2022/12/30 by Yifeng Bie, Bie, Yifeng, Shuai You +7
Chemistry · Engineering · #Advanced Chemical Sensor Technologies #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Remote-Sensing Image Classification #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.2212.14623
openalex publication_date 2022/12/30 · openalex created_date 2023/01/06 · openalex updated_date 2026/07/28
Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.