Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data

Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and manual analysis either unreliable or time-consuming. To address challenges, we explored a dense feed-forward neural network (DNN) for classifying Fourier transform infrared (FTIR) spectroscopic data. The DNN provides conditional class distributions over 16 microplastic categories given an FTIR spectrum, exceeding number of categories in other works. Our results indicate that this DNN, which is significantly smaller than contemporary models, outperforms other models and even human classification performance. Specifically, while the model broadly reproduces the decisions of human annotators, in cases of disagreement either both were incorrect or the human annotation was incorrect. The errors not being reproduced indicate that the DNN is making informed generalisable decisions. Additionally, this work indicates that there exists an upper limit on metrics measuring performance, where metrics measure agreement between human and model predictions. This work indicates that a small and efficient DNN can making high throughput analysis of difficult FTIR data possible, where predictions match or exceed the reliability typical to low-throughput methods.