基于特征干扰排除的蛋白质光谱分析仿真

Simulation on Protein Spectrum Analysis Based on the Characteristic disturbance rejection

研究蛋白质光谱准确检测优化问题。蛋白质的光谱特征较为复杂,质谱特征很容易受到外界因素的影响,造成质谱特征的波动,传统的基于谱峰特征的检测方法一旦受到干扰,会造成质谱特征在多个特征谱中都存在,影响光谱分析的精度。为了避免上述缺陷,提出了一种基于梯度模型控制曲线算法的蛋白质光谱分析方法。利用最小灰度差值方法,提取蛋白质光谱中的特征,从而为光谱分析提供准确的数据基础。利用梯度模型控制曲线方法,对蛋白质光谱进行分割处理,从而实现蛋白质光谱分析。实验结果表明,采用改进算法能够有效提高蛋白质光谱分析的准确性,可为蛋白质检测领域带来了极大的经济效益。

In this paper, optimization problem of accurate detection of the protein spectrum was studied. The spectral characteristics of the protein is more complex and mass spectra characteristics is vulnerable to external factors, resulting in fluctuations in mass characteristics. Based on the traditional method of detecting peaks characteristic, in the event of interference, it may cause the problem that mass spectrum characteristics present in a plurality of spectrums. The accuracy of spectral analysis will be impacted. To avoid these shortcomings, we proposed a new protein spectral analysis method based on the gradient model control curve algorithm. Using minimum gray level method, the characteristic of the protein spectrum was extracted, so as to provide an accurate spectral data base. And then u- sing the method of gradient model control curve, protein spectra can be segmented, in order to achieve the protein spectrum. Experimental results show that the improved algorithm can effectively progress the accuracy of the protein spectrum and bring great economic benefits for protein detection field.