Using Data-Derived Priors to Guide CNN Architecture Design for NIR Chemometrics

Applying Convolutional Neural Networks (CNNs) to near-infrared (NIR) chemometrics often involves trial-and-error or computationally expensive hyperparameter optimization (HPO) because there is no consensus on how to adapt architectures to specific spectral properties. This paper investigates whether measurable dataset descriptors—such as spectral entropy, autocorrelation, and wavelet-scale structure—can act as empirical priors to guide the initial design of shallow 1D-CNNs, effectively reducing the need for exhaustive search.

The author curated 25 distinct NIR regression tasks and computed a set of statistical descriptors for each. Two interpretable 1D-CNN scaffolds were defined: a minimalist single-convolution model and an extended model with branching, dilation, and dropout. The study performed Bayesian HPO on these models across all tasks to identify near-optimal configurations. By mapping the relationship between dataset descriptors and these optimal hyperparameters, the author derived 'warm-start' heuristics. These heuristics were then evaluated using both direct application and leave-one-dataset-out (LODO) validation to see if they could predict effective model settings for unseen datasets.

The study found clear, interpretable relationships between spectral descriptors and CNN hyperparameters, particularly regarding convolutional receptive fields. For the minimalist CNN, the preferred kernel size decreased as spectral entropy and intrinsic rank increased, while it increased with the wavelet energy-support fraction. These data-derived heuristics proved highly effective, achieving median test-RMSE ratios of 0.953 (direct) and 1.017 (LODO) compared to full HPO. Furthermore, joint optimization of preprocessing and CNN hyperparameters outperformed standardized-spectra approaches in 19 of 25 tasks, suggesting that integrating data-driven priors into the design pipeline is a robust strategy for NIR chemometrics.