The present version is 12.0. To upgrade from a previous version, please contact Umetrics Sales.
For many years, SIMCA has been the standard tool for scientists, researchers, product developers, engineers and others who have huge datasets to cope with. With just a few clicks of the mouse, your data is transformed into information, allowing you to make decisions quickly and with confidence. There are three main attributes that set SIMCA apart: USER-FRIENDLINESS – Advanced data analysis and user-friendliness is not easy to combine, but this is exactly where the new version 12 really excels! Powerful visualization has always been a characteristic of SIMCA-P, but with the new Workset Wizard and Analysis Advisor you are guided through all necessary steps from data to information. Then the Report Generator creates a report with all the plots and necessary explanations – just add your conclusions and you are done! Data analysis has truly never been easier. CUTTING-EDGE TECHNOLOGY – SIMCA-P is as always at the forefront of the latest multivariate technology, pushing the boundaries of the possible. The new OPLS implementation in the latest version is one of those unique functionalities that keep adding to the gap between SIMCA-P and any other competition. QUALITY – To comply with the highest industry standards, SIMCA-P is extensively tested and validated and the validation report is made available to all customers. Version 12 has an even more improved audit trail function. SIMCA-P+ are compliant with 21 CFR Part 11 (Electronic Records). Major pharmaceutical companies have audited Umetrics quality system for software development and validation. The audit trail is administrator controlled and check-sum protected.		DOWNLOADS AND LINKS Demo You can download a time limited evaluation version of SIMCA-P+ 12.0. At this time the batch functionality is not available in the demo. Please contact Umetrics if you would like to evaluate the batch functionality or if you need more information. Brochures Find a SIMCA-P+ brochure and technical specification in the Downloads section. User Guide and Tutorial You can download the SIMCA-P+ User Guide and Tutorial. The tutorial helps you get started with SIMCA-P+ by running through a series of examples step by step. Review Read Scientific Computing World's review of SIMCA-P+. What is multivariate technology? The Methods menu give you a brief introduction to the methodology behind SIMCA-P+. The knowledge base article Frequently Asked Questions about Multivariate Data Analysis (Q391) may also be a help. The best way to learn all you need about the methods and how to use SIMCA-P+ to transfer your data into information, is to attend one of our training courses. Support See the Technical Support menu to find out how to get assistance with using SIMCA-P+. ArtBuilder add-on Using ArtBuilder for creating new image collections for SIMCA-P+ (Q544) – you can add your own pictures to SIMCA-P+. MIN SYSTEM REQUIREMENTS • Pentium II 300 MHz • 512 MB RAM • 50 MB hard disk space • 800x600 screen res., high-color (16 bits) • Windows 2000, XP or Vista  • Internet Explorer (Ver. 4.01 SP2 or later) Larger datasets than 1000 x 1000 points require more memory and a faster processor. (See Q204 in the knowledge base.) Detailed information about supported operating systems can be found in Q22 in the knowledge base.
Some of the New Features in SIMCA-P+ 12
OPLS/O2PLS Conventional PLS uses X to construct a model of Y, where the objective is to predict Y from X. The OPLS method is a recent modification of the PLS method, which is designed to handle variation in X that is orthogonal or uncorrelated to Y. OPLS separates the systematic variation in X into two parts, one that is predictive to Y and one that is orthogonal to Y. This partitioning of the X-data provides improved model interpretability, but does not change the predictive power. O2PLS is a generalization of OPLS. In contrast to PLS and OPLS, O2PLS is bidirectional, i.e. X <=> Y, and therefore X can be used to predict Y, and Y can be used to predict X. Additionally, with O2PLS it is possible to partition the systematic variability in X and Y into three parts, (i) the X/Y joint predictive variation, (ii) the Y-orthogonal variation in X, and (iii) the X-unrelated variation in Y.
Cluster Analysis and PLS-trees Large data sets are almost always grouped. To find relationships in such data sets a reasonable division into groups must be made so that each group contains only fairly homogeneous data. To analyse large and complex data sets, one or several clustering algorithms are needed, preferably combined with PLS, OPLS™ or O2PLS™. The approach is analogous to classification and regression trees (CART), but uses the scores of PLS regression models as the basis for splitting the clusters, instead of the individual X-variables. When applied to a pair of matrices, X and Y, the approach results in a sequence of splits of the data observation-wise into a tree structure of PLS models called dendrogram. We call this dendrogram with the associated PLS models a PLS-tree in SIMCA-P+.
The Workset Wizard The new Workset Wizard guides you through all necessary steps from import of the data set to creation of the model. In some cases this is all you need to do before you open the Report Generator. You no longer need the knowledge how you do all the steps and which method to use. Leave it all to SIMCA-P+!
The Analysis Advisor Occasional users are well supported even for more advanced tasks. As you move through the analysis, the new Analysis Advisor keeps up with you providing informative explanations of the plots that you are currently viewing. You even get an on-the-fly suggestion of what to do next.
What Happens If? A key application area of SIMCA-P+ is process analysis and Multivariate Statistical Process Control (MSPC). Modern processes generate large amounts of complex data that SIMCA-P can transform into informative charts and graphs. An attractive feature is “What If”, which interactively helps you understand the multivariate behaviour of your process when changes are introduced to one or more variables simultaneously.
Report Generator This new functionality allows you to generate html reports directly from your SIMCA-P+ projects with ease. It's a great way of sharing results with colleagues and eliminates all that laborious copying and pasting. User-defined templates give you complete control of the report format, while placeholders ensure that the variable parts of your reports are updated automatically.
Chemical Structure Display In QSAR and ADME/Tox modelling, the connection between the analysis results and chemical structure is of vital importance. By providing SMILES code as a secondary ID when importing the data, SIMCA-P+ can automatically visualise relevant chemical structures in parallel with the on-going analysis. Just click on any relevant output and the associated chemical structure will automatically appear. The structural conversion is available as an add-in based on licensed technology from OpenEye. Read more about the addition in this leaflet (124kB pdf).
OPLS (Orthogonal PLS) OPLS is a modification of the usual PLS method that filters out variation that is not directly related to the response. The result is more transparent models which are easier to interpret. OPLS is of particular value in spectroscopic calibration, QSAR modelling and "omics" analysis but is set to become the standard in all areas of data analysis. The screen shot shows how OPLS enhances class separation in a proteomics study (100 ovarian cancer patients versus 92 unaffected women). OPLS is a patented technology by Umetrics. Read more on this poster (392kB pdf).

Batch Analysis SIMCA-P+ is the standard software for multivariate data analysis. SIMCA-P+ has also everything that you require for multivariate batch analysis. SIMCA-P+ provides unique batch modelling capability, dealing with phases, maturity and modelling of both the upper and lower levels. Models created by SIMCA-P+ can be transferred to SIMCA Batch On-Line, for execution in real-time. SIMCA-P + uses a hierarchical approach to batch modelling. First the evolution of the batches is modelled. Then on the batch level the whole batches are modelled as units. These models use the initial conditions plus summaries of the evolution as variables. This allows modelling, control charts, contribution plots, and interpretation of both the evolution with individual process points as observational units, and of the completed batches as units.