A NMR-based, non-targeted multistep metabolic profiling revealed l-rhamnitol as a metabolite that characterised apples from different geographic origins

Microbial composition and metabolic profiles during machine-controlled intra-factory fermentation of cocoa beans harvested in semitropical area of Japan

Cocoa bean fermentation is typically performed in a spontaneous manner on farms in tropical countries or areas and involves several microbial groups. Metabolism by microbes markedly affects the quality of cocoa beans fermented and the chocolate produced thereof. The present study characterized the microbiota and their metabolic profiles in temperature- and humidity-controlled intra-factory cocoa fermentation in a semitropical area of Japan. Although environmental factors were uniform, the microbiota of cocoa beans subjected to intra-factory fermentation was not stable between tests, particularly in terms of the cell count levels and species observed. Fermentation was sometimes delayed, and fermenting microbes were present at very low levels after 24 hr of fermentation. Due to the unstable microbiota, the profiles of water-soluble compounds differed between tests, indicating the unstable qualities of the fermented cocoa beans. These results suggest the necessity of starter cultures not only in on-farm fermentation but also in machine-controlled intra-factory cocoa fermentation.

Garland chrysanthemum consumption ameliorates age-related hearing loss in C57BL/6 mouse; model system to explore hearing loss prevention foods in a short period

Garland chrysanthemum (Glebionis coronaria L.) is an antioxidant-rich leafy vegetable. We found that garland chrysanthemum consumption ameliorated age-related hearing loss (AHL) in C57BL/6J mice, an early onset model. We also found that AHL progression was significantly ameliorated by three of ten products. Metabolome analysis of the 10 products using nuclear magnetic resonance (NMR) spectroscopy indicated that phytosterols may be involved in the amelioration of AHL. However, the direct inhibitory effect of phytosterol mixture on mouse AHL progression was not identified. These results suggest that garland chrysanthemum consumption delays AHL development in mice and its efficiency varies depending on the source of product. Our findings also suggest that phytosterol content in garland chrysanthemum function as an evaluation marker for the efficiency. Furthermore, to accelerate the search for foods that prevent AHL, we have used these data to develop an automatic threshold determination method for auditory brainstem response using machine learning.

TLC-Based Fingerprinting Analysis of the Geographical Variation of Melastoma malabathricum in Inland and Archipelago Regions: A Rapid and Easy-to-Use Tool for Field Metabolomics Studies

Melastoma malabathricum is an Indo-Pacific herb that has been used traditionally to treat numerous ailments such as wounds, dysentery, diarrhea, toothache, and diabetes. The objective of this study was to evaluate the variability of the metabolic profiles of M. malabathricum across its geographic distribution. By employing thin layer chromatography (TLC), specimens collected from six terrestrial and archipelago regions of Indonesia were analyzed by densitometry for metabolomic fingerprinting analysis combined with chemometric tools: principal component analysis (PCA) and hierarchical cluster analysis (HCA). Two PCAs were identified as PC1 and PC2 with 41.90% and 20.36%, respectively. Our results indicate the importance of considering geographic distribution during field-collection efforts since they demonstrate regional metabolic variation in secondary metabolites of M. malabathricum, as illustrated by TLC and their biological activities.

NMR Tracing of Food Geographical Origin: The Impact of Seasonality, Cultivar and Production Year on Data Analysis

The traceability of typical foodstuffs is necessary to protect high quality of traditional products. It is well-known that several factors could influence metabolites content in certified foods, but soil composition, altitude, latitude and coded production protocols constitute the territorial conditions responsible for the peculiar organoleptic and nutritional properties of labelled foods. Instead, regardless of origin, seasonality, cultivar, collection year can affect all agricultural products, so it is appropriate to include them in data analysis in order to obtain a correct interpretation of the differences linked to growing areas alone. Therefore, it is useful to use a flexible all-round technique, and NMR spectroscopy coupled with multivariate statistical analysis is considered a powerful means of assessing food authenticity. The purpose of this review is to investigate the relevance of year, cultivar, and seasonal period in the determination of food geographical origin using NMR spectroscopy. The strategy for testing these three factors may differ from author to author, but a preliminary study of cultivar or collection year effects on NMR spectra is the most popular method before starting the geographical characterization of samples. In summary, based on the available literature, the most significant influence is due to cultivar, followed by harvesting year, however seasonality is not considered a source of variability in data analysis.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Metabolomic evaluation of different starter culture effects on water-soluble and volatile compound profiles in nozawana pickle fermentation

