光谱学与光谱分析 |
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Rapid Discriminantion of Common Leather Varieties by Near Infrared Spectroscopy |
ZHOU Ying1, XU Xiao-chun1, CHEN Qi-qun1, FU Xia-ping2* |
1. Zhejiang Academy of Science and Technology for Inspection and Quarantine, Hangzhou 311215, China 2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
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Abstract 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.
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Received: 2015-06-15
Accepted: 2015-10-30
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Corresponding Authors:
FU Xia-ping
E-mail: fuxp@zju.edu.cn
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[1] TIAN Mei(田 美). China Leather(中国皮革), 2008, 37(1): 42. [2] PAN Hong-qin, TAN Jun-hong, WU Wen-yi(潘红琴, 谭钧鸿, 吴文宜). Chinese Fiber Inspection(中国纤检), 2014, 2: 88. [3] CHEN Zong-liang, SUN Shi-yu, HUANG Xiao-gang(陈宗良, 孙世彧, 黄晓刚). Leather Science and Engineering(皮革科学与工程), 2010, 20(3): 39. [4] Wang Aichen, Fu Xiaping, Xie Lijuan. Food Analytical Methods, 2015, 8: 1403. [5] Jha S N, Ruchi G. Journal of Food Science and Technology, 2010, 47(2): 207. [6] JIE Deng-fei, XIE Li-juan, RAO Xiu-qin, et al(介邓飞, 谢丽娟, 饶秀勤, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(12): 264. [7] Zamora-Rojas E, Pérez-Marin D, De Pedro-Sanz E, et al. Chemonetrics and Intelligent Laboratory Systems, 2012, 114: 30. [8] Teraoka R, Abe H, Sugama T, et al. Journal of Natural Medicines, 2012, 66(2): 329. [9] Dantas H V, Barbosa M F, Nascimento E C L,et al. Talanta, 2013, 106: 158. [10] Srivsatava A, Chowdury M K, Sharms S, et al. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013, 2(1): 615. [11] TIAN Kuang-da, QIU Kai-xian, LI Zu-hong, et al(田旷达,邱凯贤,李祖红,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(12): 3262. [12] NI Zhen, HU Chang-qin, FENG Fang(尼 珍,胡昌勤,冯 芳). Chinese Journal of Pharmaceutical Analysis(药物分析杂志), 2008, 28(5): 824. |
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