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Subjects:
Physics, Applied
The potentiality of the nuclear magnetic resonance (NMR) technique within the field of metabolomics is currently employed for several purposes, including the detection of viable microbes in microbial food safety, the assessment of aquatic living organisms subjected to contaminated water, the identification of novel biomarkers to diagnose cancer diseases and the monitoring of the plant growth status changing environmental parameters in view of smart agriculture. Even before the development of artificial intelligence, statistical analyses were successfully applied in food analysis but with some limitations. For example, traditional methods are usually not very accurate in the classification of similar foods in contrast to modern deep learning approaches that allow enhancing all small differences. However, traditional methods usually constitute the first step, providing the input for neural networks with the aim to achieve a more accurate and automatic output. Furthermore, advanced computational algorithms can be applied not only for statistical analysis, but also to execute simulations whose predictions depend on the considered conditions.
- Biomedical
- Food
- nuclear magnetic resonance
- Deep Learning
1. Introduction
Metabolomics corresponds to the part of omics sciences that investigates the whole set of small molecule metabolites in an organism, representing a large number of compounds, such as a portion of organic acids, amino acids, carbohydrates, lipids, etc. [1][2][3]. The investigation and the recording of metabolites by target analysis, metabolic profiling and metabolic fingerprinting (i.e., extracellular metabolites) are fundamental steps for the discovery of biomarkers, helping in diagnoses and designing appropriate approaches for drug treatment of diseases [4][5]. There are many databases available with metabolomics data, including spectra acquired by nuclear magnetic resonance (NMR) and mass spectrometry (MS), but also metabolic pathways. Among them, we mention the Human Metabolome Database (HMDB) [6] and Biological Magnetic Resonance Bank (BMRB) [7] that contain information on a large number of metabolites gathered from different sources. By means of the corresponding web platform, it is possible, for instance, to search for mono- and bi-dimensional spectra of metabolites, starting from their peak position [3]. However, metabolomics databases still lack homogeneity mainly due to the different acquisition conditions, including employed instruments. Thus, the definition of uniform and minimum reporting standards and data formats would allow an easier comparison and a more accurate investigation of metabolomics data [8].
In recent years, NMR has become one of the most employed analytical non-destructive techniques for clinical metabolomics studies. In fact, it allows to detect and quantify metabolic components of a biological matrix whose concentration is comparable or bigger than 1μM. 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].
2. 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 [16]. 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, Greer et al. [17] carried out NMR measurements using a not-conventional protocol to measure the magnetization relaxation times and then they efficiently classified cooking oils, milk, and soy sauces.
Since the considered datasets are very large, 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.
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. Date et al. [18] 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 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. Here, we report a multilayer ANN example showing four input neurons, two hidden layers made of three neurons, and two output neurons.
Such a scheme was organized by Wang et al. [19] for the detection, by means of NMR spectroscopy coupled with deep ANNs, of pathogenic and non-pathogenic microbes. According to the classification method, each output neuron is associated to one possible output. Here, “Salmonella” shows the highest value of output, thus corresponding to the prediction performed by the used ANN. In such a case, the weights of each input are optimized to reach the wanted outcome throughout back propagation, thus defining multiple epochs and training cycles.
Once the network is trained, it is able to perform predictions on new input data. As already mentioned, the loss and the model accuracy provide a measure of the output goodness. In fact, the aim is to minimize the disagreement between the prediction and the reality (loss) and to maximize accuracy (cross-validation method). Thanks to this approach, Wang et al. [19] found that the used ANNs accurately predict 91.2% of unknown microbes and, after repeating the model training by considering just those metabolites whose amount increased with incubation time, they observed an accuracy up to 99.2%.
Machine learning and neural network approaches are simultaneously adopted to analyze large amounts of NMR metabolomics data for food safety [20]. This can be performed also by means of magnetic resonance imaging (MRI), which is an imaging technique relying on NMR principles. Within the food field, it is mainly used to resolve the tissue texture of foods [21][22]. On the other hand, Teimouri et al. [23] used PLSR, LDA, and ANN for the classification of the data collected by CCD images from food portions, different in color and geometrical aspects. In this way, they were able to classify 2800 food samples in one hour, with an overall accuracy of 93%. Instead, De Sousa Ribeiro et al. [24] developed a CNN approach able to reconstruct degraded information on the label of food packaging. Before applying CNNs, they started with K-means clustering and KNN classification algorithms for the extraction of suitable centroids.
