Novel algorithm for analysis of cellular fluorescence spectrum based on optimization iteration

DU Yue; MENG Xiaochen; ZHU Lianqing

doi:10.5768/JAO201940.0303001

Volume 40 Issue 3

May 2019

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DU Yue, MENG Xiaochen, ZHU Lianqing. Novel algorithm for analysis of cellular fluorescence spectrum based on optimization iteration[J]. Journal of Applied Optics, 2019, 40(3): 461-467. doi: 10.5768/JAO201940.0303001

Citation:

DU Yue, MENG Xiaochen, ZHU Lianqing. Novel algorithm for analysis of cellular fluorescence spectrum based on optimization iteration[J]. Journal of Applied Optics, 2019, 40(3): 461-467. doi: 10.5768/JAO201940.0303001

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Novel algorithm for analysis of cellular fluorescence spectrum based on optimization iteration

doi: 10.5768/JAO201940.0303001

Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, China

Received Date: 2018-08-13
Rev Recd Date: 2019-01-10
Publish Date: 2019-05-01

Abstract

Abstract

When using the spectral measurement method for multi-color fluorescence analysis of cells, the emission spectrum can produce partial spectral overlap, which poses certain difficulties for qualitative and quantitative analysis. Aiming at the problem, a novel algorithm was developed to effectively decompose the overlapped spectral peaks based on optimization iteration. First, the separation model of overlapped peak was built up to obtain the peak vertex of overlapping peaks. And then, according to the size of each constructed peak area, the structure of the constructed peak was re-determined, and finally the vertex and area information of the simulated peak was obtained.Moreover, the algorithm was used to analyze the overlapping peaks formed by the superposition of Gaussian functions, and compared with the conventional methods. The results show that the analytical error of the optimized iterative algorithm is stable within 0.15%, while it can be stabilized within 0.85% after adding random noise, which are all better that the other two algorithms. In addition, the iterative efficiency under the algorithm was calculated, and the results show that the algorithm is 32.2% higher than the conventional method.
- biomedicine,
- spectral overlapping peaks,
- optimization iteration,
- Gaussian model

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References(15)

References

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