摘要
含水率是影响纸质文物保存的重要因素之一,为了建立一种快速无损检测纸质文物本体含水率检测方法,以重庆中国三峡博物馆提供的棉料四尺单宣为研究对象,利用近红外光谱仪结合化学计量学的方法无损检测宣纸本体含水率。将7种不同的湿度盐放入封闭环境箱中,调整环境湿度范围至37%RH~97%RH,并将宣纸置于封闭环境箱内平衡7 d,利用烘干法测得宣纸含水率范围为6.35%~15.55%。近红外光谱采集的范围为900~1700 nm,原始光谱数据采用光谱-距离联合法(SPXY)以4∶1的比例将210条样本划分为168条训练集和42条验证集。原始光谱数据分别利用标准正态变量变换(SNV)、基线校正(BC)、归一化(Normalize)及其组合方法进行数据预处理。利用连续性投影算法(SPA)和竞争性自适应重加权算法(CARS)选择特征波段,并建立全波段的线性偏最小二乘回归模型(PLSR)、特征波段的PLSR模型以及非线性的双层BP神经网络(DL-BPNN)模型。结果表明,以全波段数据建立的模型中,最佳预测模型为SNV-PLSR,其验证集均方根误差(RMSEP)为0.6445,决定系数(R^(2)P)为0.9283。特征波段建立的模型中,线性最佳预测模型为未预处理-CARS-PLSR,其验证集的均方根误差(RMSEP)为0.5707,决定系数(R^(2)P)为0.9438。在非线性DL-BPNN模型中,WT+Normalize-CARS-DL-BPNN预测效果更优,其验证集为0.9424,RMSEP为0.5776。综合比较三种模型的预测效果,未预处理-CARS-PLSR模型具有最佳的预测能力,表明在宣纸的光谱数据信息中,CARS特征提取方法在保留重要特征和去除冗余信息方面具有显著优势。因此,该研究验证了近红外光谱无损检测宣纸含水率的可行性,建立了棉料四尺单宣近红外光谱与含水率之间的关系,为我国纸质文物含水率的测量提供了可靠的检测技术手段。
Water content is a critical factor affecting the preservation of paper cultural relics.To establish a rapid,non-destructive method for detecting the moisture content of paper artifacts,this study focuses on four-foot single-layer Xuan paper made of cotton.We utilized near-infrared(NIR)spectrometry combined with chemometrics for non-destructive moisture detection.Seven different humidifying salts were placed in a sealed environment box to create humidity conditions ranging from 37%to 97%relative humidity(RH).The Xuan paper samples were equilibrated in this controlled environment for seven days.The water content of the samples was measured to range between 6.35%and 15.55%using the drying method.NIR spectra were collected over the range of 900 to 1700 nm.The raw spectral data were divided into 168 training sets and 42 validation sets using the spectral-distance joint method(SPXY)at a ratio of 4∶1 for a total of 210 samples.The data were preprocessed using Standard Normal Variate(SNV),Baseline Correction(BC),and normalization,both individually and in combination.Feature bands were selected using Successive Projections Algorithm(SPA)and Competitive Adaptive Reweighted Sampling(CARS).Subsequently,linear partial least squares regression(PLSR)models were established for the full spectrum and selected feature bands,as well as a nonlinear double-layer backpropagation neural network(DL-BPNN)model.The results indicated that the best prediction model for the full spectrum was SNV-PLSR,with a root mean square error(RMSEP)of 0.6445 and a coefficient of determination(R^(2) p)of 0.9283.For the feature bands,the original spectrum-CARS-PLSR model performed best,with an RMSEP of 0.5707 and an R^(2) p of 0.9438.Among the DL-BPNN models,the WT-Normalize-CARS-DL-BPNN model yielded the best results,with an R^(2) p of 0.9424 and an RMSEP of 0.5776.Comprehensively comparing the prediction effects of the three models,the original spectrum-CARS-PLSR model exhibits the best prediction ability,indicating that the CARS feature extraction method effectively retains important features while eliminating redundant information.This study confirms the feasibility of using NIR spectroscopy for non-destructive moisture content detection in Xuan paper,establishes the relationship between NIR spectra and moisture content,and provides a reliable technical means for measuring the moisture content of paper cultural relics in China.
作者
王建旭
谭银雨
覃丹
汤斌
唐欢
范文奇
杨玟
钟年丙
赵明富
WANG Jian-xu;TAN Yin-yu;QIN Dan;TANG Bin;TANG Huan;FAN Wen-qi;YANG Wen;ZHONG Nian-bing;ZHAO Ming-fu(Chongqing University of Technology,Chongqing Key Laboratory of Fiber Optic Sensing and Photoelectric Detection,Chongqing 400054,China;Key Scientific Research Base of Pest and Mold Control of Heritage Collection(Chongqing China Three Gorges Museum),State Administration of Cultural Heritage,Chongqing 400060,China)
出处
《光谱学与光谱分析》
北大核心
2025年第6期1629-1638,共10页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划项目(2020YFC1522500)
重庆市教委科技项目(KJQN202401105)
重庆中国三峡博物馆馆内自立重点课题(3GM2024-KTZ02)
重庆英才计划项目(cstc2021ycjh-bgzxm0287)资助。
关键词
近红外光谱
无损检测
含水率
纸质文物
Near-infrared spectroscopy
Non-destructive testing
Moisture content
Cultural relics of paper
