基于可见/近红外光谱分析技术的水性油墨黏度预测模型

The Model for Predicting the Viscosity of Water-Based Ink by Vis/NIR Spectroscopy

针对水性油墨黏度测量方法存在操作复杂、主观性强等问题,利用可见/近红外光谱分析技术结合化学计量学方法,建立水性油墨黏度预测模型,实现水性油墨黏度的快速无损检测。首先,利用微型光纤光谱仪采集水性油墨样本的反射光谱;再通过比较不同预处理方法对原始光谱数据的预处理效果,分别基于原始全光谱及预处理后的光谱数据构建水性油墨黏度的偏最小二乘回归(PLSR)和主成分回归(PCR)预测模型;最后,将预处理后的光谱数据采用连续投影算法(SPA)和竞争性自适应重加权算法(CARS)提取特征波长,并基于特征波长的光谱数据建立水性油墨黏度的PLS预测回归模型。结果表明,采用SPA算法从全光谱中只提取了4个特征波长,不仅显著简化了模型,提升了模型的运算效率,建立的SNV-SPA-PLS模型还具有最佳的预测性能(R_(p)^(2)=0.9992,RMSEP=0.0732)。该研究结果表明应用光谱分析技术实现对水性油墨黏度检测是有效可行的,为进一步通过光谱分析技术进行水性油墨在线黏度检测提供了新方法,为提高印刷品质量稳定性提供了技术基础。

Aiming at the problems of complex operation and strong subjectivity in the viscosity measurement method of water-based ink,using Vis/NIR spectroscopy combined with chemometric methods,the prediction model of water-based ink viscosity was established to realize the rapid non-destructive detection of the viscosity of water-based inks.Firstly,the reflectance spectral data of water-based ink samples were obtained by the micro-spectrometer.The PLSR and PCR models were constructed based on the original full spectra and the preprocessing spectral data respectively,by analyzing and comparing the preprocessing effects of different preprocessing methods.Finally,the feature wavelengths were extracted by SPA and CARS,and the PLSR model was established based on characteristic spectra to predict the viscosity of water-based ink.The results show that 4 characteristic wavelengths were extracted by the SPA algorithm from full spectra.The SNV-SPA-PLS model was simplified significantly,which improved the prediction efficiency and had the best prediction performance(R_(p)^(2)=0.9992,RMSEP=0.0732).The results show that the application of spectral analysis techniques for the viscosity of water-based ink detection is effective and feasible,which provides a new method for further viscosity testing of water-based inks by spectral analysis techniques and a technical basis for improving the quality and stability of printed materials.