吴梦超,屈永波,瞿小阳,黄新国.基于光谱融合的水性油墨印刷品颜色变化预测研究[J].包装工程,2024,45(17):200-208.
WU Mengchao,QU Yongbo,QU Xiaoyang,HUANG Xinguo.Color Change Prediction of Water-based Ink Prints Based on Spectral Fusion[J].Packaging Engineering,2024,45(17):200-208. |
| 基于光谱融合的水性油墨印刷品颜色变化预测研究 |
| Color Change Prediction of Water-based Ink Prints Based on Spectral Fusion |
| 投稿时间：2024-03-20 |
| DOI：10.19554/j.cnki.1001-3563.2024.17.024 |
| 中文关键词: 近红外光谱 油墨 光谱融合 偏最小二乘法(PLS) 颜色预测 |
| 英文关键词: near infrared spectroscopy ink spectral fusion partial least squares (PLS) color prediction |
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| 中文摘要: |
| 目的 利用近红外光谱及光谱融合策略，结合化学计量学方法，建立水性油墨的颜色预测模型，实现水性油墨印刷品颜色准确预测。方法 采集不同酒精含量和不同调色墨含量的油墨的近红外光谱反射率和吸光度数据，并测得对应的印刷品的Lab值，然后建立单一光谱不同预处理过后的偏最小二乘(Partial Least Squares，PLS)模型，以及基于数据层融合和特征层融合的PLS模型，最终通过比较预测集决定系数和预测集均方根误差(Root Mean Square Error of Prediction，RMSEP)以及色差来评估模型的预测效果。结果 单光谱建模，基于反射率建立的模型准确率高于基于吸光度建立的模型;数据层融合缺乏稳定性，对L和b值的预测有所提升，对a值的预测几乎不变;特征层融合建模效果明显好于单一光谱和数据层融合，对Lab的预测决定系数分别达到了0.996 1、0.993 9、0.997 4;RMSEP值分别为0.142 1、0.212 6、0.207 2;预测值与真实值的最大色差为0.678 3。结论 通过光谱特征融合技术能提高油墨颜色预测精度，准确预测出油墨颜色变化。 |
| 英文摘要: |
| The work aims to establish a color prediction model for water-based inks by utilizing near-infrared spectroscopy and spectral fusion strategies combined with chemometrics methods, to achieve accurate color prediction of ink prints. The near-infrared spectral reflectance and absorbance data of inks with different alcohol contents and color ink contents were collected and the corresponding Lab values of prints were measured. Then, a partial least squares (PLS) model for a single spectrum after different preprocessing and a PLS model based on data layer fusion and feature layer fusion were established. Finally, the determination coefficient of the prediction set with the root mean square error of the prediction set (RMSEP) and the chromatic aberration were compared to evaluate the predictive performance of the model. For single spectral modeling, the accuracy of the model based on reflectance was higher than that of the model based on absorption. The lack of stability in data layer fusion improved the prediction of L and b values, while the prediction of a value remains almost unchanged. The modeling effect of feature layer fusion was significantly better than that of single spectrum and data layer fusion, with prediction determination coefficients for Lab reaching 0.996 1, 0.993 9, and 0.997 4, respectively. The RMSEPs were 0.142 1, 0.212 6, and 0.207 2, respectively. The maximum color difference between the predicted value and the true value was 0.678 3. Spectral feature fusion technology can improve the accuracy of ink color prediction and accurately predict ink color changes. |
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