3. Biomedical
Metabolomics-based NMR investigations, coupled with deep learning methods, are increasingly employed within the biomedical field. More profoundly, the use of complex DL architectures hardly allows achieving a predictive power with ranking or selection. As already discussed, DL models use several computational layers to analyze input signals and establish any eventual preferred direction for signal encoding (forward or backward). This procedure does not usually allow the interpretation of input signals in terms of the used model, making it hard to identify biomarkers in a network, where biological and DL modeling are connected.
Today, it is still necessary to uniform assessment metric for biomedical data classification or prediction, also avoiding false negatives in disease diagnosis. Further, deep learning is a promising methodology to treat data collecting by smart wearable sensors, which is considered fundamental in epidemic prediction, disease prevention, and clinical decision making, thus allowing a significant improvement in the quality of life [25][26].
With the aim to obtain an accurate metabolites identification from the observation of the corresponding peaks in complex mixtures, Kim et al. [27] developed a convolutional neural network (CNN) model, called SMART-Miner, which is trained on 657 chemical entities collected from HMDB and BMRB databases. After training, the model is able to automatically carry out the recognition of metabolites from 1H-13C HSQC NMR spectra of complex metabolite mixtures, showing higher performance in comparison with other NMR-based metabolomic tools.
Brougham et al. [28], by employing ANNs on 1H NMR spectra, performed a successful classification of four lung carcinoma cell lines, showing different drug-resistance patterns. The authors chose human lung carcinoma and adenocarcinoma cell lines together with specific drug-resistant daughter lines. The ANN architecture was constructed at first using three layers and the corresponding weights were determined by minimizing the root mean square error. Then, the authors analyzed networks with four layers, two of which are hidden. Their results show that the four-layer structure with two hidden layers provided a 100% successful classification [28]. These data are very interesting in terms of the robustness of the used approach: the cell lines were correctly classified, even though the effects were provoked by the operator and independently from the spectra chosen for training and validation.
Very recently, Di Donato et al. [29] analyzed serum samples from 94 elderly patients with early stage colorectal cancer and 75 elderly patients with metastatic colorectal cancer. With the aim to separately observe each different molecular component, these authors acquired one-dimensional proton NMR spectra by using three different pulse sequences for each sample: (i) a nuclear Overhauser effect spectroscopy pulse sequence to observe molecules with both low and high molecular weight; (ii) a common spin echo mono-dimensional pulse sequence [30] to observe only lighter metabolites and (iii) a common diffusion-edited pulse sequence to observe only macromolecules [29]. Their results, taking advantage of Kaplan–Meier curves for prognosis and of a PCA-based kNN analysis, allowed distinguishing relapse-free and metastatic cancer groups, with the advantage of obtaining information about the risks in the early stage of the colorectal cancer disease.
Peng et al. [31], by using two-dimensional NMR correlational spectroscopy on the longitudinal and transversal components of the magnetization relaxation time during its equilibrium recovery, were able to perform a molecular phenotyping of blood with the employment of supervised learning models, including neural networks. In detail, by means of a fast two-dimensional Laplace inversion [32], they obtained T1–T22 correlation spectra on a single drop of blood in a few minutes with a benchtop-sized NMR spectrometer. Then, they converted the NMR correlational maps for deep image analysis, achieving useful insights for medical decision making by the application of machine learning techniques. In particular, after an initial dimensionality reduction by unsupervised analysis, supervised neural network models were applied to train and predict the data that, at the end, were compared with the diagnostic prediction made by humans. The results showed that ML approaches outperformed the human being and took a much shorter time. Therefore, the authors demonstrated the clinical efficacy of this technique by analyzing human blood in different physiological and pathological conditions, such as oxidation states [31]. Concerning the analysis of different physiological conditions, it was reported the T1–T2 correlational maps of blood cells at oxygenated (a), oxidized (b), and deoxygenated (c) states. Three peaks with different relaxation times values were observed and assigned to the different microenvironments that water experiences in the considered samples of red blood cells. For the obtained maps, the coordinate for the bulk water peak (slowest component) is shown at the upper left of the map indicating T1 and T2 relaxations (in ms) and T1/T22-ratio, respectively. Instead, the coordinates of the fastest components, due to hydration and bound water molecules [33], are reported close to the corresponding correlation peak.
This entry is adapted from the peer-reviewed paper 10.3390/app12062824
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