dataset of 48 chemically diverse functionalized amino acids (FAAs) with anticonvulsant activity that were synthesized previously, and successful QSAR models of FAA anticonvulsants have been developed (Shen et al. 2002). Both methods utilized multiple descriptors such as molecular connectivity indices or atom-pair descriptors, which are derived from two-dimensional molecular topology. QSAR models with high internal accuracy were generated, with leave-one-out cross-validated (q ) values rang-... 

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