Principal component regression calibration based

Fourier transform infrared (FTIR) spectroscopy of coal low-temperature ashes was applied to the determination of coal mineralogy and the prediction of ash properties during coal combustion. Analytical methods commonly applied to the mineralogy of coal are critically surveyed. Conventional least-squares analysis of spectra was used to determine coal mineralogy on the basis of forty-two reference mineral spectra. The method described showed several limitations. However, partial least-squares and principal component regression calibrations with the FTIR data permitted prediction of all eight ASTM ash fusion temperatures to within 50 to 78 F and four major elemental oxide concentrations to within 0.74 to 1.79 wt % of the ASTM ash (standard errors of prediction). Factor analysis based methods offer considerable potential in mineral-ogical and ash property applications. 

Because of peak overlappings in the first- and second-derivative spectra, conventional spectrophotometry cannot be applied satisfactorily for quantitative analysis, and the interpretation cannot be resolved by the zero-crossing technique. A chemometric approach improves precision and predictability, e.g., by the application of classical least sqnares (CLS), principal component regression (PCR), partial least squares (PLS), and iterative target transformation factor analysis (ITTFA), appropriate interpretations were found from the direct and first- and second-derivative absorption spectra. When five colorant combinations of sixteen mixtures of colorants from commercial food products were evaluated, the results were compared by the application of different chemometric approaches. The ITTFA analysis offered better precision than CLS, PCR, and PLS, and calibrations based on first-derivative data provided some advantages for all four methods. ... 

This chapter ends with a short description of the important methods, Principal Component Regression (PCR) and Partial Least-Squares (PLS). Attention is drawn to the similarity of the two methods. Both methods aim at predicting properties of samples based on spectroscopic information. The required information is extracted from a calibration set of samples with known spectrum and property. 

Experience in this laboratory has shown that even with careful attention to detail, determination of coal mineralogy by classical least-squares analysis of FTIR data may have several limitations. Factor analysis and related techniques have the potential to remove or lessen some of these limitations. Calibration models based on partial least-squares or principal component regression may allow prediction of useful properties or empirical behavior directly from FTIR spectra of low-temperature ashes. Wider application of these techniques to coal mineralogical studies is recommended. 

The calibration methods most frequently used to relate the property to be measured to the analytical signals acquired in NIR spectroscopy are MLR,59 60 principal component regression (PCR)61 and partial least-squares regression (PLSR).61 Most of the earliest quantitative applications of NIR spectroscopy were based on MLR because spectra were then recorded on filter instruments, which afforded measurements at a relatively small number of discrete wavelengths only. However, applications involving PCR and PLSR... 

The prediction of Y-data of unknown samples is based on a regression method where the X-data are correlated to the Y-data. The multivariate methods, usually used for such a calibration, are principal component regression (PCR) and partial least squares regression (PLS). Both methods are based on the assumption of linearity and can deal with co-linear data. The problem of co-linearity is solved in the same way as the formation of a PCA plot. The X-variables are added together into latent variables, score vectors. These vectors are independent since they are orthogonal to each other and they can therefore be used to create a calibration model. 

In the arsenal of calibration methods there are methods more suited for modelling any number of correlated variables. The most popular among them are Principal Component Regression (PCR) and Partial Least Squares (PLS) [3], Their models are based on a few orthogonal latent variables, each of them being a linear combination of all original variables. As all the information contained in the spectra can be used for the modelling, these methods are often called the full-spectrum methods. ... 

On the other hand, atomic emission spectra are inherently well suited for multivariate analysis due to the fact that the intensity data can be easily recorded at multiple wavelengths. The only prerequisite is that the cahbration set encompasses all likely constituents encountered in the real sample matrix. Calibration data are therefore acquired by a suitable experimental design. Not surprisingly, many of the present analytical schemes are based on multivariate calibration techniques such as multiple linear regression (MLR), principal components regression (PCR), and partial least squares regression (PLS), which have emerged as attractive alternatives. 

In a paper by Kirsch and Drennen [73], intact theophylline tablets coated with an ethylcellulose polymer were analyzed by grating-based and AOTF spectrometers. The purpose of the work was threefold. First, tablets were coated with increasing levels of ethylcellulose to vary the dissolution profiles of the dosage forms. After NIR spectra were collected, the tablets were subjected to dissolution in a USPDAII. The time required for 50% of the drug to enter solution was used as the measure of dissolution rate. Principal component regression was used to develop the calibration. The calibration provided a SEE of 2.8 min, a coefficient of determination of. 977, and an SEP of 6.6 min, with time to 50% dissolution values ranging from 48 to 93 min. 

Principal component regression and PLS are normally employed when the user does not (or cannot) define the particular set of wavelengths that should be used for model calibration. In this case, the regression technique must provide both the vector of regression coefficients b and the most suitable set of wavelengths to be used for model calibration. Therefore, PCR and PLS calibration models are based on the information available within the whole spectral region defined by the user. These models are commonly employed when process responses (for instance, individual monomer conversions in solution copolymerizations) are functions of multiple process perturbations (e.g., batch time and initial compositions of chemical constituents). 

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). 

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