A Bayesian Inference-Based Method for Uncertainty Analysis in Raman Spectroscopy

Raman spectroscopy is an important analytical technique with advantages in non-destructive and rapid analysis, and it is widely used in fields such as chemical analysis, materials science, and biomedical research. The integration of Raman spectroscopy with deep learning methods has been shown to produce excellent chemical analysis results. However, challenges arise when neural networks are faced with issues such as environmental noise and the analysis of samples outside the training dataset, where the reliability and interpretability of the prediction results become critical. Bayesian inference allows for the updating of the hypothesis probability of the neural network’s predictions, thereby incorporating additional evidence and information. To address the issue of uncertainty analysis in Raman spectroscopy predictions, this paper proposes a novel uncertainty analysis method based on the combination of neural networks and Bayesian inference, referred to as BayesianVGG. This method achieves accurate sample classification while quantifying the confidence of the model’s output results through Bayesian inference, significantly enhancing the robustness of Raman spectroscopy analysis in complex environments. In experiments on an animal blood Raman spectroscopy dataset (both reflection and transmission modes), the proposed method achieves classification accuracies of 95.36% and 94.83%, respectively, showing slight improvements over other classical machine learning and neural network methods. Furthermore, by generating prediction confidence heatmaps, BayesianVGG effectively addresses the uncertainty analysis of unknown samples, thereby improving the interpretability of the prediction results.