[Rapid Discriminantion of Common Leather Varieties by Near Infrared Spectroscopy]

Rapid classification of leather variety means important to product process control, trading process and market surveillance. There is no official detection standard on classification of leather variety for the present. By now the testers use organoleptic method, burning method, chemical dissolution method, microscope method, or combination of them, to give a convincing result. The testers are required to highly sufficiently experienced, and not influenced by subjective factors. It also costs too much time. For the purpose of this research, spectra of five common varieties of leather samples (full-grain leather, split leather, sheep leather, reborn leather and manmade leather) were collected from market. Discriminant analysis combined with pre-processing method, including multiplicative signal correction (MSC), standard normal variate (SNV), first derivative and second derivative were used to classify the spectra above. It shows that the above five varieties of leather overlapped seriously in the same space. But manmade leather can be easily distinguished from the other four leather varieties using rear spectra, with the misclassified percent of 1.2%. The last four leather varieties covered each other partly in the same space, classify of any two of them can reach a lower misclassified percent, about 1.3%～17.9%. Different pre-processing method affected the discriminantion model positively or negatively with no regularity. None of these pre-processing methods was found to give a positive effect in a stable and persistent way. It can be concluded that it is feasible to discriminate the common leather varieties by near infrared Spectroscopy. All of the samples were taken from the finish products in the market (eg, handbag, belt, leather coat), which were processed in different ways (eg. tanning, knurling, dyeing). The different processes of the samples could bring an unforeseeable influence to the model which may be reduced by some method, for example, increasing the number and variety of samples.