New Sartorius Stedim Biotech Software for Multivariate Data Analytics

The new SIMCA 16 software from Sartorius Stedim Biotech for multivariate data analytics is available from its subsidiary, Sartorius Stedim Data Analytics. The updated software enables data organization and decision making supported by multivariate models for single and multiblock analysis.

The new software’s functionality features include usability improvements that provide users with enhanced plot interactivity and quick raw data visualization capabilities. 

The updated graphical interface has context-based ribbons and panes that help reduce time spent looking for functions. The new ribbons will be especially useful for working with batch data, according to the company. The software also includes a wizard that adapts to users’ modeling objectives rather than focusing on which algorithm to use and guides them through set-up. Additionally, a data-merging functionality eliminates the need to manually combine and align data in Excel.

The software comes with new score space exploration and multivariate solver tools that help turn models into real-life factor combinations for easier pattern data interpretation and use. 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 in one click. With the multivariate solver tool, scientists can determine optimum factor settings for desired process outputs, such as critical quality attributes. The multivariate solver tool 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. The company states that both tools make trouble shooting process data and performing deviation analysis simpler tasks.

To increase application and functional flexibility, the software includes MOCA, a newl tool for analyzing more than two blocks of data and new Python plugin capability. MOCA provides a quick overview of an entire system and delivers information for continuing analysis. MOCA is suitable for scientists such as systems biologists who want to compare data from one system that has been obtained using different “omics” and other techniques.

The Python plugin functionality provides greater workflow flexibility by enabling users to create a file reader plugin that 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 the software, the company reports. Additionally, the use of the software is recognized by the European Medicines Agency and FDA for real-time release testing.

“Developing and producing biotherapeutics generates a vast array of process data in different formats from a variety of equipment types. This data holds the key to improving performance but can be challenging to input, model, and interpret,” said Stefan Rännar, product manager at Sartorius Stedim Data Analytics, in a May 8, 2019 press release. “We’re pleased to introduce our new SIMCA 16 because it offers elevated levels of control over data, model generation and decision making, enabling scientists to optimize resource use and cost efficiency, while more importantly, achieving consistency in their product quality.”

Source: Sartorius Stedim Biotech