# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 4 Applications of Deep Learning Approaches for NMR-Based Metabolomics
### Food  
Foodomics is a term referred to the metabolomic approaches applied to foodstuffs for investigating topics mainly related with nutrition. Nowadays, DL methods are being progressively applied in the food field with different purposes, such as fraud detection [115]. Furthermore, another important issue is to guarantee the geographical origin and production/processing procedures of food, the precise proportions of ingredients, including additives and the kind of used raw materials. In this context, machine learning is a powerful method for achieving an adequate classification. For example, [PERSON] et al. [116] carried out NMR measurements using a not-conventional protocol to measure the magnetization relaxation times (both the longitudinal T\({}_{1}\) and transverse T\({}_{2}\)) and then they efficiently classified cooking oils, milk, and soy sauces (see Figure 19). Since the considered datasets are very large (typically about 5 \(\times\) 10\({}^{6}\) points each), the authors first reduced their size by means of the singular value decomposition, thus allowing a fast classification and also providing little insight into the sample physical properties. Figure 19 reports different combinations for the obtained classification features. Figure 19,b corresponds to the two components used by the Gaussian fit of those peaks revealed by the inverse 2D Laplace transform [117]. A sharp distinction of the samples is clearly shown for every adopted combination. The y-axes of Figure 19,c report the first component of T\({}_{1}\) versus the first and second components of T\({}_{2}\), respectively. Contrarily, the y-axes of Figure 19,d report the second component of T\({}_{1}\) versus the first and second components of T\({}_{2}\), respectively. The authors found that most of the trained models reached an accuracy up to 100% (see, for example, Figure 20). Finally, they also pointed out the effect of the sample temperature on classification accuracy for achieving reliable results (see Figure 20). Nowadays, deep neural networks (DNNs) are rarely used for metabolomics studies because the assignment of metabolites contribution in NMR spectra still lacks highly reliable yields due to the complexity of the investigated biological matrix and thus of the corresponding signals. As described in the previous section, different deep learning methods were used, but some of them are characterized by some limitations (i.e., low accuracy in classification). Some efforts were made to overcome this problem. [PERSON] et al. [118] recently developed a DNN method that includes the evaluation of the so-called mean decrease accuracy (MDA) to estimate every variable. It relies on a permutation algorithm that uses the same permutation algorithm as the one used in the previous section. Figure 19: T\({}_{1}\)–T\({}_{2}\) correlational maps classifying several kinds of oils: olive (blue), canola (orange), corn (yellow) and vegetable (purple) by using the two components used by the Gaussian fit of those peaks revealed by the inverse 2D Laplace transform. (**a,c**) report the first component of T\({}_{1}\) versus the first and second components of T\({}_{2}\), respectively. (**b,d**) report the second component of T\({}_{1}\) versus the first and second components of T\({}_{2}\), respectively. See main text and Ref. [116] for details. Figure reprinted with permission from Ref. [116]. Copyright 2018 Elsevier. Figure 20: (**a**) Comparison of the accuracy for the predictive power of the algorithms applied to classify cooking oil samples by employing three different classification training; (**b**) accuracy of predictive power applied to soy sauce sample highlighting the effect of temperature. Figure adapted with permission from Ref. [116]. Copyright 2018 Elsevier. that allows the recognition of the sample geographical origins and the identification of their biomarkers. On the other hand, for food authenticity and nutritional quality, the fast revelation of viable microbes is still a challenge.