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<p>Conventional “sample-destructive—offline” methods for determining the Mooney viscosity (ML) of natural rubber (NR) are time-consuming, costly, and unsuitable for on-site process control. In this study, we propose an end-to-end, non-destructive framework that directly predicts ML from raw NR sheets using visible–near-infrared hyperspectral imaging (Vis–NIR HSI, 420–870&#xa0;nm, 150 sampled bands at 2&#xa0;nm intervals) coupled with a deep regression network. A total of 540 NR specimens were imaged under strict darkroom conditions. We developed a one-dimensional convolutional neural network augmented with a Squeeze-and-Excitation (SE) spectral attention module—termed SE-Attention-1D-CNN—to extract multi-scale spectro-chemical features while adaptively reweighting wavelength channels associated with viscosity-related moieties. The model was trained using an L2-regularized mean squared error (MSE) loss, the Adam optimizer, four-fold cross-validation, and early stopping to ensure generalization.The proposed network achieved an average correlation coefficient(R) of 0.873, coefficient of determination (R2) of 0.762, root mean square error (RMSE) of 4.037, mean absolute error (MAE) of 3.364, and residual predictive deviation (RPD) of 2.003. Ablation experiments further demonstrated the effectiveness of the SE module. Specifically, after removing the SE module, the CNN model showed inferior performance, with R and R<sup>2</sup> decreasing to 0.827 and 0.684, respectively, RMSE and MAE increasing to 4.645 and 3.775, respectively, and RPD dropping to 1.761. These results highlight the crucial role of attention-driven spectral weighting in improving predictive accuracy and model robustness. By embedding the SE mechanism within a 1D CNN, this study enables high-precision, non-destructive ML prediction and provides a novel approach for the intelligent grading of natural rubber in industrial settings.</p>

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SE-Attention-1D-CNN: A Deep Learning Framework for Nondestructive Mooney Viscosity Prediction in Natural Rubber Using Vis–NIR Hyperspectral Imaging

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Xu Zhang,
  • Ming Liu,
  • Yun Li,
  • Lijun Yun,
  • Zaiqing Chen

摘要

Conventional “sample-destructive—offline” methods for determining the Mooney viscosity (ML) of natural rubber (NR) are time-consuming, costly, and unsuitable for on-site process control. In this study, we propose an end-to-end, non-destructive framework that directly predicts ML from raw NR sheets using visible–near-infrared hyperspectral imaging (Vis–NIR HSI, 420–870 nm, 150 sampled bands at 2 nm intervals) coupled with a deep regression network. A total of 540 NR specimens were imaged under strict darkroom conditions. We developed a one-dimensional convolutional neural network augmented with a Squeeze-and-Excitation (SE) spectral attention module—termed SE-Attention-1D-CNN—to extract multi-scale spectro-chemical features while adaptively reweighting wavelength channels associated with viscosity-related moieties. The model was trained using an L2-regularized mean squared error (MSE) loss, the Adam optimizer, four-fold cross-validation, and early stopping to ensure generalization.The proposed network achieved an average correlation coefficient(R) of 0.873, coefficient of determination (R2) of 0.762, root mean square error (RMSE) of 4.037, mean absolute error (MAE) of 3.364, and residual predictive deviation (RPD) of 2.003. Ablation experiments further demonstrated the effectiveness of the SE module. Specifically, after removing the SE module, the CNN model showed inferior performance, with R and R2 decreasing to 0.827 and 0.684, respectively, RMSE and MAE increasing to 4.645 and 3.775, respectively, and RPD dropping to 1.761. These results highlight the crucial role of attention-driven spectral weighting in improving predictive accuracy and model robustness. By embedding the SE mechanism within a 1D CNN, this study enables high-precision, non-destructive ML prediction and provides a novel approach for the intelligent grading of natural rubber in industrial settings.