Resolution Enhancement in Mass Spectrometry by Autoregressive Deconvolution

Hiroshi Kato; Morio Ishihara; Munetaka Nakata

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Resolution Enhancement in Mass Spectrometry by Autoregressive Deconvolution

机译：通过自回归反卷积提高质谱分辨率

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A numerical spectral-deconvolution method with an autoregressive (AR) model is propoosed to overcome a noise problem that limits resolution-enhancement ability of a traditional Fourier-transform (FT) deconvolution. The optimal AR-model is determined by a new criterion that uses similarity between measured by using cted spectra. Additionally, unreliable peak-heights appearing in an AR power-spectrum are corrected by using its convolution with a Gaussian function with a small width.

机译：提出了一种具有自回归（AR）模型的数字频谱解卷积方法，以克服噪声问题，该问题限制了传统傅立叶变换（FT）解卷积的分辨率增强能力。最佳AR模型由新标准确定，该新标准使用通过cted光谱测量的相似性。另外，通过使用AR卷积与小宽度的高斯函数的卷积来校正出现在AR功率谱中的不可靠的峰高。

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《Journal of the Mass Spectrometry Society of Japan》 |2001年第5期|p.175-182|共8页
作者
Hiroshi Kato; Morio Ishihara; Munetaka Nakata;
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正文语种 jpn
中图分类分析化学;一般工业技术;
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Resolution Enhancement in Mass Spectrometry by Autoregressive Deconvolution

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