Forensic Characterization of Tires by Attenuated Total Reflectance–Fourier Transform Infrared (ATR–FTIR) Spectroscopy and Machine Learning Algorithms

Abstract

Rapid and nondestructive recognition method of tire rubber is reported by attenuated total reflectance–Fourier transform infrared (ATR–FTIR) spectroscopy with machine learning algorithms. The infrared spectra were obtained from 187 truck, sedan, motorbike, and passenger car tires. Weighted k nearest neighbor analysis (WKNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used and compared for modeling. The influences of the k value, kernel function, number of estimators, maximum depth, minimum sample leaf, and other parameters affecting recognition performance were optimized. The results show that the recognition performance of four models was SVM > LR > WKNN > RF. The SVM model (polynomial kernel function, C = 20, gamma = 1) was considered to be optimal. All samples had a recognition accuracy of 100% based on the tire type with a training set value of 96.9% and test set value of 92.3%. Hence, the developed procedure is suitable for the characterization of tires.

Keywords:

• Attenuated total reflectance–Fourier transform infrared (ATR–FTIR) spectroscopy
• forensic tire analysis
• logistic regression (LR)
• random forest (RF)
• support vector machine (SVM)
• weighted k-nearest neighbor (KNN) analysis