In the case of supervised models, their accuracy can be verified using two implemented cross-validation approaches:

Leave-One-Batch-Out cross-validation;
10-fold cross-validation.

Additional validation of the constructed model can be done by applying it to a labelled test data set or it can be used to predict an unlabelled test data set. In this case, test data and training data are processed in the same way and then the pre-constructed model is applied. Alternatively, the model and parameters can be exported and saved or sent to another user for further use.

When completed, the analysis data is displayed as a printable results report with clear information in the form of diagrams, a summary of the metadata and the prediction. In addition to the report, detailed results can be exported as csv files listing all pre-processed spectra, metadata tables and predictions for each spectrum.