Partial least squares quantification

Data arrays (one-way data) consist of spectral values taken at a given time point or electrophoretic responses at a given wavelength over time. One-way data can be used for sample characterization, classification, and quantification with multivariate calibration methods (e.g., principal component analysis and partial least square regression). 

Numerous software data treatments authorize the elucidation of mixture composition from spectra. One of the best-known methods is the Kalman s least squares filter algorithm, which operates through successive approximations based upon calculations using weighted coefficients (additivity law of absorbances) of the individual spectra of each components contained in the spectral library. Other software for determining the concentration of two or more components within a mixture uses vector quantification mathematics. These are automated methods better known by their initials PLS (partial least square), PCR (principal component regression), or MLS (multiple least squares) (Figure 9.26). 

PLS (Partial Least Squares) regression was used for quantification and classification of aristeromycin and neplanocin A (Figure 4). Matlab was used for PCA (Principal Components Analysis) (according to the NIPALS algorithm) to identify correlations amongst the variables from the 882 wavenumbers and reduce the number of inputs for Discriminant Function Analysis (DFA) (first 15 PCA scores used) (Figure 5). 

The accuracy of quantification methods may be improved using multivariate methods since these have the advantage of analysing variation across the whole spectmm rather than the intensity of individual bands [11, 65]. The use of IR spectroscopy with factor analysis [66], partial least squares [67,68] and neural networks [69], and FT-Raman with factor analysis [70] and principal component analysis [71] has been demonstrated. 

Paracetamol, propiphenazone and caffeine First to fourth derivative spectra of components were subjected to chemometric analysis (principal component regression, PCR partial least squares with one dependent variable, PLS-1 three dependent variables, PLS2) and adopted for multicomponent analysis. The third derivative spectra of aU ingredients became a basis of quantification method. 39... 

Ferrari, S., Ratner, B.D. (2000) ToF-SIMS quantification of albumin adsorbed on plasmadeposited fluoropolymers by partial least-squares regression. Surf Interface Anal., 29,837-844. 

S. Kasemsumran, Y. P. Du, K. Maruo, and Y. Ozaki, Improvement of Partial Least-Squares Models for In tro and In vo Glucose Quantifications by Using Near-Infrared Spectroscopy and Searching Combination Moving Window Partial Least-Squares, Chemometrics Intell. Lab. Syst., 82,97 (2006). 

Calculation methods such as multivariate method based on least-squares or partial least-squares algorithms [78] or univariate method [77] allow quantification of each tracer in the 10-100 pmol range. The standard curves for the assay of carbamazepine in mono and triple-CMIA are shown in Fig. 8.10. 

Camacho, W. and S. Karlsson, Quantification of Antioxidants in Polyethylene by Near Infrared (NIR) Analysis and Partial Least Squares (PLS) Regression. Int. J. Polym. Analysis Charact., 2002.7 41-51. 

Figure 6.7 The root mean square error of calibration (RMSEC), leave-one-out cross validation (RMSECV) and prediction (RMSEP) are plotted as a function of the signal-to-noise ratio (SNR). While the intrinsic SNR amounts to 3000, random noise was artificially added to mid-IR spectra of 247 serum samples (which decreases the SNR) and the concentration of glucose was recalculated by means of partial least squares (PLS) based on the noisy spectra. The open symbols refer to assessing the quality of quantification within the teaching set, while the filled symbols relate to an external validation set. The data show that the noise can be increased by more than an order of magnitude before the prediction accuracy of the independent external validation set (RMSEP) is affected. In addition, it can clearly be observed that the RMSEC is a poor measure of accuracy since it suggests delivering seemingly better results for lower SNRs, while in fact the calibration simply tends to fit the noise for low values of SNR (see section 6.7).

A novel methodology for the quantification of Sunset Yellow has been developed using image analysis (RGB histograms) and partial least squares (PLSs) regression [38]. The developed method presented many advantages compared with alternative methodologies... 

Other study was carried out to develop a method based on FTIR spectroscopy combined with chemometrics of multivariate calibrations (partial least square and principal component regression) as well as discriminant analysis for quantification and discrimination of canola oil in virgin coconut oil (Che Man Rohman 2013). 

Quantification of canola oil in virgin coconut oil was performed using multivariate calibrations of partial least square (PLS) and principal component regression (PCR). The relationship between actual and predicted values of canola oil in virgin coconut oil was... 

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