Research on the measurement method of printing ink content based on spectrum

Abstract

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.