The Chemometrics Toolbox provides more than 70 specialized MATLAB functions for chemometric calibration. It enables you to perform quantitative and qualitative analysis using powerful methods such as Multiple Linear Regression, Principal Component Regression, and Partial Least-Squares in latent variables. Chemometrics organizes chemical data into matrices so that it can be processed to create calibrations or extract useful information. This makes MATLAB an ideal environment for chemometrics.
The Chemometrics Toolbox provides a comprehensive array of the functions needed by chemists, biologists, scientists, and engineers to develop and validate calibrations used with sophisticated process analyzers and laboratory instruments. Designed to enhance productivity, the Chemometrics Toolbox provides the tools that enable users to quickly and easily apply chemometric techniques to data, allowing them to perform quantitative and qualitative analysis on their applications while maintaining confidence in the math.
In order to simplify industrial deployment of calibrations and to eliminate version-related maintenance issues, the Chemometrics Toolbox remains compatible with all versions of MATLAB running on all platforms. Some people are successfully using the Chemometrics Toolbox with Octave, an Open Source MATLAB clone which is available at no charge.
|||More than 70 specialized chemometrics functions|
|||Classical Least-Squares (K-matrix) multiple linear regression|
|||Inverse Least-Squares (P-matrix) multiple linear regression|
|||Principal component analysis (PCA or factor analysis)|
|||Principal component regression (PCR)|
|||Partial least-squares (PLS)|
|||Indicator functions for model formulation for use with factor-based methods|
|||Plotting and visualization functions|
|||Keystroke-by-keystroke introductory Tutorial|
Multiple linear regression (MLR) techniques. The Chemometrics Toolbox includes core routines for producing quantitative calibrations. Primary capabilities include: classical least-squares (CLS or K-matrix), inverse least-squares (ILS or P-matrix), and Q-matrix methods.
Factor-based techniques. The toolbox includes factor-based methods for producing quantitative calibrations and for gaining qualitative insight into data. Key functions include: principal component analysis (PCA or factor analysis), principal component regression (PCR), partial least-squares (PLS), and PLS regression matrix (eliminating the need for iterative PLS prediction).
Indicator functions. A complete family of indicator functions are included to assist in formulating the correct models when using factor-based techniques: reduced eigenvalues indicator, two-way F-test for reduced eigenvalues, fit to training data, fit to validation data, fractional indicator function, cross-validation, and predicted residual error sum-of-squares (PRESS).
Plotting. Several plotting commands make it easy to view data and spot patterns, such as plotting up to four statistical indicator functions in a single figure, and plotting scores vs. scores to visualize data clusters with factor-based techniques.
Tutorial. The introductory tutorial included in the user's guide quickly teaches you how to use the toolbox by leading you keystroke-by- keystroke through the toolbox functions.