Development of a quantitative method to evaluate the printability parameters of water-based ink using visible and near infrared spectroscopy

Abstract

Water-based inks are widely used in green packaging and printing. The printability parameters of water-based inks, such as viscosity (alcohol concentration (AC)) and color (toning additive concentration (TYC/TRC)), can only be controlled manually in many printing companies. The printability parameters of water-based inks with different additives were analyzed using spectral preprocessing, variable selection, and model-building methods with visible and near infrared (vis-NIR) spectral data (380∼980 nm). Model performance was compared using the root mean square error of cross-validation (RMSEC) and the coefficient of determination (R2). The results of the experiment indicate that the viscosity of the water-based inks can be quantitatively predicted using the principal component analysis and back propagation neural network model (PCA-BPNN) combined with Savitzky-Golay (SG) smoothing in the spectral subrange, which is superior to the PLS regression model. The R2c and R2p of the PCA-BPNN model were up to 0.998 and 0.993, and the RMSEC and RMSEP values obtained were 0.21 and 0.34. Similarly, the concentration of toning yellow and toning red (TYC/TRC) can be quantitively predicted using the PCA-BPNN model combined with SG smoothing in the 617∼726 nm spectral range, which is better than iPLS regression model. These results indicate that the use of vis-NIR spectroscopy and chemometrics is a promising strategy, reliable for predicting the printability parameters of water-based inks, and provides the technical basis for subsequent implementation of online inspection.