Metabolomic characterization of a lactic-fermented pickle of nozawana (Brassica rapa L. var. hakabura) was conducted to evaluate the effects of different starter culture strains on the chemical profiles. We compared the profiles of water-soluble and volatile compounds obtained by non-targeted nuclear magnetic resonance and solid-phase microextraction gas chromatography/mass spectrometry analyses. Principal component analyses indicated that the fermented samples differed significantly in terms of the levels of various compounds, including taste- and aroma-active components, such as water-soluble residual sugars, organic acids, mannitol, ethanol, dihydroxyacetone, ornithine, γ-aminobutyric acid, choline, volatile isothiocyanates, 3,4-epithiobutyl cyanide, 2,3-butanedione, acetoin, ethyl acetate, dimethyl trisulfide, and S-methyl thioacetate. Fermentation with a Latilactobacillus curvatus culture was associated with a unique metabolite profile characterized by higher levels of isothiocyanates and hexanoic acid and lower levels of lactic acid, acetic acid, acetoin, and 2,3-butanedione. These variations in the chemical profile might be associated with different qualities in fermented nozawana pickle products.

Estimation of komatsuna freshness using visible and near-infrared spectroscopy based on the interpretation of NMR metabolomics analysis

The objective of this study was to explore the potentiality and mechanism of visible and near-infrared (Vis-NIR) spectroscopy in estimating the freshness of komatsuna. We monitored the cumulative CO₂ production of komatsuna stored under different conditions as a freshness indicator and measured the Vis-NIR spectra of komatsuna as the predictor. Using the informative wavelengths (IW) selected using the stepwise selectivity ratio method, we constructed an accurate freshness prediction model through PLSR analysis. The IW in the visible region were attributed to pigments such as chlorophyll. In the NIR region, ten amino acids were identified as directly or indirectly contributing to the IW and were highly related to freshness. They were confirmed on the basis of the strong correlations between the informative NIR signals and NMR signals, which were determined using statistical heterospectroscopy. The results demonstrate the feasibility of Vis-NIR spectroscopy in estimating the freshness of komatsuna using the IW.

Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework

Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T₂ relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

NMR Characterization of Ten Apple Cultivars from the Piedmont Region

The metabolite profile of ten traditional apple cultivars grown in the Piedmont region (Italy) was studied by means of nuclear magnetic resonance spectroscopy, identifying an overall number of 36 compounds. A more complete assignment of the proton nuclear magnetic resonance (¹H NMR) resonances from hydroalcoholic and organic apple extracts with respect to literature data was reported, identifying fructose tautomeric forms, galacturonic acid, γ-aminobutyric acid (GABA), p-coumaroyl moiety, phosphatidylcholine, and digalactosyldiacylglycerol. The chemical profile of each apple cultivar was defined by thorough quantitative NMR analysis of four sugars (fructose, glucose, sucrose, and xylose), nine organic acids (acetic, citric, formic, citramalic, lactic, malic, quinic, and galacturonic acids), six amino acids (alanine, asparagine, aspartate, GABA, isoleucine, and valine), rhamnitol, p-coumaroyl derivative, phloretin/phloridzin and choline, as well as β-sitosterol, fatty acid chains, phosphatidylcholine, and digalactosyldiacylglycerol. Finally, the application of PCA analysis allowed us to highlight possible differences/similarities. The Magnana cultivar showed the highest content of sugars, GABA, valine, isoleucine, and alanine. The Runsé cultivar was characterized by high amounts of organic acids, whereas the Gamba Fina cultivar showed a high content of chlorogenic acid. A significant amount of quinic acid was detected in the Carla cultivar. The knowledge of apple chemical profiles can be useful for industries interested in specific compounds for obtaining ingredients of food supplements and functional foods and for promoting apple valorization and preservation.

