Above is a screenshot of Analyze-IQ as used to build a new model. The lefthand pane of this screenshot shows a set of spectra from the currently opened database; the user has selected a subset of these for the model. The righthand pane shows the set of machine learning methods that are available for building a model. In this example, the user has selected the PCANipals with k-NN to build a classification model. This interface also allows the user to set the appropriate parameters, e.g. the number of principal components and the k value for the k-NN classifier. Once a model has been built, it can be used to make predictions (identification or quantification) on test samples. Below is a screenshot of Analyse-IQ being used to make a prediction on a test sample, the spectrum of which is shown in the middle of the screenshot. In this example, a classification model, which was designed to identify acetonitrile has been applied to the test sample and has determined acetonitrile is not present in the sample. With the Analyse-IQ software, a number of pre-built models may be applied to a single sample spectrum.

 

 

In this way, users of the Analyze-IQ software can execute any of the machine learning techniques developed in QRS/ML to build their own classification and regression models based on their own spectral data.