Correlation Spectroscopy

Course Description

Correlation Spectroscopy is an increasingly popular method of analyzing spectral data, with the goal of improved understanding of sample chemistry, physics and spectroscopy. The original form of this method dealt exclusively with cases where a single sample is analyzed spectroscopically while being perturbed mechanically in a sinusoidal manner. However, more recent extensions towards spectroscopic data that is collected in the presence of any sample perturbation led to the concept of Generalized 2D Correlation Spectroscopy, which is the subject of this course.

The course will begin with an explanation of the principles of “classical” Correlation Spectroscopy. This will include a discussion of the two complementary methods to display the temporal correlation behavior in spectral data sets: the synchronous and asynchronous maps.

Several chemometric methods can be particularly useful when combined with Correlation Spectroscopy, including Self-Modeling Curve Resolution (a series of methods designed to resolve mixture data into their pure components and their contributions), and multi-block PLS modeling. Several different Curve Resolution methods will be discussed, including the “Pure Variable Method,” and it will be shown that these methods can be used with correlation spectroscopy to greatly simplify the interpretability of correlation maps.

This will be followed with a discussion and demonstration of “hetero-spectral” and “hetero-analytical” correlations, where the above-mentioned techniques are used to analyze data that was generated from the measurement of the same set of samples by two different spectroscopic or analytical methods (i.e., mid-IR and near-IR, or near-IR and DSC). For these cases, it will be shown again how chemometrics can aid in the resolution and interpretability of correlation maps.

Finally, it will be shown how correlation spectroscopy, when combined with chemometrics, can facilitate the determination of the number of components in complex mixture data. . The course includes hands-on computer time for participants to work example problems using PLS_Toolboxand MATLAB.

Prerequisites

Linear Algebra for ChemometriciansChemometrics I–PCA and Multivariate Curve Resolution or equivalent experience.

Course Outline

  1. Introduction
    Principles of “classical” Correlation Spectrsocopy
    Synchronous Maps
    Asynchronous Maps
  2. Combining other Chemometric Methods with Correlation Spectroscopy
    Self-Modelling Curve Resolution
    Multi-block PLS
    Improving Interpretability
  3. Hetero Correlations
    Hetero-spectral
    Hetero-analytical
    Interpretation
  4. Determination of number of components