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