Continuing the previous example, if the vertical intensities are expanded a little bit more, we can appreciate (see Figure 2) that peak picking finds not only the main transitions, but also many other peaks like small impurities, 13C satellites, etc.įigure 2: Expanded view of Figure 1. In any event, regardless of the method employed, the main obstacle to achieving a successful automatic analysis of 1H NMR spectra with minimal user intervention lies in the fact that NMR spectra do not consist only of peaks arising from the transitions of the studied spin system, but also many others, such as, solvent and impurity peaks, spinning sidebands, reference peaks (e.g. Of course, this approach is only valid in weakly coupled systems and, thus, has a more limited scope when compared to QM methods, but is useful for a rapid spectrum analysis. the use of the popular first-order analysis rules. They are certainly the most rigorous yet complex methods, although interestingly, the most popular approaches even more than 40 years ago when computer power was much more limited than today.Īnother approach, which incidentally has attracted significant interest in recent years, despite being computationally much less demanding, is based on the same technique typically used by organic chemists, i.e. Historically, this automatic analysis was based on Quantum Mechanical (QM) methods. Whenever practicable, this should be done as automatically as possible by the NMR Software.

Nonetheless, if we restrict the context to that of the simple analysis of small molecules for their characterization, peak picking is usually applied to the calculation of coupling constants and chemical shifts, (in other words determination of the spin system(s) in the spectrum). The question is: What is the purpose of applying peak picking? Well, there is no definitive answer and it depends upon the particular application. For example, some can resolve overlapped peaks better than others ( ), others operate more efficiently with spectra with low SNR, etc.), but the important element I would like to highlight here is the fact that the output of any peak picking algorithm is just a plain list of significant points in a spectrum.

Figure 1: Illustration of traditional peak pickingĮach NMR application might offer different levels of sophistication in the peak picking algorithms.

Peak picking in mestrenova