Each organic compound with protons gives unique NMR spectrum which is practically identical with any spectrometer operating at certain field. A distinctive feature of the 1D 1H NMR spectra is that even the most complex spectra can be described by a few NMR spectral parameters (chemical shifts and coupling constants) within experimental accuracy, employing the quantum mechanical (QM) theory. The highly diagnostic parameters can be used in structural identification and analysis.
The NMR spectral parameters offer also a very efficient way to store artefact free spectra in Adaptive Spectral Libraries (ASL) , instead of variable quality experimental spectra. Once spectrum has been measured and modelled in one magnetic field strength using QMSA, the spectrum can be simulated in every detail in any other field and mixtures – to be used in quantification of the mixtures with ChemAdder software.
One way to open the principles behind QMSA is to think that the spin-system of coupled nuclear spins in a molecule floats in the bath of electrons, interacting only weakly with the environment. The energetics of the system of the precessing (a kind of vibration) spins obeys the laws of quantum mechanics perfectly so that the transition energies can be calculated theoretically within experimental accuracy from the chemical shifts and coupling constants and, thus, even a most complex spectrum can compressed into a few parameters, the number of which is much smaller than that of the structural features of the spectrum.
Modern spectrometers produce spectra with zero baseline (all spectral intensities have some chemical correspondence) and with minimal solvent suppression bias and other artefacts. Because NMR signal area is closely proportional to the number of protons in the sample, the spectral intensities can be transformed to mg/ml without calibration.
The fundamental problem of qQMSA is that chemical shifts depend on sample. The variation of the chemicals shifts are typically < 0.01 ppm. The solution is to use patternsearch and priorknowledge algorithms, which are based on the fact that the variations are not independent but they correlate. The more spectra analysed, the higher the correlations are. This means that simultaneous analysis of N spectra yields more and better information than N independent analyses for the same spectra.
The ChemAdder program can be used to extract Region Of Interests (ROI) from 2D-spectra. ROI's are rectangular shaped regions on top of the 2D-spectra. The volumes of the ROI's give quantitative information and the F1/F2 projections can be used as a starting point for QMSA. One example for this kind of analysis is a metabolomic flux analysis.
If you are interested about the ROI-feature, watch our video about the metabolic flux analysis with ChemAdder: