Installation instructions:

Download the R-based software CAT:

Updated folders for previous installations:

Please cite as: R. Leardi, C. Melzi, G. Polotti, CAT (Chemometric Agile Tool), freely downloadable from http://gruppochemiometria.it/index.php/software

For the Linux users: CAT can be run under Linux by using the software wine (64bits)

A YouTube channel, containing tutorials and demos, is active at the address https://www.youtube.com/channel/UCVIUJAhMVR0a59m3BG_dR0A

For any suggestion or bug please contact Riccardo Leardi (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.)

Download the latest version of the Visual Basic® Experimental Design Tool (October 4 2024) implemented at Mapei S.p.A. R&D laboratories with the support of Prof. Riccardo Leardi as a full and intuitive Microsoft Excel®-based alternative to the R-based CAT Software. If you have any questions, suggestions or comments please feel free to mail the author (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.).

Collection of Matlab modules for calculating unsupervised and supervised mutlivariate models (download from the Milano Chemometrics and QSAR Research Group):

  • Classification toolbox (for Matlab): classification (supervised pattern recognition) multivariate models: Discriminant Analysis, Partial Least Square Discriminant Analysis (PLSDA), Classification trees (CART), K-Nearest Neighbors (kNN), class modeling Potential Functions (Kernel Density Estimators), Support Vector Machines (SVM), Unequal class models (UNEQ), Soft Independent Modeling of Class Analogy (SIMCA), Backpropagation Neural Networks (BPNN)..
  • PCA toolbox (for Matlab): unsupervised multivariate models for data structure analysis: Principal Component Analysis (PCA), Multidimensional Scaling (MDS) and Cluster Analysis.
  • N3-BNN toolbox (for Matlab): N3 (N-Nearest Neighbours), BNN (Binned Nearest Neighbours) and kNN (k Nearest Neighbours) local classification methods.
  • Kohonen and CPANN toolbox (for Matlab): Kohonen Maps and Counterpropagation Artificial Neural networs (CPANNs), Supervised Kohonen networks and XY-fused networks.
  • Regression toolbox (for Matlab): collection of MATLAB modules for calculating regression multivariate models: Ordinary Least Squares (OLS), Partial Least Squares (PLS), Principal Component Regression (PCR), Ridge regression, local regression based on K Nearest Neighbours (KNN) and Binned Nearest Neighbours (BNN) approaches, and variable selection approaches (All Subset Models, Forward selection, Genetic Algorithms and Reshaped Sequential Replacement).
  • PLS-GA toolbox (for Matlab): Matlab modules for variable selection based on Genetic Algorithms coupled with PLS by Riccardo Leardi (download from the Quality & Technology website, Department of Food Science, University of Copenhagen)

MATLAB GUIs released by Chimslab (Università di Modena - Reggio Emilia)

  • Colourgrams GUI: a graphical user-frielndly interface for the analysis of large datasets of RGB images through the colourgrams approach.
  • Hyperspectrograms GUI: a graphical user-friendly interface for the analysis of large datasets of images through the hyperspectrograms approach.
  • RGB Image Correction GUI: a graphical user-friendly interface for the standardization of RGB images.
  • Soft PLSDA routine: a MATLAB function to run Soft PLS-DA algorithm

The NSIMCA toolbox extends the SIMCA method to array of order >= 2.   NSIMCA is written in MATLAB and includes few essential Guidelines. The toolbox is available from: www.models.life.ku.dk/nsimca.

The reference for the NSIMCA toolbox for MATLAB is: C. Durante, R. Bro, M. Cocchi, A classification tool for N-way array based on SIMCA methodology, Chemometrics & Intelligent Laboratory Systems. 106 (2011), 73-85.

If you have any questions, suggestions or comments please feel free to mail us (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.).

The software calculates and displays VIP (Variable Influence in Projection) scores for NPLS and NPLSDA models. The Multi-way VIP code is written in MATLAB and can be downloaded at: www.models.life.ku.dk/nvip

It is described in: S. Favilla C. Durante,M. Li Vigni,M. Cocchi, Assessing feature relevance in NPLS models by VIP, Chemom. Intell. Lab. Syst. 2013 (129) 76-86.

If you have any questions, suggestions or comments please feel free to mail us (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.).

PoliBrush is a freely distributed, stand-alone software designed for teaching exploratory multivariate analysis in the frame of color RGB and spectral imaging. PoliBrush implements principal component analysis (PCA) as its core method. It can be downloaded at: https://github.com/paololiveri/polibrush

Bgen is a tool for batch process monitoring, particularly tailored for small-data scenarios. Bgen is based on a data-driven methodology, as previously reported in the literature (Tulsyan, Garvin & Ündey, J. Process Control, 2019), which leverages machine learning algorithms rooted in Gaussian process state-space models to generate in-silico batch trajectory data from limited historical records. The repository is available at the following link: https://github.com/antoniobenedetti-pmh/BGen

It is described in: L. Gasparini, A. Benedetti, G. Marchese, C. Gallagher, P. Facco, M. Barolo; On the use of machine learning to generate in-silico data for batch process monitoring under small-data scenarios, Computers & Chemical Engineering, 180, 2024, 108469 [link]