Regression analysis, partial least squares

Partial least-squares regression analysis, 16 753, 754, 755-756 Partially alkoxylated chlorotitanates,... 

Human perception of flavor occurs from the combined sensory responses elicited by the proteins, lipids, carbohydrates, and Maillard reaction products in the food. Proteins Chapters 6, 10, 11, 12) and their constituents and sugars Chapter 12) are the primary effects of taste, whereas the lipids Chapters 5, 9) and Maillard products Chapter 4) effect primarily the sense of smell (olfaction). Therefore, when studying a particular food or when designing a new food, it is important to understand the structure-activity relationship of all the variables in the food. To this end, several powerful multivariate statistical techniques have been developed such as factor analysis Chapter 6) and partial least squares regression analysis Chapter 7), to relate a set of independent or "causative" variables to a set of dependent or "effect" variables. Statistical results obtained via these methods are valuable, since they will permit the food... 

Partial least squares regression analysis (PLS) has been used to predict intensity of sweet odour in volatile phenols. This is a relatively new multivariate technique, which has been of particular use in the study of quantitative structure-activity relationships. In recent pharmacological and toxicological studies, PLS has been used to predict activity of molecular structures from a set of physico-chemical molecular descriptors. These techniques will aid understanding of natural flavours and the development of synthetic ones. 

A relatively recent development in QSAR research is molecular reference (MOLREF). This molecular modelling technique is a method that compares the structures of any number of test molecules with a reference molecule, in a quantitative structure-activity relationship study (27). Partial least squares regression analysis was used in molecular reference to analyse the relation between X- and Y-matrices. In this paper, forty-two disubstituted benzene compounds were tested for toxicity to Daphnia... 

Statistical analysis Clustering based on correlations to identify group relationships. Partial least square regression analysis to track pathways of signal flow. Principal component analysis to identify significant signaling components. (82-85)... 

Near-infrared (NIR) spectroscopy is becoming an important technique for pharmaceutical analysis. This spectroscopy is simple and easy because no sample preparation is required and samples are not destroyed. In the pharmaceutical industry, NIR spectroscopy has been used to determine several pharmaceutical properties, and a growing literature exists in this area. A variety of chemoinfometric and statistical techniques have been used to extract pharmaceutical information from raw spectroscopic data. Calibration models generated by multiple linear regression (MLR) analysis, principal component analysis, and partial least squares regression analysis have been used to evaluate various parameters. 

A partial least-square regression analysis was applied by Svinning and Bremseth (1993) on alite crystal size and various process parameters, showing that alite size accounted for only approximately 40% of the total variance and that an increase in the >60-pm crystals occurred with decreasing secondary air temperature measured in the cooler. Statistical analyses of this type, in the present writer s opinion, are greatly needed in our industry to decipher relative importance of the many kinds of measurements. 

SIA instrumentation has been applied to the automated dissolution studies of a sustained release formulation of ibuprofen. The instrument monitored the absorbance of seven replicate samples six times per hour. With the application of partial least squares regression analysis to the resultant absorbance data, this same instrumentation was capable of the simultaneous determination of aspirin, phenacetin, and caffeine in pharmaceutical formulations. 

JB Cooper, PE Flecher, TM Vess, WT Welch. Remote fiber-optic Raman analysis of xylene isomers in mock petroleum fuels using a low-cost dispersive instrument and partial least squares regression analysis. Appl Spectrosc 49 586-592, 1995. 

JB Cooper, KL Wise, J Groves, WT Welch. Determination of octane number and Reid vapor pressure of commercial petroleum fuels using FT-Raman spectroscopy and partial least squares regression analysis. Anal Chem 67 4096-4100, 1995. 

After an alignment of a set of molecules known to bind to the same receptor a comparative molecular field analysis CoMFA) makes it possible to determine and visuahze molecular interaction regions involved in hgand-receptor binding [51]. Further on, statistical methods such as partial least squares regression PLS) are applied to search for a correlation between CoMFA descriptors and biological activity. The CoMFA descriptors have been one of the most widely used set of descriptors. However, their apex has been reached. 

To gain insight into chemometric methods such as correlation analysis, Multiple Linear Regression Analysis, Principal Component Analysis, Principal Component Regression, and Partial Least Squares regression/Projection to Latent Structures... 

On the other hand, techniques like Principle Component Analysis (PCA) or Partial Least Squares Regression (PLS) (see Section 9.4.6) are used for transforming the descriptor set into smaller sets with higher information density. The disadvantage of such methods is that the transformed descriptors may not be directly related to single physical effects or structural features, and the derived models are thus less interpretable. 

Donahue, S.M., Brown, C.W., Scott, M.J., "Analysis of Deoxyribonucleotides with Principal Component and Partial Least-Squares Regression of UV Spectra after Fourier Processing", Appl. Spec. 1990 (44) 407-413. 

Partial least squares regression (PLS). Partial least squares regression applies to the simultaneous analysis of two sets of variables on the same objects. It allows for the modeling of inter- and intra-block relationships from an X-block and Y-block of variables in terms of a lower-dimensional table of latent variables [4]. The main purpose of regression is to build a predictive model enabling the prediction of wanted characteristics (y) from measured spectra (X). In matrix notation we have the linear model with regression coefficients b ... 

A difficulty with Hansch analysis is to decide which parameters and functions of parameters to include in the regression equation. This problem of selection of predictor variables has been discussed in Section 10.3.3. Another problem is due to the high correlations between groups of physicochemical parameters. This is the multicollinearity problem which leads to large variances in the coefficients of the regression equations and, hence, to unreliable predictions (see Section 10.5). It can be remedied by means of multivariate techniques such as principal components regression and partial least squares regression, applications of which are discussed below. 

Glen, W. G., Dunn, W. J., Ill, and Scott, D. R. (1989) Principal components analysis and partial least squares regression. Tetrahedron Comput. Meth. 2, 349-376. 

The diffusion of correlation methods and related software packages, such as partial-least-squares regression (PLS), canonical correlation on principal components, target factor analysis and non-linear PLS, will open up new horizons to food research. 

J. Amador-Hernandez, L. E. Garcia-Ayuso, J. M. Fernandez-Romero and M. D. Luque de Castro, Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry, J. Anal. At. Spectrom., 15, 2000, 587-593. 

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