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Shapley Additive exPlanations-Integrated Convolutional Neural Networks for Chemically Interpretable Fourier-Transform Infrared-Based Microplastic Characterization
Summary
A CNN-SHAP fusion framework achieved 99.6% accuracy in classifying six common plastic polymers from FTIR spectra while using SHAP attribution to identify the specific spectral features driving each classification decision. Making microplastic identification explainable and chemically interpretable is essential for building scientific confidence in automated monitoring systems used to track plastic pollution in environmental samples.
Fourier-transform infrared (FTIR) spectroscopy is a widely adopted technique for microplastic (MP) identification due to its molecular sensitivity and nondestructive nature. However, many FTIR-machine learning (ML) approaches operate as black-box classifiers, providing limited transparency regarding the spectral regions that drive model decisions and thereby constraining confidence in automated analysis. We present CNN-SHAP fusion, an explainable deep-learning framework that integrates one-dimensional convolutional neural networks (1D-CNNs) with SHapley Additive exPlanations (SHAP) to support postacquisition, attribution-based interpretation of FTIR spectra. The framework combines CNN-derived spectral embeddings with SHAP-weighted wavenumber representations as meta-features within an ensemble learning architecture, enabling the systematic evaluation of model sensitivity across the infrared spectrum. Using standardized preprocessing and a balanced dataset comprising six common polymers (HDPE, LDPE, PET, PP, PS, and PVC) measured under controlled laboratory conditions, CNN-SHAP fusion achieves a mean cross-validated classification accuracy of 99.6%. Attribution analysis indicates that model predictions are primarily influenced by spectral regions that are diagnostically relevant for polymer identification, including aliphatic C-H stretching, carbonyl-associated bands, aromatic features, and characteristic fingerprint-region patterns. These attribution profiles are consistent with established FTIR assignments for the polymers examined and remain stable across the cross-validation folds. CNN-SHAP fusion provides a transparent and reproducible framework for FTIR-based microplastic classification in laboratory and batch-processing workflows. By linking predictive performance with spectrally interpretable attribution, the approach supports the informed evaluation of model behavior and establishes a foundation for future validation under more complex and environmentally representative conditions.