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

• Published in: Analytical letters Vol. 56; no. 10; pp. 1551 - 1565
• Main Author: Du, Haojun
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.07.2023; Taylor & Francis Ltd
• Subjects: Algorithms; Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy; forensic tire analysis; Fourier transforms; Infrared spectra; Infrared spectroscopy; Kernel functions; logistic regression (LR); Machine learning; Motorcycles; Nondestructive testing; Polynomials; random forest (RF); Recognition; Reflectance; support vector machine (SVM); Support vector machines; Tires; weighted k-nearest neighbor (KNN) analysis
• ISSN: 0003-2719; 1532-236X
• DOI: 10.1080/00032719.2022.2138422

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.