CNNs for Vis-NIR Chemometrics: From Contradiction to Conditional Design

• URL: http://arxiv.org/abs/2605.02636v1
• Date: Mon, 04 May 2026 14:21:02 GMT
• Title: CNNs for Vis-NIR Chemometrics: From Contradiction to Conditional Design
• Abstract summary: Near-infrared (NIR) deep-learning studies in chemometrics increasingly report mutually inconsistent conclusions regarding convolutional neural network (CNN) design.<n>The same architecture can appear superior in one study and inferior in another, creating a practical impasse for chemometric practitioners.<n>We argue that these contradictions are not evidence of irreconcilable methods but a structurally expected consequence of uncontrolled variables.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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