# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches  
[PERSON]  
1 Department of Mathematical and Computational Science, Physical Science and Earth Science, University of Messina, Viale F. Stagno D'Alcontres 31, I-98166 Messina, Italy; [EMAIL_ADDRESS] (S.V. ); [EMAIL_ADDRESS] (EN. ); [EMAIL_ADDRESS] (A.M.M. ); [EMAIL_ADDRESS] (E.F.)  
[PERSON]  
1 Department of Mathematical and Computational Science, Physical Science and Earth Science, University of Messina, Viale F. Stagno D'Alcontres 31, I-98166 Messina, Italy; [EMAIL_ADDRESS] (S.V. ); [EMAIL_ADDRESS] (EN. ); [PERSON]. ); [EMAIL_ADDRESS] (E.F.)  
[PERSON]  
1 Department of Mathematical and Computational Science, Physical Science and Earth Science, University of Messina, Viale F. Stagno D'Alcontres 31, I-98166 Messina, Italy; [EMAIL_ADDRESS] (S.V. ); [EMAIL_ADDRESS] (EN. ); [PERSON]. ); [EMAIL_ADDRESS] (E.F.)  
[PERSON]  
1 Department of Mathematical and Computational Science, Physical Science and Earth Science, University of Messina, Viale F. Stagno D'Alcontres 31, I-98166 Messina, Italy; [EMAIL_ADDRESS] (S.V. ); [EMAIL_ADDRESS] (EN. ); [EMAIL_ADDRESS] (A.M.M. ); [EMAIL_ADDRESS] (E.F.)  
[PERSON]  
2 Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale F. Stagno D'Alcontres 31, I-98166 Messina, Italy; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] 2022212  
metabolic components of a biological matrix whose concentration is comparable or bigger than 1 \(\upmu\)M (see Appendix A). Such sensitivity, relatively low if compared with other MS techniques, allows to assign up to 20 metabolites in vivo, and up to 100 metabolites in vitro [9; 10; 11]. Numerous strategies are being designed to overcome actual limitations, including a lower selectivity compared to the MS technique coupled with gas or liquid chromatography (GC-MS and LC-MS, respectively) and a low resolution for complex biological matrices. These include the development of new pulse sequences mainly involving field gradients for observing multidimensional hetero- or homo-nuclear correlations [12]. Within metabolomics investigations, NMR analyses are usually coupled with statistical approaches: sample randomization allows to reduce the correlation between confounding variables, sample investigation order and experimental procedures. In the last ten years, nested stratified proportional randomization and matched case-control design were adopted in the case of imbalanced results [13; 14; 15]. In any case, data pre-processing is a relevant step before performing data analysis by means of a conventional approach or a statistical one. The goal of pre-processing is to homogenize the acquired data, avoiding the presence of instrumental bias mainly involving peaks' features for a better quantification of metabolites. For example, the pre-processing of NMR spectra concerns phasing, baseline correction, peak alignment, apodization procedures, normalization and binning [16; 17] (see Figure 1). In particular, the binning procedure corresponding to the spectral segmentation is performed mainly in those cases of challenging spectral alignment or simply for reducing the data points [18]. Even though binning reduces data resolution, binning procedures are commonly used [19; 20; 21]. Figure 1: Schematic workflow illustrating the steps of NMR based metabolomic studies coupled with chemometrics and pathway analysis. (1) Sample preparation and NMR tube filling (top left); (2) experimental parameters setting and data acquisition (top right); (3) data processing (middle left); (4) execution of multivariate statistical analysis (bottom right); (5) determination of metabolic pathways (bottom left). Some figures are reprinted from Refs. [22; 23] under the terms of the CC-BY license. For what concerns normalization, recorded spectra are usually normalized by the total integrated area and thus the metabolites concentration can be compared among different samples.