High-accuracy leather species identification via Raman spectroscopy and attention-enhanced 1D-CNN

Abstract

Leather derived from different animal sources exhibits significant differences in both performance and value. Traditional leather identification methods suffer from subjectivity, inefficiency, and high costs, motivating the need for rapid, objective, and cost-effective alternatives. To achieve rapid and non-destructive classification of leather types, our study introduces a novel combination of Raman spectroscopy and a one-dimensional convolutional neural network (1D-CNN) enhanced with a self-attention mechanism to efficiently capture subtle spectral differences among leather types. A total of 1066 Raman spectra from cow, sheep, pig, and crocodile leathers were collected. Spectral data underwent smoothing, baseline correction, and normalization. Seven samples from each leather class were randomly assigned to the training set, while the remaining three samples per class were designated as an independent validation set. Data augmentation was performed by adding Gaussian noise and applying slight spectral shifts to simulate real-world variability, expanding the training set to 3,810 samples. The proposed 1D-CNN model incorporates a self-attention mechanism to extract key spectral features and is compared with machine learning models and 1D-CNN models that do not integrate attention mechanisms. Experimental results demonstrate that our method outperforms existing approaches. After incorporating the self-attention mechanism, the model maintained a high accuracy during cross-validation, while its average classification accuracy on the independent test set increased from 92.11 % to 96.28 %. This result demonstrates that the proposed approach achieves enhanced generalization performance under different data partitioning schemes. This efficient, non-destructive, and reliable method not only enables accurate leather species identification and luxury goods authentication, but also shows promise for broader material classification and quality control applications.