# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## Appendix A Technical Aspects
This information can be easily obtained by the \({}^{1}\)H-\({}^{13}\)C HSQC experiment (see Figure 11), giving an unambiguous identification of the two organic compounds [145]. HMBC is an appropriate technique to analyze the correlations using the coupling of protons with heteroatoms, which are separated up to four bonds, providing complementary information to that given by HSQC for the structural characterization of metabolites. Figure 13: NMR spectrum of ultrafiltered human serum at 700 MHz with the identified compounds labeled above each of the corresponding peaks. Figure reprinted from Ref. [3] under the terms of the Creative Common CC-BY license. Thus, metabolic identification can be easily reached by the combination of 2D-NMR techniques with the metabolite databases. However, in some cases, the concentration of metabolites is very low, and their peaks are often overlapped making their identification difficult, even when employing 2D NMR techniques. In these cases, if the sharp chemical shift of the compounds to identify is known, it is recommended to use a standard (reference) compound, which is added in the concentration range 10-100 \(\upmu\)M. For instance, this method was applied to identify the uridine diphosphate (UDP) conjugates, which are present in very low concentrations in cellular extracts with overlapped peaks, but their chemical shift is well known and the signal-to-noise ratio (S/N) has sufficient intensity to be quantified by 1D/2D NMR experiments [145]. Figure A4 shows in details how the spiking of pure compounds into a mixture aids the identification of metabolites within the spectrum and also its quantification by performing peak fitting of the two spectral regions corresponding to UDP-nacetylglucosamine (UDP-Gluc-NAc). Note that the proton signal on the left side (\(\delta\) = 5.50 ppm) of UDP-Gluc-NAc is overlapped to that of galactose-1-phosphate (Gal-1-P), whereas signals from the uridine group (\(\delta\) = 5.95 ppm) superimpose with those from UDP-glucose. In addition, without spiking, it is almost impossible to define the shift of the methyl group belonging to UDP-Gluc-Nac acetyl (right region) since there is a big overlap with other signals, such as the multiplets from glutamine and glutamate. The addition of a standard is also employed to obtain an absolute quantification of the metabolites contained in the sample. Therefore, the estimation of the metabolites concentration can be made by comparing the area of the metabolites NMR peaks with that of the reference sample by the following equation:  
\[\frac{M}{S}=\frac{Im}{Is}\times\frac{Ns}{Nm}\] (A1)in which \(M\) and \(S\) represent the amounts of the considered metabolite and that of the reference, while Im and Is indicate the area under the curve of corresponding peaks, and Nm and Ns represent the number of protons which contribute to these bands, respectively [145]. To quantify a small set of metabolites whose resonances are well-resolved peaks, also the pulse length-based concentration determination (PULCON) quantitative NMR can be used. It considers that the signal intensity is inversely proportional to the duration of the 90\({}^{\circ}\) pulse adopted to excite nuclei [146]. Finally, another possibility for quantitative NMR analysis was reached by using a digital standard electronic reference to access in vivo concentration (ERETIC) technique [147]. It consists of the generation of a signal via a second channel of the probe and the addition of it as a pseudo-FID during the acquisition of the proton experiment, resulting in a common NMR signal [148]. Initially, the ERETIC technique required to be calibrated before running the quantification measurements and some hardware rearrangements. Improvements of ERETIC are ERETIC2 (Bruker Biospin, Topspin 3.0) and quantification by artificial signal (QUANTAS) [149]. Considering the complexity of NMR spectrum of metabolites, often, peak integration is not a sufficient method for the quantitative estimation, and in these cases, the deconvolution approach is preferred.