Convolution neural networks were applied to classify speckle images generated from nanoparticle 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.

应用卷积神经网络对从纳米颗粒悬浮液产生的斑点图像进行分类，从而识别悬浮液。电影形式的斑点图像是从置于薄比色皿中的悬浮液获得的。在单组分单分散性悬浮液以及双组分悬浮液上均对分类器进行了培训，验证和测试。它能够正确识别所有73个班级–不同于训练集的暂停，这远远超出了人类实验人员的能力，并显示出学习更多功能的能力。这些类别包括不同的纳米颗粒材料和大小，以及不同浓度的悬浮相。我们还研究了该系统的概括能力，通过测试训练有单组分悬浮液和双组分悬浮液的系统。发现归纳能力很有希望，但受到很大限制。还比较了使用神经网络的分类系统和使用支持向量机（SVM）的分类系统。SVM被发现消耗更多的资源，因此无法在全尺寸斑点图像上进行测试。使用图像片段会极大地降低SVM和神经网络的结果。我们表明，即使使用包含卷积神经网络分类的斑点图像，也可以快速识别甚至包含大型多参数类集的纳米粒子（胶体）悬浮液。还比较了使用神经网络的分类系统和使用支持向量机（SVM）的分类系统。SVM被发现消耗更多的资源，因此无法在全尺寸斑点图像上进行测试。使用图像片段会极大地降低SVM和神经网络的结果。我们表明，即使使用包含卷积神经网络分类的斑点图像，也可以快速识别甚至包含大型多参数类集的纳米粒子（胶体）悬浮液。还比较了使用神经网络的分类系统和使用支持向量机（SVM）的分类系统。SVM被发现消耗更多的资源，因此无法在全尺寸斑点图像上进行测试。使用图像片段会极大地降低SVM和神经网络的结果。我们表明，即使使用包含卷积神经网络分类的斑点图像，也可以快速识别甚至包含大型多参数类集的纳米粒子（胶体）悬浮液。