PLS analysis

Cruciani G, S dementi and M Baroni 1993. Variable Selection in PLS Analysis. In Kubinyi H (Editor) 31 QSAR in Drug Design. Leiden, ESCOM, pp. 551-564. 

A Brief Review of the QSAR Technique. Most of the 2D QSAR methods employ graph theoretic indices to characterize molecular structures, which have been extensively studied by Radic, Kier, and Hall [see 23]. Although these structural indices represent different aspects of the molecular structures, their physicochemical meaning is unclear. The successful applications of these topological indices combined with MLR analysis have been summarized recently. Similarly, the ADAPT system employs topological indices as well as other structural parameters (e.g., steric and quantum mechanical parameters) coupled with MLR method for QSAR analysis [24]. It has been extensively applied to QSAR/QSPR studies in analytical chemistry, toxicity analysis, and other biological activity prediction. On the other hand, parameters derived from various experiments through chemometric methods have also been used in the study of peptide QSAR, where partial least-squares (PLS) analysis has been employed [25]. 

As an example we try to model the relation between the sensory data of Table 35.1 and the instmmental measurements of Table 35.4. The PLS analysis results are shown in Table 35.8. The first PLS dimension loads about equally high on... 

To construct the reference model, the interpretation system required routine process data collected over a period of several months. Cross-validation was applied to detect and remove outliers. Only data corresponding to normal process operations (that is, when top-grade product is made) were used in the model development. As stated earlier, the system ultimately involved two analysis approaches, both reduced-order models that capture dominant directions of variability in the data. A PLS analysis using two loadings explained about 60% of the variance in the measurements. A subsequent PCA analysis on the residuals showed that five principal components explain 90% of the residual variability. 

Wold, S., Geladi, P., and Ohman, J., Multi-way principal components and PLS analysis, J. Chemometrics 1, 41-56 (1987b). 

Using our dataset which includes all of the descriptors mentioned so far, we conducted a PLS analysis using SIMCA software [34], In the initial PLS model, MW, V, and a (Alpha) were removed because they are in each case highly correlated with CMR (r > 0.95). SIMCA s VIP function selected only qmin (Qnegmin) for removal on the basis of it making no important contribution to the model. In the second model, 2q+/a (SQpos A) and ECa/a (SCa A) coincided nearly exactly in the three-component space of these two, we decided to keep only ECa/a in the third and final model. This model consisted of three components and accounted for 75% of the variance in log SQ the Q2 value was 0.66. 

Fig. 10.1. Loadings plot for the variables in the partial least squares (PLS) analysis. Abscissa first component ordinate second component.

In PLS analysis a distance to the overall model (distance-to-model), defined as the variance in the descriptors remaining after the analysis (residual standard deviation RSD), is given for each predicted compound. This is a very important piece of information that is presented to the researcher (see Section 16.5.4). 

The correlation was made using PLS analysis within the VolSurf software. The solubility was quantified via the —log[Soly]-values, where Soly was expressed in mol L 1 at 25°C. The quantitative PLS analysis resulted in a two-component model. The recalculated versus experimental PLS plot (Fig. 17.3) shows the correlation obtained. From the objects pattern, a differentiation between very poorly/ poorly/medium/highly/very highly soluble compounds was seen to be possible, though fine quantitative predictions were difficult to achieve. 

In our original column on this topic [1] we had only done a principal component analysis to compare with the MLR results. One of the comments made, and it was made by all the responders, was to ask why we did not also do a PLS analysis of the synthetic linearity data. There were a number of reasons, and we offered to send the data to any or all of the responders who would care to do the PLS analysis and report the results. Of the original responders, Paul Chabot took us up on our offer. In addition, at the 1998 International Diffuse Reflectance Conference (The Chambersburg meeting), Susan Foulk also offered to do the PLS analysis of this data. 

In PLS, the variance is generated using the quantities generated by the referee analytical method. Therefore, the factors in a PLS analysis (especially the first) resemble the spectrum of the active ingredient (assuming a quantitative analysis of a constituent). When measuring a physical attribute (hardness, polymorphic form, elasticity), the PLS factor may not resemble any of the materials present in the mixture. 

The absorbance values of this solution was recorded between 190 and 300 nm spectral range eveiy 5 nm for PLS analysis of sweeteners (Apt, Ace-K, Sac). 

The SIMCA software is available in two forms, both developed by Wold (2 5, 31) 1) an interactive, Fortran version which runs on Control Data Corporation (CDC) machines, and 2) an interactive version, SIMCA-3B. Additional information on these programs is contained in Appendix I. Only the SIMCA-3B version contains the CPLS-2 program used for PLS analysis. 

Fig. 5. Discriminant PLS analysis of NMR time domain CPMG data acquired at 100 MHz versus mealiness in Cox s apples having three degrees of mealiness and labelled (Coxa, Coxb and Coxc), acquired in the data for Ref. 34.

Afzelius, L., Masimieembwa, C.M., Karlen, A., Andeesson, T.B., and Zamora, 1. Discriminant and quantitative PLS analysis of competitive CYP2C9 inhibitors versus non-inhibitors using alignment independent GRIND descriptors. 

Figure 8.11 An OTC analgesic tablet (Excedrin) with three APIs. Spatial distribution of each API was obtained using PLS analysis. (A-C) PLS score images of acetaminophen, aspirin, and caffeine, respectively and (D-F) single-pixel microspectra (solid line) compared to pure component acetaminophen, aspirin, and caffeine spectra (dashed line), respectively.

The raw data are discussed in detail in Section 5.2.2.2. In the ICLS and MIR ap plications the data are split into calibration and validation sets. For this PLS analysis, the 95 spectra from the 12 design points are all used to construct the model using a leave-one-out cross-validation procedure. 

The reduction depicted in equation 53 is the basis of a method for determination of POV in the 0 to 10 meqkg range by FTNIR. Although the technical procedure is simple, data need PLS analysis because of the overlap of signals in the data acquisition region (4695 to 4553 cm- )235,435... 

PLS was advantageous when studying the relationship of the toxicity of thiify triazines on Daphnia magna (25), and in a comparison between Hansch analysis and PLS analysis, using the same data set, it was shown that tiie multivariate approach of PLS provided more useful models than the Hansch type approach (26). 

This study demonstrated that the micro-mesoporous composite materials could be synthesized with two-step treatment by microwave using two different templates system with TPABr and MTAB. This formation was controlled by the self-assembly formation of supramolecular templates between MTA micelles and SiO /TPA gels. As varying microwave irradiation time of micro-mesoporous materials, gradually transition from the mesophase to micro-mesophase was occurred. These materials have higher dm spacing of mesoporous materials and lead to transition from mesophase to micro-microphase by an increment of synthetic time, while the calcined products is formed with bimodal and trimodal pore size distribution under microwave irradiation within 3 h. From TG-DTA and PL analysis, the self-assembly formation of supramolecular templates between MTA+ micelles and SiO /TPA+ gels were monitored. 

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