Model Building: Analysis Phases Overview

Table of Contents | Previous | Next

Analysis Phases

The Analysis window serves as the core interface to the Solo modeling and analysis functions. You create your models in an Analysis window, apply models in this window, and also analyze and explore the models in this window. Three phases are required to completely carry out modeling and analysis in the Analysis window-the Calibration phase, the Test and Validation phase, and the Model Application phase.

Calibration phase

The Calibration phase consists of model building and exploratory analysis. In this phase, which affects only the Calibration side of the Status pane, you must load data into the X calibration control. This data is referred to as x block data, and it is a set of multivariate measurements on your data samples. Some analysis methods also require you to load data into the Y calibration control. This data is referred to as y block data and it is a set of secondary or reference measurements on the same data samples. During analysis, you identify any patterns or trends in the data, and any other information that you consider relevant, for example, any relationships that might exist between the x data and the y data, and use this information to build a model. See Building the Model in the Calibration Phase.

Test and Validation phase

The Test and Validation phase consists of applying the model that you built in the Calibration phase to your validation data, which is data with known physical and/or chemical characteristics. In this phase, which affects the Validation side of the Status pane, you must load data into to the X validation control, and if applicable, the Y validation control. As is the case in the Calibration phase, the data that you load into the X control is referred to as x block data, and it is a set of multivariate measurements on your data samples. Likewise, the data that you load into the Y control is referred to as y block data and it is a set of secondary or reference measurements on the same data samples. You use this validation data to confirm that the model that you built captures valid patterns and trends in the data. You test and validate the model by applying it to the validation data and verifying that the test results are acceptable. For example, PCA analysis is typically used for pattern recognition. A correctly built PCA model, therefore, can identify the instances for which this pattern has been broken, such as a failure in material that does not meet specifications. During the Test and Validation phase of a PCA model, some of the validation data samples should meet specifications and some of the validation data samples should be "out of spec." A well-built PCA model will identify or flag these "out of spec" samples. If the test results are acceptable, you can continue to the next phase, the Model Application phase. If the test results are not acceptable, you must return to the Calibration phase. See Applying the Model in the Test and Validation Phase.

Model Application phase

The Model Application phase consists of applying the tested and verified model to new data, which is data with unknown characteristics, and therefore, the results of applying the model cannot be known in advance. If your test results, however, were acceptable in the Test and Validation phase, then the results from the Model Application phase are also likely accurate. For example, a correctly built PCA model that was successfully tested and validated in the Test and Validation phase should identify "out of spec" samples during the Model Application phase.