China Plastics ›› 2021, Vol. 35 ›› Issue (9): 116-121.DOI: 10.19491/j.issn.1001-9278.2021.09.018

• Standard and Test • Previous Articles     Next Articles

Classification of Disposable Plastic Cup Cover Based on Raman Difference Spectroscopy

GUO Qi1(), JIANG Hong1(), WU Kenan2, YANG Jinjie1, DUAN Bin3, LIU Feng3   

  1. 1.Institute of Criminal Investigation,People’s Public Security University of China,Beijing 100038,China
    2.Institute of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China
    3.Nanjing Jianzhi Instrument Equipment Co,Ltd,Nanjing 210049,China
  • Received:2021-02-09 Online:2021-09-26 Published:2021-09-23

Abstract:

Raman difference spectroscopy is one of the most commonly used testing methods. In this paper, a computer classification and recognition technology were applied for the test of a common disposable plastic cup cover at the scene of the case to obtain the differential Raman spectra of the specimens. According to the obtained spectrum, the specimens can be divided into three categories: PP, PS and pet. The peak positions of functional groups were analyzed and compared. The best clustering number was then determined to be 5 by using the calinski harabash evaluation index, followed by carrying out the K?means clustering. 40 specimens were randomly selected to establish a support vector machine discriminant model. The directional gradient histogram and gray level co?occurrence matrix were extracted and combined into a vector as a row of the feature matrix. The remaining 8 specimens were used as the test set to verify the results. The results indicated that the recognition accuracy reached 100 %. This efficient identification method has the advantages of low inspection cost and fast analysis speed, and it can play a technical reference role for the actual process of handling cases form the public security organs.

Key words: Raman difference spectroscopy, disposable plastic cup cover, calinski harabasz evaluation index, K?means clu?stering, support vector machine algorithm

CLC Number: