Fourier transform is the data processing naturally associated to most NMR experiments. Notable exceptions are Pulse Field Gradient and relaxation analysis, the structure of which is only partially suitable for FT. With the revamp of NMR of complex mixtures, fueled by analytical challenges such as metabolomics, alternative and more apt mathematical methods for data processing have been sought, with the aim of decomposing the NMR signal into simpler bits. Blind source separation is a very broad definition regrouping several classes of mathematical methods for complex signal decomposition that use no hypothesis on the form of the data. Developed outside NMR, these algorithms have been increasingly tested on spectra of mixtures. In this review, we shall provide an historical overview of the application of blind source separation methodologies to NMR, including methods specifically designed for the specificity of this spectroscopy.

中文翻译：

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核磁共振波谱盲源分离综述

傅里叶变换是与大多数核磁共振实验自然相关的数据处理。值得注意的例外是脉冲场梯度和弛豫分析，其结构仅部分适用于 FT。随着复杂混合物 NMR 的改进，在代谢组学等分析挑战的推动下，人们寻求替代且更合适的数据处理数学方法，目的是将 NMR 信号分解为更简单的位。盲源分离是一个非常广泛的定义，它重新组合了几类用于复杂信号分解的数学方法，这些方法不使用对数据形式的假设。这些算法在 NMR 之外开发，越来越多地在混合物光谱上进行测试。在这篇综述中，我们将提供盲源分离方法在 NMR 中应用的历史概述，