多模NEMS质谱信号的贝叶斯反演

R. Perenon, E. Sage, A. Mohammad-Djafari, L. Duraffourg, S. Hentz, A. Brenac, R. Morel, P. Grangeat
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引用次数: 1

摘要

纳米机电系统是一类具有高灵敏度的新型传感器,为质谱分析领域开辟了新的前景。这种采集是在计数模式下进行的,相关信息处理的主要任务是检测分子,量化它们各自的质量,并将这些信息结合起来,以恢复被分析溶液的质谱。我们提出了一种基于测量系统分层描述的联合检测-量化方法。计算使用可逆跳跃蒙特卡罗马尔可夫链算法完成。本文所描述的方法解决了两个问题:多输出信号的联合脉冲反卷积(多模式采集)和观测信号与分子质量(包括分子在传感器上的定位)之间的非线性关系。我们在模拟和实验数据上验证了我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian inversion of multi-mode NEMS mass spectrometry signal
Nano ElectroMechanical Systems are a new class of sensors that offers high sensitivity and opens new perspectives in the mass spectrometry field. This acquisition is performed in counting-mode, and the main tasks of associated information processing are to detect the molecules, to quantify their respective mass and to combine this information in order to recover the mass spectrum of the analysed solution. We propose a joint detection-quantification method based on a hierarchical description of the measurement system. Computation is done using a Reversible Jumps Monte-Carlo Markov-Chain algorithm. The approach we are describing in this communication solves the two problems of the joint impulse deconvolution on multiple output signals (multi-mode acquisition) and the non-linear relation between the observed signals and the mass of molecules, including the localization of the molecules on the sensor. We validate our method on both simulated and experimental data.
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