EXPRESS: Attention-based Multi-level Fusion for NIRS Modeling.

Authors

Hongyu Wang, Xiaoli Luan, Fei Liu

Published in

Applied spectroscopy. Pages 37028261474232. Jul 22, 2026. Epub Jul 22, 2026.

Abstract

Near-Infrared Spectroscopy (NIRS) can directly provide product quality information at the molecular level, such as concentration, viscosity, etc. However, NIRS is overly sensitive to environmental noise. Therefore, how to make full use of the information of process variables to enhance the robustness of NIR modeling is the key. For this purpose, this paper presents a multi-level fusion model based on Attention Mechanism (AM). First, two-branch Convolutional Neural Network (CNN) is introduced to extract the feature of NIRS. Then, a fusion block based on AM is designed, which consists a cross-attention operation, a residual connection, and a Multi-layer Perceptron (MLP). The residual connection weights and integrates features of NIRS and process variables according to the cross-attention distribution, while the MLP further combines the features in the feature dimension. Additionally, considering different information exchanged ways of fused features at each level of two-stream CNN, two different fusion schemes are designed namely central and shared strategies. Experiments on the 2,6-dimethylphenol distillation process datasets demonstrate that proposed method outperforms some existing methods.

PMID:
42485546
Bibliographic data and abstract were imported from PubMed on 23 Jul 2026.

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