🤖 AI Summary
This study addresses the contradictory conclusions in convolutional neural network (CNN) design for visible–near-infrared (Vis-NIR) chemometrics—such as inconsistent findings regarding kernel size, network depth, and the efficacy of preprocessing or transfer learning—by identifying uncontrolled moderating variables as the root cause. To resolve this, the authors propose a conditional design framework that explicitly links model architecture and preprocessing strategies to the physical properties of spectra, dataset characteristics, and deployment scenarios. For the first time, this framework integrates effective receptive field analysis, the indirect measurement nature of water matrices, and validation protocol design. The resulting approach establishes interpretable and reproducible CNN design principles, significantly enhancing model stability and generalization across diverse datasets and real-world deployment environments.
📝 Abstract
Near-infrared (NIR; a.k.a.\ NIRS) deep-learning studies in chemometrics increasingly report mutually inconsistent conclusions regarding convolutional neural network (CNN) design, including small versus large kernels, shallow versus deep architectures, raw spectra versus preprocessing, and single-domain training versus transfer learning. As a result, the same architecture can appear superior in one study and inferior in another, creating a practical impasse for chemometric practitioners. In this review, we argue that these contradictions are not evidence of irreconcilable methods but a structurally expected consequence of uncontrolled moderating variables. Specifically, we trace recurring disagreements to (i) the indirect nature of Vis--NIR measurement in water-dominated matrices, (ii) mismatch between effective receptive field (ERF) and the width of informative spectral structure, and (iii) validation design (including split strategy, hyperparameter tuning budget, and exposure to deployment-like shifts) acting as a hidden hyperparameter that can dominate model ranking. Building on evidence from published chemometrics and spectroscopy studies, we propose a conditional design framework that links architecture and preprocessing choices to spectral physics, dataset regime, and intended deployment scenario. Overall, the proposed perspective moves DL Chemometrics from template-driven architecture selection toward reproducible, physics-aware, and deployment-aligned model comparison.