Complex applications benefit from the increased prediction accuracy and sensitivity to outliers (e.g., unusual samples, process upsets) associated with a multivariate approach. Generating a decision tree around a classification model (predicting a sample category), a regression model (predicting a continuous property or concentration), or any combination of both model types allows predefined rules to be applied to complex problems. When coupled to a given instrument system, the result is a turn-key custom analyzer.
The key aspects of InStep are:
- its behind the scene use of Pirouette to make predictions
- a form-based interface for creating decision trees using multivariate models
- a “watched folder” approach which allows it to run concurrently with instrument data acquisition programs
- generation of custom reports which can be displayed on-screen and/or saved to a file
- presentation of control charts for visual monitoring of results and diagnostics
InStep Features:
- Easy setup of InStep prediction methods
- Complex heirarchical methods are possible with InStep
- Specify location for files to be processed
- Create a report format for the predictions
- Customize the output with meaningful commentary