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Citation: Sang Guotong, Liao Xiaoxi, He Xinlong, Wang Jifen. Research on the Tire Rubber Based on K-Nearest Neighbor Algorithm and Infrared Spectroscopy[J]. Chemistry, ;2019, 82(1): 87-91. shu

Research on the Tire Rubber Based on K-Nearest Neighbor Algorithm and Infrared Spectroscopy

1.
Institute of Forensic Science and Technology, People's Public Security University of China, Beijing 100038
2.
School of International Police Studies, People's Public Security University of China, Beijing 100038

Corresponding author: Wang Jifen, wangjifen58@126.com
Received Date: 7 June 2018
Accepted Date: 12 September 2018

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In forensic science, it is particularly important for the rapid detection of traffic accidents and some lawsuits to identify the tire rubber. A method, non-destructive identification of tire rubber based on infrared spectroscopy and radial basis function neural network, is proposed based on the problem of traditional sampling and analysis techniques would destroy the physical evidence and the comprehensive consideration of multi-variable and multi-dimensional differences in samples. Samples of different brands were collected, and their spectra were automatically baseline corrected and normalized, and were smoothed using the Savitsky-Golay algorithm. Dimension reduction method is used to achieve efficient screening of 840 original features to 5 identification features. Using cross-validation of training samples for test samples and selecting the K value of 1, "Feature 3" as the main independent variable, and "Feature 4", "Feature 5", "Feature 2", and "Feature 1" as covariates as classification parameters. A classification model was established through calculating the distance between samples according to the weighted feature of importance. The total classification accuracy rate is 83.56%, and the differentiation effect is good. Finally, 73 samples were successfully divided into 10 categories. The results showed that combing the IR spectral detection and KNN can identify the tire rubber spectra. The method has strong universality and high efficiency, and can provide certain reference significance.
- IR spectra,
- Tire rubber,
- KNN,
- Identification
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1.
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