  Convolution neural networks were applied to classify speckle images generated
from nano-particle suspensions and thus to recognise suspensions. The speckle
images in the form of movies were obtained from suspensions placed in a thin
cuvette. The classifier was trained, validated and tested on both single
component monodispersive suspensions, as well as on two-component suspensions.
It was able to properly recognise all the 73 classes - different suspensions
from the training set, which is far beyond the capabilities of the human
experimenter, and shows the capability of learning many more. The classes
comprised different nanoparticle material and size, as well as different
concentrations of the suspended phase. We also examined the capability of the
system to generalise, by testing a system trained on single-component
suspensions with two-component suspensions. The capability to generalise was
found promising but significantly limited. A classification system using neural
network was also compared with the one using support vector machine (SVM). SVM
was found much more resource-consuming and thus could not be tested on
full-size speckle images. Using image fragments very significantly deteriorates
results for both SVM and neural networks. We showed that nanoparticle
(colloidal) suspensions comprising even a large multi-parameter set of classes
can be quickly identified using speckle images classified with convolution
neural network.
