XAI-2DCOS: Enhancing Interpretability in Spectral Deep Learning Models Through 2D Correlation Spectroscopy

Abstract

Deep learning (DL) has significantly advanced Raman spectra analysis, achieving high accuracy and efficiency. However, their complexity and opacity limit their application in areas where understanding and transparency are essential. To address this, we present XAI-2DCOS, an innovative eXplainable Artificial Intelligence (XAI) framework that employs 2D correlation spectroscopy (2DCOS). Traditionally, 2DCOS reveals the sequence of molecular changes under varying conditions. We repurpose it to enhance the interpretability of DL models by linking changes in spectral features to model outputs, identifying critical wavenumbers, and how their variations affect model accuracy. We applied XAI-2DCOS to a DL model trained on a dataset of oil Raman spectra, demonstrating its ability to identify critical spectral features that align with domain knowledge. To improve robustness, we integrated a conditional generative adversarial network (CGAN) for data augmentation. CGAN generates synthetic data, ensuring the presence of spectra across the entire probability range. A normalized relevance score quantifies the contribution for each wavenumber to the model's prediction. A predictive probability map delineates decision boundaries within the PCA space. Synchronous 2DCOS maps are used to guide spectral adjustments that improve prediction confidence for specific class predictions. These adjustments can affect multiple output classes with differential scaling of output activations, suggesting that crossing a threshold can shift the model decision. Our results show that XAI-2DCOS improves the interpretability and reliability of DL models applied to Raman spectra. Furthermore, CGAN data augmentation extends the applicability of XAI-2DCOS to smaller datasets.