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SOLO provides the Graphical Interfaces for quickly managing and analyzing data, authoring and applying models and interpreting results. Data can be imported from a variety of different file types and quickly assembled into convenient DataSet objects. Once in a DataSet object the user can easily manage labels, axis scales, and classes and can include/exclude data from the analysis with a click. Modeling and analyzing results is just a matter of drag-and-drop of the data to our comprehensive Analysis GUI.
SOLO provides the Graphical Interfaces for quickly managing and analyzing data, authoring and applying models and interpreting results. Data can be imported from a variety of different file types and quickly assembled into convenient DataSet objects. Once in a DataSet object the user can easily manage labels, axis scales, and classes and can include/exclude data from the analysis with a click. Modeling and analyzing results is just a matter of drag-and-drop of the data to our comprehensive Analysis GUI.
MATLAB Connection Tools
The PLS_Toolbox is a collection of advanced chemometric routines, i.e., tools designed specifically for modeling data from chemical systems. Originally named for the partial least squares (PLS) routines that have become a mainstay for calibration model development, the PLS_Toolbox now contains over 100 functions that can be used in a variety of technical areas. This includes linear and nonlinear modeling tools, along with functions that help the user apply them in the chemical laboratory or process environment. Tools included in the toolbox are point and click graphical user interfaces for principal components analysis and partial least squares regression and a suite of tools for the analysis of multi-way data.
With MIA_Toolbox, multivariate images from remote sensing to microscopy can be easily analyzed using the same PLS_Toolbox tools you are already familiar with. MIA_Toolbox allows you to load, manipulate, and analyze multivariate images in the Analysis Graphical interface as well as using many of the higher-level command-line functions. Principal components analysis, multivariate curve resolution, SIMCA and PLSDA classification, K-Means clustering, and even regression can all be performed on images with this extension pack.
Extended Multiplicative Scatter Correction (EMSC) is a powerful preprocessing technique used to isolate and remove complicated multiplicative and additive effects, such as those caused by light scattering in reflectance spectroscopy. It expands on the popular Multiplicative Scatter/Signal Correction (MSC) technique by offering much improved flexibility in identifying known interferences to remove as well as scaling targets and known analyte spectra. Eigenvector Research is offering this powerful patented technique as an add-on for use with PLS_Toolbox as EMSC_Toolbox.
Model_Exporter converts models created within the PLS_Toolbox or Solo chemometrics modeling environments into an interpretable format for use outside of these products.
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Welcome to the world's most extensive suite of chemometric tools!

See what makes NeuroSolutions the premier neural network development environment. Take a few minutes to browse through the key benefits of the software, or simply visit the topics that interest you. See what makes NeuroSolutions the premier neural network development environment. Take a few minutes to browse through the key benefits of the software, or simply visit the topics that interest you.

The PLS_Toolbox is a collection of advanced chemometric routines, i.e., tools designed specifically for modeling data from chemical systems. Originally named for the partial least squares (PLS) routines that have become a mainstay for calibration model development, the PLS_Toolbox now contains over 300 functions that can be used in a variety of technical areas.

This includes linear and nonlinear modeling tools, along with functions that help the user apply them in the chemical laboratory or process environment. Tools included in the toolbox are point and click graphical user interfaces for principal components analysis and partial least squares regression and a suite of tools for the analysis of multi-way data.

PLS_Toolbox contains the tools required by chemical engineers, analytical chemists and other analysis-driven scientists to explore their data and build predictive models. It gets its name from the Partial Least Squares (PLS) regression method, which has become the standard calibration method in many regression applications, but the toolbox now includes so much more including a unified graphical interface and over 300 tools for use in a wide variety of technical areas. The latest version includes a number of new tools in our graphical interface as well as new command-line functionality.

PLS_Toolbox tools include...

  • Data Exploration and Pattern Recognition (Principal Components Analysis, Parallel Factor Analysis, MCR, Purity, Tucker Models...)
  • Classification (SIMCA, k-nearest neighbors, PLS Discriminant Analysis, Cluster Analysis with Dendograms...)
  • Linear and Non-Linear Regression (PLS, Principal Components Regression, N-way PLS, Locally Weighted Regression...)
  • Self-modeling Curve Resolution, Pure Variable Methods (CODA_DW, Purity (compare to SIMPLSMA), CompareLCMS...)
  • Curve fitting and Distribution fitting and analysis tools
  • Instrument Standardization (Piece-wise Direct, Generalized Least Squares Preprocessing...)
  • Advanced Graphical Data Set Editing and Visualization Tools
  • Advanced Customizable Order-Specific Preprocessing (Centering, Scaling, Smoothing, Derivatizing...)
  • Missing Data Support
  • Variable Selection (Genetic algorithms, IPLS, purity-based analysis)
Plus all the cutting edge tools you've come to expect from Eigenvector Research! All with source code allowing the advanced user to view and understand the techniques - no more black-box analyses. Command-line users may find the Function Reference-Card (get as PDF here) useful to print out and refer to while working with PLS_Toolbox.

System Requirements

If you are using MATLAB 6.5, 7.x or higher (R13.1 through R2007b) you are ready to use PLS_Toolbox. PLS_Toolbox does not require other MATLAB toolboxes. Like all MATLAB toolboxes, PLS_Toolbox is platform independent. It will function on any platform on which MATLAB functions (e.g. MAC, PC, UNIX, Linux).

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