装饰纸作为现代家居产品中必不可少的重要饰面材料之一, 不仅具有良好的装饰效果, 还可以大大地改善材料的表面性能。然而, 装饰纸外观质量的控制(如色差的评价和控制), 仍然是制约装饰纸产业发展的重要因素之一。利用人眼来判别和控制往往因人而异, 偏主观而非客观。因此, 寻找一种客观、有效的方法来代替人的视觉感官来快速判别具有重要意义。本研究利用可见光光谱结合主成分分析法对不同类别装饰纸进行了模式识别分类研究, 探讨了该技术的可行性。结果表明: (1)可见光光谱与表征装饰纸表面视觉特性的参数之间呈现出显著的相关性, 相关系数都达到了0.85以上, 多数均达到0.99, 说明可见光光谱中包含有描述装饰纸表面视觉特征的参数;(2)利用装饰纸表面的可见光光谱结合主成分分析方法所建立的模式识别模型, 对不同装饰纸表面视觉特征类型进行模式识别时, 正确率达到94%～100%, 说明可见光光谱分析技术有潜力成为装饰纸表面视觉特征快速、客观、有效识别和分类的新技术。

As one of the most important decorative materials for the modern household products, decorative papers impregnated with melamine not only have better decorative performance, but also could greatly improve the surface properties of materials. However, the appearance quality (such as color-difference evaluation and control) of decorative papers, as an important index for the surface quality of decorative paper, has been a puzzle for manufacturers and consumers. Nowadays, human eye is used to discriminate whether there exist color difference in the factory, which is not only of low efficiency but also prone to bring subjective error. Thus, it is of great significance to find an effective method in order to realize the fast recognition and classification of the decorative papers. In the present study, the visible spectroscopy coupled with principal component analysis (PCA) was used for the pattern recognition of decorative papers with different visual characteristics to investigate the feasibility of visible spectroscopy to rapidly recognize the types of decorative papers. The results showed that the correlation between visible spectroscopy and visual characteristics (L*, a* and b*) was significant, and the correlation coefficients were up to 0.85 and some was even more than 0.99, which might suggest that the visible spectroscopy reflected some information about visual characteristics on the surface of decorative papers. When using the visible spectroscopy coupled with PCA to recognize the types of decorative papers, the accuracy reached 94%～100%, which might suggest that the visible spectroscopy was a very potential new method for the rapid, objective and accurate recognition of decorative papers with different visual characteristics.