In this paper, glass fiber fabric reinforced polyphenylene sulfide composites were prepared by hot pressing. The effects of glass fibre modification and hot pressing temperature on the properties of the composites were investigated using a scanning electron microscope, infrared spectrometer, universal testing machine, and DIGEYE digital imaging colour measurement system. The results show that after the treatment with a silane coupling agent, the silane coupling agent was more uniformly distributed on the surface of the glass fibres, and the bonding effect between the glass fibre fabric and polyphenylene sulphide was significantly improved. The strength of the composites increased and then decreased with the increase of hot pressing temperature, and the surface colour of the composites became darker and darker. When the hot-pressing temperature is 310 °C, the mechanical properties of glass fabric-reinforced polyphenylene sulfide composites are at their best, the tensile strength reaches 51.9 MPa, and the bending strength reaches 78 MPa.

Currently, the methods to identify leather materials have limitations, and identifying natural leather types is also relatively complex. In this research, the microstructures of four types of mammalian leathers (cattle leather, pig leather, sheep leather, and deer leather), three kinds of reptilian leathers (crocodile leather, lizard leather, and snake leather) and regenerated leather were characterized by scanning electron microscopy. The spectral curves (from 900 to 1700 nm) of these leather samples were extracted using a hyperspectral imaging system, and their spectral characteristics were analyzed. A method of leather identification by the hyperspectral imaging system combined with chemometrics was established. The results showed that the spectral curves of natural and regenerated leather differed in the number, position, and depth of the characteristic peaks, enabling the classification of regenerated leather by comparative analysis with the naked eye. The first-order derivative processing–principal component analysis–discriminant analysis model achieved a 98% correct classification rate, confirming the hyperspectral imaging system’s feasibility in the leather material identification field. We believe that his research is beneficial for the leather industry to understand the classifications scientifically, in order to protect consumer rights and further develop the leather testing industry.