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  Forget What the Books Say

NMR can be quite simple or quite complicated. The subject in itself is, for the most part, simple. NMR processing is one of the simplest NMR topics to understand. Why, then, is it so confusing for many people? The main reason is that there have been a number of lies, first written in some books and subsequently reported by all the others, that nobody ever cared to clarify.
For example, you can find the formula:

number of points = 2 x Spectral width x Acquisition time.

Though often reported and used, it is wrong and very misleading. If you count manually, one by one, the points contained in any spectrum, you will obtain the correct formula:

number of points = Spectral width x Acquisition time.

Other examples exist, but they will not be reported here. The lesson is: if you trust too much in the books, or into your past experience, soon or later you will become so confused that nothing will make sense to you anymore. Forget the books, forget the software you have used so far, trust into your senses and, especially, into common sense.

Remember that many NMR books were written in the “Continuous Wave” era. Even many apparently recent books were written before the appearance of today's digital instruments. Manufacturers of spectrometers have good reasons to conceal what actually happens inside their boxes. All the books you can find about FT-NMR are quite distant from reality. This is not a bad thing. It is better a false description that you can fully grasp, than an accurate description you barely understand and does not give you the right logic frame. Just don't trust too much. Remember that you live in the digital era: nothing you hear or see is the real thing, perhaps it's the mp3 version of it!

Spending time with NMR means, mainly, doing processing. If you are curious enough, you can combine two things into a single operation: processing your own spectra and, at the same time, learning how FT-NMR works. Whenever you have time, try reprocessing your spectra aiming for better quality, more symmetric peak-shapes, more signal-to-noise, more resolution, flatter baselines, integrals with integer values....

See also

Free Induction Decay

Fourier Transform

Weighting in 1D Spectroscopy


Exporting Spectra