• 제목/요약/키워드: FTICR mass spectrum

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Speed Improvement of an FTICR Mass Spectra Analysis Program by Simple Modifications

  • Jeon, Sang-Hyun;Chang, Hyeong-Soo;Hur, Man-Hoi;Kwon, Kyung-Hoon;Kim, Hyun-Sik;Yoo, Jong-Shin;Kim, Sung-Hwan;Park, Soo-Jin;Oh, Han-Bin
    • Bulletin of the Korean Chemical Society
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    • 제30권9호
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    • pp.2061-2065
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    • 2009
  • Two simple algorithm modifications are made to the THRASH data retrieval program with the aim of improving analysis speed for complex Fourier transform ion cyclotron resonance (FTICR) mass spectra. Instead of calculating the least-squares fit for every charge state in the backup charge state determination algorithm, only some charge states are pre-selected based on the plausibility values obtained from the FT/Patterson analysis. Second, a modification is made to skip figure-of-merit (FOM) calculations in the central m/z region between two neighboring peaks in isotopic cluster distributions, in which signal intensities are negligible. These combined modifications result in a significant improvement in the analysis speed, which reduces analysis time as much as 50% for ubiquitin (8.6 kDa, 76 amino acids) FTICR MS and MS/MS spectra at the reliability (RL) value = 0.90 and five pre-selected charge states with minimal decreases in data analysis quality (Table 3).

Optimized Automatic Noise Level Calculations for Broadband FT-ICR Mass Spectra of Petroleum Give More Reliable and Faster Peak Picking Results

  • Hur, Manhoi;Oh, Han-Bin;Kim, Sung-Hwan
    • Bulletin of the Korean Chemical Society
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    • 제30권11호
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    • pp.2665-2668
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    • 2009
  • A new algorithm for determining noise level is proposed for more reliability in interpreting spectral data for complex Fourier transform ion cyclotron resonance (FTICR) mass spectra of petroleum. In the new algorithm, a moving window with a fixed number of data points was adopted, instead of a fixed m/z width. In the analysis of petroleum, it was found that a moving window of 50,000 or more data points was optimal. This optimized automated peak picking performed well even with frequency-dependant noise in the mass spectrum. Additionally, this fast, automated peak picking algorithm was suitable for the analysis of a large set of samples.