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65,66 In combinatorial and high-throughput screening, chemometrics has been successfully applied for visualization and pattern recognition,20,67 lead identi cation and optimization,68,69 library design,70,71 process optimization,72,73 and development of predictive odels and quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs)67,70 The implementation of multivariate calibration improves analysis selectivity when spectroscopic or any other types of measurements are performed with relatively nonselective or low-resolution instruments60,74 In combinatorial materials science, pattern recognition techniques are used to nd similarities and differences between samples of combinatorial libraries, and they serve as a powerful visualization tool and can warn of the occurrence of abnormalities in the measured data20 The correlated patterns revealed in large data sets by these techniques can determine the structural relationships among screening hits and can signi cantly reduce data dimensionality to make the database more manageable.

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Methods of pattern recognition include principal components analysis (PCA), hierarchical cluster analysis (HCA), soft independent modeling of class analogies (SIMCA), and neural networks, among some others75,76 Multivariate calibration methods offer several advantages over univariate calibration methods77,78 Signal averaging is achieved, since more than one measurement channel is employed in the analysis Also concentrations of multiple species may be measured if they are present in the calibration samples A calibration model is built by using responses from calibration standards The analysis of unknown samples will suffer if a species is present in the sample that is not accounted for in the calibration model This is mitigated somewhat by the ability to detect whether a sample is an outlier from the calibration set.

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