Evaluation of a novel computer dye recipe prediction method based on the PSO-LSSVM models and single reactive dye database

In the process of fabric dyeing production, the color samples are usually obtained from the users first, and then the color-matching personnel conducts proofing according to the color of the samples. Therefore, the efficiency of color matching is low, and many fabric color-matching technologies emerge. In this work, we designed a series of dyeing schemes, and built three single reactive dye databases by dyeing cotton knitted fabrics with Levafix Red, Levafix Blue and Levafix Amber in the pad-dry-pad-steam (PDPS) process, respectively. Furthermore, three prediction models were established based on PSO-LSSVM, which took the color parameter L∗, a∗ and b∗ value of the dyed fabrics as model input and dye concentration as model output. The color parameter L∗, a∗ and b∗ values of the fabrics dyed at the dye concentration predicted from the PSO-LSSVM models are consistent with actual measured values of the tested cotton fabrics. All evaluation indexes show that the Ep of PSO-LSSVM models are above 96%, possessing a high accuracy of dye recipe prediction and strong dye specificity, which can be used in actual dyeing color matching. •Three single reactive dye databases were established.•Three prediction models were established based on PSO-LSSVM.•The color parameter L∗, a∗ and b∗ value of the dyed fabrics were used as model input.•Dye concentration was used as model output.•Models have high prediction accuracy and strong specificity.