Chemometrics
Abstract
The article provides an introduction to some of the basic concepts and methods in chemometrics. The first part is devoted to classical univariate statistics with emphasis on estimation and comparison of statistical parameters from one or several measurement series and its application to measurements from chemistry. The second part presents special techniques of data evaluation in analytical chemistry such as calibration, signal processing, and characterizations of analytical procedures. The third and last part introduces modern concepts of multivariate statistics (including multiway data analysis) with applications to chemometrical data sets. Numerical examples serve as illustrations for the presented methods.
The article contains sections titled:
1. |
Introduction |
2. |
Measurements and Statistical Distributions |
2.1. |
Measurements |
2.2. |
Statistical Distributions |
2.3. |
Estimates |
2.4. |
Accuracy and Precision |
3. |
Statistical Tests |
3.1. |
General Procedure |
3.2. |
Tests on Parameters of One or Two Measurement Series |
3.3. |
Outliers, Trend and Nonparametric Tests |
4. |
Comparison of Several Measurement Series |
4.1. |
Homogeneity of Variances |
4.2. |
Equality of Expected Values |
5. |
Regression and Calibration |
5.1. |
Regression Analysis |
5.2. |
Calibration |
6. |
Characterization of Analytical Procedures |
7. |
Signal Processing |
7.1. |
Fourier Transform |
7.2. |
Data Smoothing |
7.3. |
Signal Resolution |
8. |
Basic Concepts of Multivariate Methods |
8.1. |
Objects, Variables, and Data Sets |
8.2. |
Correlation and Distance Matrices |
8.3. |
Data Scaling |
9. |
Factorial Methods |
9.1. |
Principal Components Analysis |
9.2. |
Factor Analysis |
10. |
Classification Methods |
10.1. |
Cluster Analysis |
10.2. |
Supervised Classification |
11. |
Multivariate Regression |
11.1. |
Multiple Linear Regression |
11.2. |
Latent Variable Regression |
12. |
Multidimensional Arrays |
