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 (toning yellow concentration/toning red 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 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.
© 2023 The Author(s)
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