Literature DB >> 22054090

An improved algorithm to remove cosmic spikes in Raman spectra for online monitoring.

Sheng Li1, Liankui Dai.   

Abstract

Raman spectral analysis integrated with multivariate calibration is a fast and effective solution to monitor chemical product properties. However, Raman instruments utilizing charge-coupled device (CCD) detectors suffer from occasional spikes caused by cosmic rays. Cosmic spikes can disturb or even destroy the meaningful chemical information expressed by normal Raman spectra. In online monitoring, some cosmic spikes have intensity and bandwidth similar to normal Raman peaks of chemical components when a low resolution and cost-effective Raman instrument is used. Moreover, the online Raman spectra always contain variations of strong Raman peaks and fluorescence. Current spike-removal methods seem to have difficulty detecting and recovering cosmic spikes in these online Raman spectra. Therefore, an improved algorithm is proposed. In this algorithm, a new scheme composed of intensity identification and local moving window correlation analysis is introduced for cosmic spike detection; intensity identification based on derivative spectra and local linear fitting approximation are used for the recovery of cosmic spikes. The algorithm is proved to be simple and effective and has been applied in an online Raman instrument installed at a continuous catalytic reforming unit in a refinery.

Entities:  

Year:  2011        PMID: 22054090     DOI: 10.1366/10-06169

Source DB:  PubMed          Journal:  Appl Spectrosc        ISSN: 0003-7028            Impact factor:   2.388


  3 in total

Review 1.  Potential of Raman spectroscopy for the analysis of plasma/serum in the liquid state: recent advances.

Authors:  Drishya Rajan Parachalil; Jennifer McIntyre; Hugh J Byrne
Journal:  Anal Bioanal Chem       Date:  2020-01-03       Impact factor: 4.142

2.  Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data.

Authors:  Xun Zhang; Sheng Chen; Zhe Ling; Xia Zhou; Da-Yong Ding; Yoon Soo Kim; Feng Xu
Journal:  Sci Rep       Date:  2017-01-05       Impact factor: 4.379

3.  A Statistical Approach of Background Removal and Spectrum Identification for SERS Data.

Authors:  Chuanqi Wang; Lifu Xiao; Chen Dai; Anh H Nguyen; Laurie E Littlepage; Zachary D Schultz; Jun Li
Journal:  Sci Rep       Date:  2020-01-29       Impact factor: 4.379

  3 in total

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