Calibration advantages over univariate

Multiv te calibration offers several advantages over univariate approaches ... 

Multivariate calibration methods offer several advantages over univariate calibration methods. 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. Multivariate calibration approaches permit selective quantitation of several analytes of interest in complex combinatorial libraries using low-resolution instruments when overlapping responses from different species preclude the use of univariate analysis. Quantitative... 

The strengths of the factor-based methods lie in the fact that they are multivariate. The diagnostics are excellent in both the calibration and prediction phases. Improved precision and accuracy over univariate methods can often be realized because of the multivariate advantage. Ultimately, PLS and PCR are able to model complex data and identify when the models are no longer valid. This is an extremely powerful combination. 

Multivariate calibrations have become a commonly applied tool in the field of modern analytical chemistry and, specifically, in quantitative IR analysis [13,14]. PLS regression is one of several methods that utilize an entire spectral information band present in IR data, often referred to as full-spectrum calibrations. The advantages of full-spectrum calibrations, such as PLS and CLS, are improvements in precision and robustness over univariate calibrations owing to increased signal averaging from including more spectral intensities. The distinction between PLS and CLS manifests in the fact that PLS is a factor-based regression, which means the full spectra for the acquired... 

An important advantage of multivariate calibration over univariate calibration is that, because many measurements are obtained from the same solution, the signal from the analytes and that from the interferences can be separated mathematically, so concentrations can be determined without the need for highly selective measurements for the analyte. This advantage has been termed the first-order advantage, and eqn (4.3) is also called the first-order calibration model. The term first-order means that the response from a test specimen is a vector (a first-order tensor). This nomenclature and the advantages of first-order calibration have been well described in the theory of analytical chemistry. To use this advantage, however, there is one major requirement the multivariate measurements of the calibrators must contain... 

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