S-Matrix was incorporated in the State of California in 1985. The company started as a consulting service, applying statistical tools and methods to solve industrial research and development problems in a variety of industries, including Aerospace, Fine Chemicals, Semiconductor, Consumer Products, and Pharmaceuticals.
For the past two decades S-Matrix has been developing advanced and innovative approaches to experimental design and multivariate data analysis according to Quality-by-Design principles, and incorporating these approaches into its Fusion AE expert-system software product suite. Fusion AE supports full 21 CFR 11 compliance and offers a built-in workflow management system with full user permissions and authorities control and e-review/e-approve workflow control.
S-Matrix’s strategic business alliances with international Pharmaceutical, Analytical Instrument, and Process Equipment companies enable S-Matrix to rapidly automate targeted R&D experimentation applications within the expert-system framework of its innovative Quality-by-Design software platform.
Just some of the innovations developed and offered first by S-Matrix include:
- Automated Design of Experiments (DOE) for Chromatography Data Software (CDS)
S-Matrix’s patented Fusion AE™ platform (U.S. Patent No. 7,239,966 B2) exports statistical experimental designs to the CDS as ready-to-run instrument methods and sequences in the native file/data formats of the CDS, and imports all chromatographic results for instant data analysis and reporting. All of these exchanges use file-less data transfer protocols which leave no data outside a regulatory compliant software environment.
- Advanced Optimality Designs
S-Matrix’s algorithm design generators use a combination of optimality criteria to assure (1) uniform sampling (coverage) of experimental design regions, and (2) sampling of the interior of the experimental design region where complex variable effects are commonly expressed. These designs are proven to be far superior to simple D-optimal designs from a diagnostic and predictive modeling (data-2-knowledge) standpoint.
- Automated and User-interactive Operating Modes for All Product Modules
Automated mode – all statistical decisions by which the experimental design is selected and the data analysis is performed are automatically made for the user based on rigorous statistical principles and criteria, and all decisions are documented in the design and analysis reports.
User-interactive mode – wizard dialogs guide the user through all statistical decisions by which the experimental design is selected and data analysis is performed. All valid options are displayed with the software’s recommendations highlighted and pre-selected by default. All user selections are documented in the design and analysis reports.
- Advanced Repair Designs (Data Mining Support)
S-Matrix’s algorithm design generators can diagnose the information content of existing data sets in terms of the data’s ability to support development of diagnostic and predictive multivariate statistical models (data-2-knowledge). If the data set does not support full model development, the design generators can create efficient "Repair Designs" which define the specific additional data required to fill in all information gaps with the minimum number of additional experiment runs.
- Novel Chromatographic Method Performance Characterizations
S-Matrix’s patented Trend Responses™ technology (U.S. Patent No. 7,613,574 B2) overcomes the limitations inherent in both the sequential and classical Design of Experiments (DOE) approaches for critical quality attributes in liquid chromatography such as peak resolution to place Design of Experiments (DOE) activities on a rigorous and quantitative footing.
- Robustness Simulation
S-Matrix’s patented Robustness Simulator™ technology (U.S. Patent No. 7,606,685 B2) is the first software to completely integrate (DOE, Monte Carlo simulation, and Statistical Process Control (SPC) techniques and methods into expert-system R&D software. The result is the ability to efficiently study any product or process and simultaneously optimize it for both mean (average) performance and robustness in terms of all critical quality attributes.