Convolutional Neural Networks in Vis-NIR Chemometrics: From Contradiction to Conditional Design
Near-infrared (NIR and Vis-NIR) spectroscopy is widely used for rapid, non-destructive analysis in food, agriculture, pharmaceuticals, process analytical technology, and bioprocess monitoring. Nevertheless, deep-learning studies in NIR chemometrics often reach conflicting conclusions about convolutional neural network (CNN) design, including kernel size, depth, preprocessing, model complexity, and transfer robustness. This review argues that many apparent contradictions reflect incomplete experimental conditioning rather than incompatible findings. CNN performance depends on interactions among spectral physics, dataset regime, acquisition protocol, validation design, and deployment condit...
| Authors | Dário Passos |
|---|---|
| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 28 July 2026 |
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