Vironova Analyzer Software (VAS)

Automation enabled by machine learning

VAS comes with machine learning capabilities making it possible to set up methods to automatically classify nanoparticles. The machine learning method used is called support vector machines (SVM) and is a supervised learning method. This means that the classifier must be trained on a set of manually classified particles before it can be applied to new, unclassified particles. To do this discrimination it uses a predefined combination of features including shape, size and intensity profile.

The trained classifier is proven for the ability to discriminate between filled and empty particles. To do this discrimination it uses a predefined combination of features including shape, size and intensity profile. 

The classifier can be applied to all particles in an mage or to individual particles separatel in a similar way as the manual classification tool.

Time saved with VAS

Significant time can be saved with VAS. In this example it is shown the time required for the analysis of liposome lamellarity in 20 images of a sample with average quality. Using VAS instead of doing the imaging, detection, classification and reporting manually can save your organization 12 hours per sampled analyzed.