This paper reports a novel route for the coloration of polyester fabric with green synthesized silver nanoparticles (G-AgNPs@PET) using chitosan as a natural eco-friendly reductant. The formation of AgNPs was confirmed by UV-visible spectroscopy. The morphologies and average particles size of G-AgNPs was investigated by transmission electron microscope (TEM) analysis. The uniform deposition of G-AgNPs on the PET fabric surface was confirmed with scanning electron microscopy (SEM) and Fourier transform infrared (FT-IR) spectroscopy. The thermal properties were investigated using a thermogravimetric analyzer (TGA). The coloration and fastness properties of fabric were found to be significantly improved, a result related to the surface plasmon resonance of G-AgNPs. The antibacterial properties of fabric were also found to be excellent as more than 80% bacterial reduction was noticed even after 10 washing cycles. Overall, the proposed coating process using green nanoparticles can contribute to low-cost production of sustainable textiles.

To digital grade the staining color fastness of fabrics after rubbing, an automatic grading method based on spectral reconstruction technology and BP neural network was proposed. Firstly, the modeling samples are prepared by rubbing the fabrics according to the ISO standard of 105-X12. Then, to comply with visual rating standards for color fastness, the modeling samples are professionally graded to obtain the visual rating result. After that, a digital camera is used to capture digital images of the modeling samples inside a closed and uniform lighting box, and the color data values of the modeling samples are obtained through spectral reconstruction technology. Finally, the color fastness prediction model for rubbing was constructed using the modeling samples data and BP neural network. The color fastness level of the testing samples was predicted using the prediction model, and the prediction results were compared with the existing color difference conversion method and gray scale difference method based on the five-fold cross-validation strategy. Experiments show that the prediction model of fabric color fastness can be better constructed using the BP neural network. The overall performance of the method is better than the color difference conversion method and the gray scale difference method. It can be seen that the digital rating method of fabric staining color fastness to rubbing based on spectral reconstruction and BP neural network has high consistency with the visual evaluation, which will help for the automatic color fastness grading.