2019/08/29 by Hongzhen Tian, Tian, Hongzhen, Andi Wang +13
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Advanced biosensing and bioanalysis techniques #Chemical engineering #Computer science #Engineering #Environmental science #FOS: Electrical engineering #Fourier transform infrared spectroscopy #Library science #Machine Learning in Materials Science #Signal Processing (eess.SP) #Spectroscopy Techniques in Biomedical and Chemical Research #Subject (documents) #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.11001
published in arXiv (Cornell University) (Cornell University) · \{copyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
arxiv created 2019/08/29 · openalex publication_date 2019/08/29 · arxiv updated 2019/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Fourier-transform infrared spectroscopy (FTIR) is a versatile technique for characterizing the chemical composition of the various uncertainties, including baseline shift and multiplicative error. This study aims at analyzing the effect of certain treatment on the FTIR responses subject to these uncertainties. A two-step method is proposed to quantify the treatment effect on the FTIR signals. First, an optimization problem is solved to calculate the template signal by aligning the pre-treatment FTIR signals. Second, the effect of treatment is decomposed as the pattern of modification g that describes the overall treatment effect on the spectra and a vector of effect \boldsymbolδ that describes the degree of modification. \mathbf g and \boldsymbolδ are solved by another optimization problem. They have explicit engineering interpretations and provide useful information on how the treatment effect change the surface chemical components. The effectiveness of the proposed method is first validated in a simulation. In a real case study, it's used to investigate how the plasma exposure applied at various heights affects the FTIR signal which indicates the change of the chemical composition on the composite material. The vector of effects indicates the range of effective plasma height, and the pattern of modification matches existing engineering knowledge well.