This example uses the Fermentation Process.jmp and Fermentation Process Batch Yield Results.jmp sample data tables to analyze enzyme production. Yield is the amount of an enzyme produced by genetically modified yeast. There are 100 process measurements per batch that were taken at equally spaced times over a 12-hour period.
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Select Analyze > Specialized Modeling > Functional Data Explorer.
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Click OK.
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Click the Functional Data Explorer Group red triangle and select Data Processing > Align > Align Range 0 to 1. This aligns the input variable to be between 0 and 1 in each Functional Data Explorer report.
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Click the Functional Data Explorer Group red triangle and select Models > B-Splines. This fits a B-spline model to each of the functional processes.
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Figure 14.7 Functional Data Explorer Report for Ethanol
Figure 14.8 Model Summary Report for Ethanol
Figure 14.7 and Figure 14.8 show the model reports for one of the functional process variables, Ethanol. Scroll through the full report to view the models fit for each of the process variables. Next, use the FPCs in the Function Summaries report in an analysis.
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Press Ctrl, click any Function Summaries red triangle, and select Customize Function Summaries.
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In the box next to Enter number of FPCs to show, type 3.
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Click the Deselect All Summaries box.
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Click OK.
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Click the Functional Data Explorer Group red triangle and select Save Summaries.
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In the Functional Data Explorer Model Summaries.jmp data table, right-click BatchID and deselect Link ID.
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In the Functional Data Explorer Model Summaries.jmp data table, right-click BatchID and select Link Reference > Fermentation Process Batch Yield Results.jmp.
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Use the Generalized Regression personality of the Fit Model platform to determine how Yield is affected by the functional process variables.
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Click Run.
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Click Go.
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Figure 14.9 Generalized Regression Report for Batch Yield
The Generalized Regression report shows that Yield is significantly affected by certain components of Ethanol, Molasses Feed, NH3 Feed, and Air. The RSquare for the model is 0.732545. By using FDE to perform dimension reduction on the functional processes first, you greatly reduce the number of variables, while still retaining the ability to build a reasonable prediction models.