At Purency, we use machine learning to automate certain processes that are too time-intensive to be done by humans. Machine learning models are ultimately mathematical models which were created from training data by a machine learning algorithm. So, to solve the microplastics classification problem, we trained a model to decide which pixel is a polymer and which one is not. We also taught it to distinguish between different types of polymers. There are different approaches to train machine learning models: supervised and unsupervised learning. In unsupervised learning you simply feed data into the algorithm and the algorithm learns by itself what the outcome should be. In supervised learning, the algorithm is trained with labelled data, which is the outcome of the classification process. Purency uses supervised learning and trains the model based on representative data which was compiled and analysed by microplastics experts around the world. Thereby, providing a fully trained, out of the box, and robust method to laboratories analysing microplastics – enabling you to identify not just what one person, but a group of microplastics experts would find in a sample.