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

Autonomous Raman instruments deployed on Mars rovers, deep-sea landers, and mobile field robots must interpret raw spectra that are distorted by fluorescence baselines, peak shifts, and limited ground-truth labels. Using rigorously documented subsets of the RRUFF mineral database, we systematically evaluate one-dimensional convolutional neural networks (CNNs) and report four practical advances: (i) Reproducible, baseline-independent classification: Compact end-to-end CNNs surpass k -nearest-neighbors and support-vector classifiers built on handcrafted peak features, eliminating background-correction and peak-picking stages. Unlike earlier work, we isolate the contribution of learned features and ensure full reproducibility by releasing all data splits and preprocessing scripts. (ii) Pooling controlled robustness: By adjusting a single pooling parameter, CNNs accommodate Raman shift displacements up to 30 cm –1, enabling a practical trade-off between translational invariance and class resolution, aligned with instrument stability and spectral variability. (iii) Label-efficient learning: Semisupervised generative adversarial networks and contrastive pretraining improve classification accuracy by up to 11% when only 10% of labels are available. While smaller than gains in vision tasks–due to Raman spectra’s lower complexity–these methods remain valuable for autonomous deployments with limited annotation. (iv) Constant time adaptation: Freezing the CNNs backbone and retraining only the softmax layer transfers the model to unseen minerals at O ( 1 ) inference cost, outperforming Siamese networks on resource-limited robotic processors. The resulting workflow– train directly on raw spectra, tune pooling to instrument tolerance, add semisupervision when labels are scarce, and fine-tune lightly for new targets –offers a practical path toward robust, low-footprint Raman classification in autonomous field settings.