| 纸张含水量的傅里叶变换中红外光声光谱分析 |
| 投稿时间：2017-07-24 修订日期：2017-08-18 点此下载全文 |
| 引用本文：马赵扬,杜昌文.纸张含水量的傅里叶变换中红外光声光谱分析[J].红外,2017,38(11):44~48 |
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| 基金项目:中国国家图书馆青年项目(NLC-KY-2015-36) |
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| 中文摘要:随着科学技术的不断发展，社会正在向智能化
演变。实现纸质文献的实时监测以达到保存和保护文献的目的
是未来发展的必然趋势。为了给库房文献的实时监测提供依据，结合
化学计量学方法，利用傅里叶变换中红外光
声光谱技术快速测定了纸张含水量。基于中红外光声光谱，分别采用
主成分回归(Principal Components Regression, PCR)方法和
偏最小二乘回归(Partial Least Squares Regression, PLSR)方法建立了纸张含水量的
定量模型，并通过交叉验证选择了最佳主成分数。结果表明，PLSR方法
的建模结果优于PCR方法(决定系数R2：0.3681>0.3532)。
通过增加主成分数可以使模型预测变好，但也存在过拟合风险。未来拟采
集更多的纸张样本，以期建立稳定的纸张性质检测模
型，为实现纸质文献的红外光谱实时监测奠定基础。 |
| 中文关键词:纸张含水量 红外光声光谱 主成分回归 偏最小二乘回归 |
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| Determination of Paper Moisture Content by Infrared Photoacoustic Spectroscopy in Fourier Transform |
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| Abstract:With the progress of science and
technology, society is evolving toward intelligence.
To use real-time paper literature monitoring to realize
the preservation of paper literature will be an inevitable
development trend in the future. In order to provide the
basis for the real-time monitoring of library literature, Fourier
transform medium infrared photo-acoustic spectroscopy is used
to determine the water content in paper in combination with
stoichiometry. A quantitative model of paper water content is
established by Principal Component Regression (PCR) and Partial
Least Squares Regression (PLSR) respectively on the basis of
medium-infrared photoacoustic spectroscopy. The optimum primary
component number is selected by cross-validation. The result shows
that the modeling result of partial least squares regression is
better than that of the principal component regression (the determination
coefficient is 0.3681>0.3532). By increasing the number of principal
component, the prediction of the models will be better. However,
there are some risks of fitting. In the future, more paper samples
will be collected so as to establish a stable paper quality detection
model and lay a basis for the real-time infrared spectrum monitoring of paper literature. |
| keywords:paper moisture content infrared photoacoustic spectroscopy principal component regression partial least squares regression |
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