Sartorius Stedim Data Analytics SIMCA 16 software for Multivariate Data Analytics
Sartorius Stedim Data Analytics has announced SIMCA 16 software for multivariate data analytics. The updated SIMCA focuses on delivering a complete data analysis experience, from data organisation through to data-driven decision-making, supported by multivariate models for single and multiblock analysis.
The software has functionality features that will save time for expert users, as well as those new to multivariate data analysis. Usability improvements provide novices with an intuitive introduction to SIMCA and existing users with good plot interactivity and quick raw data visualisation capabilities. The software’s updated graphical interface with its context-based ribbons and panes means scientists will spend less time looking for functions and the ribbons will be especially useful for those working with batch data.
The software includes a wizard that adapts to users’ modelling objectives and guides them through set-up, making the initial steps of creating each model easier. Its advanced data merging functionality saves time by eliminating the need to manually combine and align data in Excel.
The software comes with novel score space exploration and multivariate solver tools which help turn models into real-life factor combinations. In just one click, the score space exploration tool allows users to convert scatter plots into real factor settings to, for example, detect which sample is missing in a stack of observations. With the multivariate solver tool, scientists can determine optimum factor settings for desired process outputs such as critical quality attributes and can also lock model parameters to a specific batch of raw material to find the process parameters for achieving consistent product quality and operational efficiency. Both tools make troubleshooting process data and performing deviation analysis simpler tasks.
To increase application and functional flexibility, the software includes MOCA, a novel tool for analysing more than two blocks of data, and Python plug-in capability. MOCA provides a quick overview of an entire system, delivering information for continuing analysis, and is suitable for scientists such as systems biologists wanting to compare data from one system that has been obtained using different ‘omics’ and other techniques.
The Python plug-in functionality provides workflow flexibility by enabling users to create a file reader plug-in, which can read files like any other file format as they are being imported. This is especially useful when scientists need to transfer data from a new instrument with a non-standard export format or from text files where data is not configured correctly for SIMCA, saving them time and effort with pre-processing and importing data.
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