Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopy

• URL: http://arxiv.org/abs/2509.25964v1
• Date: Tue, 30 Sep 2025 09:01:38 GMT
• Title: Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopy
• Authors: Deniz Soysal, Xabier García-Andrade, Laura E. Rodriguez, Pablo Sobron, Laura M. Barge, Renaud Detry,
• Abstract summary: We evaluate one-dimensional convolutional neural networks (CNNs) and report four advances.<n> compact CNNs surpass $k$-nearest-neighbors on handcrafted features.<n>label-efficient learning: semi-supervised generative adversarial networks and contrastive pretraining raise accuracy by up to $11%$ with only $10%$ labels.<n>This workflow involves training on raw spectra, tuning pooling, adding semi-supervision when labels are scarce, and fine-tuning lightly for new targets.
• Score: 1.1545092788508224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous Raman instruments on Mars rovers, deep-sea landers, and field robots must interpret raw spectra distorted by fluorescence baselines, peak shifts, and limited ground-truth labels. Using curated subsets of the RRUFF database, we evaluate one-dimensional convolutional neural networks (CNNs) and report four advances: (i) Baseline-independent classification: compact CNNs surpass $k$-nearest-neighbors and support-vector machines on handcrafted features, removing background-correction and peak-picking stages while ensuring reproducibility through released data splits and scripts. (ii) Pooling-controlled robustness: tuning a single pooling parameter accommodates Raman shifts up to $30 \,\mathrm{cm}^{-1}$, balancing translational invariance with spectral resolution. (iii) Label-efficient learning: semi-supervised generative adversarial networks and contrastive pretraining raise accuracy by up to $11\%$ with only $10\%$ labels, valuable for autonomous deployments with scarce annotation. (iv) Constant-time adaptation: freezing the CNN backbone and retraining only the softmax layer transfers models to unseen minerals at $\mathcal{O}(1)$ cost, outperforming Siamese networks on resource-limited processors. This workflow, which involves training on raw spectra, tuning pooling, adding semi-supervision when labels are scarce, and fine-tuning lightly for new targets, provides a practical path toward robust, low-footprint Raman classification in autonomous exploration.

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