基于高光谱数据的纸张粘度反演模型构建

doi:10.3964/j.issn.1000-0593(2024)12-3524-10

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Abstract Paper-based cultural relics hold crucial significance to the historical and cultural heritage and the continuation of the national spirit in China. At the same time, the aging of paper seriously affects the longevity of artifacts. Paper viscosity is an important indicator reflecting the degree of paper aging. Traditional paper viscosity measurement methods are destructive experiments that cause inevitable secondary damage to valuable cultural relics. Hyperspectral remote sensing technology can achieve non-destructive and rapid analysis to address this issue, providing an effective approach to establishing a paper viscosity inversion model. In this study, drying and moist-heat paper aging were taken as the research subjects, and110 groups of simulated paper aging samples with measured viscosity content were obtained. Through spectral filtering, a hyperspectral database of paper aging was established. Based on this, nine data processing methods, including original spectrum first-order derivative, original spectrum second-order derivative, original spectrum reciprocal logarithm, original spectrum reciprocal logarithm first-order derivative, multiplicative scatter correction, multiplicative scatter correction spectrum first-order derivative, multiplicative scatter correction spectrum second-order derivative, multiplicative scatter correction spectrum reciprocal logarithm, and continuous wavelet transform were analyzed, as well as two feature selection methods, competitive adaptative reweighted sampling (CARS) and correlation coefficients (R), were analyzed in combination as input for the models to identify the best model through dataset partitioning validation. Research results have shown: (1) In the original spectrum, the correlation at 430 nm is the highest with viscosity, with an R-value of 0.75. After data processing, the R-value at 430 nm increases to 0.874 following the application ofthe reciprocal logarithm first-order derivative of the original spectrum method, which significantly enhances the paper viscosity information in the spectrum; (2) After the original spectrum is transformed into the second-order derivative, the correlation at 578 nm is the highest, with an R-value of 0.57, significantly lower than the highest R-value (0.75) in the original spectral data. This finding indicates that the second-order derivative is unsuitable for paper viscosity estimation. Meanwhile, the maximum correlation coefficients of other transformation methods are all higher than the original spectrum, demonstrating the effectiveness of spectral transformation; (3) For the original spectrum, as the decomposition scale increases, the paper viscosity information contained in the spectrum decreases, and the highest correlation coefficient occurs at 484nm at the decomposition scale of 2, with a value of 0.873; (4) Among different input combinations, the model with the highest accuracy uses the reciprocal logarithm first-order derivative of the original spectrum as input. Based on the CARS method, the support vector regression (SVR),random forest (RF), and AdaBoost models have R² values of 0.96, 0.93, and 0.93, respectively, and RMSE values of 14.80, 18.33, and 19.79 mL·g^-1 for the validation dataset. We recommend prioritizing using the reciprocal logarithm first-order derivative of the original spectrum as inputs and the SVR model with the CARS feature selection algorithm for paper viscosity inversion. The above research findings demonstrate the applicability of hyperspectral technology for non-destructive analysis of paper viscosity, providing a scientific basis for the restoration work of paper-based cultural relics.

Key words：Paper viscosity; Hyperspectral; Feature selection; Deep learning

Received: 2023-11-14 Accepted: 2024-03-13

ZTFLH:

TP79

Corresponding Authors: QU Liang E-mail: quliang@dpm.org.cn

Cite this article:

WANG Sa,QU Liang,ZHANG Li-fu, et al. Research on the Inverse Model of Paper Viscosity Based on Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3524-3533.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)12-3524-10 OR https://www.gpxygpfx.com/EN/Y2024/V44/I12/3524

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