title: An auto-adaptive background subtraction method for Raman spectra.
abstract: Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy.
authors.affiliation: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Xie
authors.affiliation: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China.
authors.forename: Lidong
authors.initials: L
authors.lastname: Yang
authors.affiliation: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China.
authors.forename: Xilong
authors.initials: X
authors.lastname: Sun
authors.affiliation: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China.
authors.forename: Dewen
authors.initials: D
authors.lastname: Wu
authors.affiliation: College of Chemistry and Chemical Engineering, Xiamen University, China.
authors.forename: Qizhen
authors.initials: Q
authors.lastname: Chen
authors.affiliation: College of Chemistry and Chemical Engineering, Xiamen University, China.
authors.forename: Yongming
authors.initials: Y
authors.lastname: Zeng
authors.affiliation: State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China. Electronic address: guokunliu@xmu.edu.cn.
authors.forename: Guokun
authors.initials: G
authors.lastname: Liu
keywords: Auto-adaptive; Background subtraction; Raman spectrum