In the printing quality inspection, measuring the primary color ink content of printed products is an important link. In order to obtain accurate measurement results in the ink content measurement system, this study preprocesses the interference information such as stray light and noise in the collected ink content spectrum information of cyan, magenta, and yellow samples firstly. Considering the limited denoising ability of common filtering methods in the frequency domain, a composite filtering method combining median filtering and wavelet transform is introduced to preprocess the collected spectral signal. The system not only suppressed the random interference, but restored original signal well. Then, considering that there are many redundant data in full-wavelength spectral reflectance data, the Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm were used to extract characteristic wavelengths from the preprocessed information. After extracting the characteristic wavelengths, the primary color ink content prediction models were built based on the Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) algorithms. The test results show that SVR models based on SPA and CRAS feature extraction algorithms have better prediction effect than PLSR models. Among them, CARS-SVR algorithm models have the best prediction effect, and the modeling wavelengths of three primary color samples decreases by 82.75%, 69.25% and 90.50%, compared with the whole wavelengths. The Cross-Validation Root Mean Square Error (RMSECV) and coefficient of determination (R²) of CARS-SVR content prediction models are 0.0256, 0.0182, 0.0249 and 0.9931, 0.9966, 0.9936, respectively. The system showed better measurement accuracy.

在印刷质量检验中，测量印刷品的原色油墨含量是一个重要环节。为了在油墨含量测量系统中获得准确的测量结果，本研究首先对采集到的青色、品红色和黄色样品的油墨含量光谱信息中的杂散光和噪声等干扰信息进行预处理。考虑到常用滤波方法在频域去噪能力有限，引入中值滤波和小波变换相结合的复合滤波方法对采集到的频谱信号进行预处理。该系统不仅抑制了随机干扰，而且很好地恢复了原始信号。那么，考虑到全波长光谱反射率数据中存在很多冗余数据，采用逐次投影算法（SPA）和竞争自适应重加权采样（CARS）算法从预处理信息中提取特征波长。提取特征波长后，基于偏最小二乘回归（PLSR）和支持向量回归（SVR）算法建立原色油墨含量预测模型。测试结果表明，基于SPA和CRAS特征提取算法的SVR模型比PLSR模型具有更好的预测效果。其中，CARS-SVR算法模型预测效果最好，三基色样本的建模波长与全波长相比分别下降了82.75%、69.25%和90.50%。交叉验证均方根误差 (RMSECV) 和决定系数 (R² ) CARS-SVR 内容预测模型分别为 0.0256、0.0182、0.0249 和 0.9931、0.9966、0.9936。该系统显示出更好的测量精度。