Automating infrared spectra interpretation in microplastic identification is of interest since most current methodologies are conducted manually or semi-automatically, which requires substantial processing time and presents a higher accuracy limited to single-polymer materials. Furthermore, when it comes to multicomponent or weathered polymeric materials commonly found in aquatic environments, identification usually becomes considerably depreciated as peaks shift and new signals are frequently observed, representing a significant deviation from reference spectral signatures. Therefore, this study aimed to develop a reference modeling framework for polymer identification through infrared spectra processing, addressing the limitations above. The case study selected for model development was polypropylene (PP) identification, as it is the second most abundant material in microplastics. Therefore, the database comprises 579 spectra with 52.3% containing PP to some degree. Different pretreatment and model parameters were evaluated for a more robust investigation, totaling 308 models, including multilayer perceptron and long-short-term memory architectures. The best model presented a test accuracy of 94.8% within the cross-validation standard deviation interval. Overall, the results achieved in this study indicate an opportunity to investigate the identification of other polymers following the same framework.
Keywords: Algorithms; Artificial intelligence; Infrared spectroscopy; Machine learning; Micropolymers; Polymer characterization.
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