Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint
Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint
复制标题
透射红外显微镜和机器学习应用于原厂汽车油漆法医检验
作者：
F. Kwofie;Nuwan Perera;K. S. Dahal;G. Affadu;K. Nishikida;B. Lavine
利用机器学习方法对原始汽车涂料各层红外光谱的交替最小二乘（ALS）重建进行分析，以提高法医汽车涂料检测的准确性和速度。从2000年到2006年在北美销售的26种原始设备制造商（OEM）涂料作为测试平台，验证了在之前的研究中开发的ALS程序，该程序用于从OEM涂料样品的横截面红外线图中对每层进行光谱重建。对内部库的红外光谱（用高压透射金刚石细胞收集）和ALS重建的相同油漆样品的红外光谱（在环境压力下使用配备BaF2细胞的红外透射显微镜获得）的检查显示，在包括队列的许多样品中，有较大的峰移（约10 cm−1）和一些振动模式。这些峰移归因于透射红外显微镜和用于收集内部红外光谱库的红外光谱仪的红外光束的剩余偏振的差异。为了解决某些振动模式所遇到的频移问题，使用实验室先前开发的校正算法对内部光谱库和红外显微镜中的红外光谱进行转换，以模拟iS-50 FT-IR光谱仪收集的ATR光谱。将该校正算法应用于ALS重建光谱和内部红外库光谱，成功地减轻了先前在某些振动模式下遇到的大峰移。使用机器学习方法来识别OEM油漆样本来源的汽车制造商和装配厂，26个横截面汽车油漆样本中的每一个都被正确分类为车辆的“品牌”和型号，并且还与内部红外光谱库中的正确油漆样本相匹配。图形抽象
Alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint were analyzed using machine learning methods to improve both the accuracy and speed of a forensic automotive paint examination. Twenty-six original equipment manufacturer (OEM) paints from vehicles sold in North America between 2000 and 2006 served as a test bed to validate the ALS procedure developed in a previous study for the spectral reconstruction of each layer from IR line maps of cross-sectioned OEM paint samples. An examination of the IR spectra from an in-house library (collected with a high-pressure transmission diamond cell) and the ALS reconstructed IR spectra of the same paint samples (obtained at ambient pressure using an IR transmission microscope equipped with a BaF2 cell) showed large peak shifts (approximately 10 cm−1) with some vibrational modes in many samples comprising the cohort. These peak shifts are attributed to differences in the residual polarization of the IR beam of the transmission IR microscope and the IR spectrometer used to collect the in-house IR spectral library. To solve the problem of frequency shifts encountered with some vibrational modes, IR spectra from the in-house spectral library and the IR microscope were transformed using a correction algorithm previously developed by our laboratory to simulate ATR spectra collected on an iS-50 FT-IR spectrometer. Applying this correction algorithm to both the ALS reconstructed spectra and in-house IR library spectra, the large peak shifts previously encountered with some vibrational modes were successfully mitigated. Using machine learning methods to identify the manufacturer and the assembly plant of the vehicle from which the OEM paint sample originated, each of the twenty-six cross-sectioned automotive paint samples was correctly classified as to the “make” and model of the vehicle and was also matched to the correct paint sample in the in-house IR spectral library. Graphical Abstract