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Fractional volume integration in two-dimensional NMR spectra: CAKE, a Monte Carlo Approach

机译:二维NMR光谱中的分数体积积分:CAKE,蒙特卡洛方法

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Quantitative information from multidimensional NMR experiments can be obtained by peak volume integration. The standard procedure (selection of a region around the chosen peak and addition of all values) is often biased by poor peak definition because of peak overlap. Here we describe a simple method, called CAKE, for volume integration of (partially) overlapping peaks. Assuming the axial symmetry of two-dimensional NMR peaks, as it occurs in NOESY and TOCSY when Lorentz-Gauss transformation of the signals is carried out, CAKE estimates the peak volume by multiplying a volume fraction by a factor R. It represents a proportionality ratio between the total and the fractional volume, which is identified as a slice in an exposed region of the overlapping peaks. The volume fraction is obtained via Monte Carlo Hit-or-Miss technique, which proved to be the most efficient because of the small region and the limited number of points within the selected area. Tests on simulated and experimental peaks, with different degrees of overlap and signal-to-noise ratios, show that CAKE results in improved volume estimates. A main advantage of CAKE is that the volume fraction can be flexibly chosen so as to minimize the effect of overlap, frequently observed in two-dimensional spectra.
机译:来自多维NMR实验的定量信息可以通过峰值积分获得。标准过程(选择峰值周围的区域并添加所有值)通常因峰值重叠而偏差较差的峰值定义。在这里,我们描述了一种简单的方法,称为蛋糕,用于(部分)重叠峰的卷集成。假设二维NMR峰的轴对称性,当执行信号的Lorentz-Gauss转换时,饼干通过将体积分数乘以因子R来估计峰值。它代表比例比在总和和分数之间,在重叠峰的暴露区域中被识别为切片。体积分数通过蒙特卡罗击中或错过技术获得,这被证明是由于小区域和所选区域内有限的点数是最有效的。对模拟和实验峰的测试,具有不同程度的重叠和信噪比,表明蛋糕导致改善的体积估计。蛋糕的主要优点是可以灵活地选择体积分数,以便最小化重叠的效果,经常在二维光谱中观察到。

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