CAT (Chemometric Agile Tool)
- Istruzioni.txt (istruzioni in italiano)
- Instructions.txt (english instructions)
- Istruzioni.pdf (istruzioni in italiano)
- Instructions.pdf (english instructions)
Download the R-based software CAT:
- setup_CAT.exe (October 28 2022)
Update packages for previous installations:
- myscript.rar (October 28 2022)
- mylib.rar (September 12 2022)
- working.rar (January 8 2021)
- R-3.0.0.rar (June 4 2019)
- home.rar (March 19 2019)
- ggobi.rar (March 9 2013)
Please cite as: R. Leardi, C. Melzi, G. Polotti, CAT (Chemometric Agile Tool), freely downloadable from http://
Note for the "old" users: with the updates dated January 8 2021 or following, the folder pdf is no longer required and can therefore be deleted. The string in the Target field of the shortcut must then be replaced according to what is reported in the Instructions.txt file.
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
BasiCAT Experimental Design Software
Matlab toolboxes for multivariate analysis
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-Genetic Algorithm toolbox (for Matlab)
- 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)
Colourgrams, Hyperspectrograms and RGB Image Correction GUIs
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
NSIMCA Toolbox for Multiway classification
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.
Multi-way VIP for variable selection
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.