Determination of the Geographical Origin of Asparagus officinalis L. by 1H NMR Spectroscopy

Food authenticity concerning the geographical origin becomes increasingly important for consumers, food industries, and food authorities. In this study, nontargeted ¹H NMR metabolomics combined with machine learning methodologies was applied to successfully distinguish the geographical origin of 237 samples of white asparagus from Germany, Poland, The Netherlands, Spain, Greece, and Peru. Support vector classification of the geographical origin achieved an accuracy of 91.5% for the entire sample set and 87.8% after undersampling the majority class. Important regions of the spectra could be identified and assigned to potential chemical markers. A subset of samples was compared to isotope-ratio mass spectrometry (IRMS), an established method for the determination of origin of white asparagus in Germany. Here, SVM classification led to accuracies of 79.4% for NMR and 70.9% for IRMS. Finally, the classification of asparagus from different German regions was evaluated, and the influence of year and variety was analyzed.

Biomass composition explains fruit relative growth rate and discriminates climacteric from non-climacteric species

Fleshy fruits are very varied, whether in terms of their composition, physiology, or rate and duration of growth. To understand the mechanisms that link metabolism to phenotypes, which would help the targeting of breeding strategies, we compared eight fleshy fruit species during development and ripening. Three herbaceous (eggplant, pepper, and cucumber), three tree (apple, peach, and clementine) and two vine (kiwifruit and grape) species were selected for their diversity. Fruit fresh weight and biomass composition, including the major soluble and insoluble components, were determined throughout fruit development and ripening. Best-fitting models of fruit weight were used to estimate relative growth rate (RGR), which was significantly correlated with several biomass components, especially protein content (R=84), stearate (R=0.72), palmitate (R=0.72), and lignocerate (R=0.68). The strong link between biomass composition and RGR was further evidenced by generalized linear models that predicted RGR with R-values exceeding 0.9. Comparison of the fruit also showed that climacteric fruit (apple, peach, kiwifruit) contained more non-cellulosic cell-wall glucose and fucose, and more starch, than non-climacteric fruit. The rate of starch net accumulation was also higher in climacteric fruit. These results suggest that the way biomass is constructed has a major influence on performance, especially growth rate.

Metabolomic-Based Approach to Identify Biomarkers of Apple Intake

SCOPE

There is an increased interest in developing biomarkers of food intake to address some of the limitations associated with self-reported data. The objective is to identify biomarkers of apple intake, examine dose-response relationships, and agreement with self-reported data.

METHODS AND RESULTS

Metabolomic data from three studies are examined: an acute intervention, a short-term intervention, and a free-living cohort study. Fasting and postprandial urine samples are collected for analysis by ¹ H-NMR and liquid chromatography-mass spectrometry (LC-MS). Calibration curves are developed to determine apple intake and classify individuals into categories of intake. Multivariate analysis of data reveals that levels of multiple metabolites increase significantly post-apple consumption, compared to the control food-broccoli. In the dose-response study, urinary xylose, epicatechin sulfate, and 2,6-dimethyl-2-(2-hydroxyethyl)-3,4-dihydro-2H-1-benzopyran increase as apple intake increases. Urinary xylose concentrations in a free-living cohort perform poorly at an individual level but are capable of ranking individuals in categories of intake.

CONCLUSION

Urinary xylose exhibits a dose-response relationship with apple intake and performs well as a ranking biomarker in the population study. Other potential biomarkers are identified and future work will combine these with xylose in a biomarker panel which may allow for a more objective determination of individual intake.

Amino Acid Profile of Fruits as Potential Fingerprints of Varietal Origin

This study aims to assess the capability of the ¹H-NMR profiling of fruits from different genera in combination with multivariate data analysis to provide feasible information for fruit juices’ authenticity in terms of botanical origin. Nine fruit varieties from four genera were selected for the experimental plan. The juice obtained from the fruits was characterized using the ¹H-NMR technique, selecting the obtained amino acid profile of fruits as a potential specific fingerprint. Due to the complex information provided by the NMR spectra, a chemometric approach of the data was further applied to enable the differentiation of the fruit samples, highlighting thus its suitability as a discrimination tool for the varietal origin. The advantage of this analytical approach is given by the relatively simple working procedure, which consists of an easy, fast, and accessible preparation stage while providing complex information on fruit composition.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

1H-NMR-based metabolomic profiles of different sweet melon (Cucumis melo L.) Salento varieties: Analysis and comparison

Melon (Cucumis melo L.) is a significant source of substances able to provide human health benefits. From the 18th century in the Salento area (Apulia region), the cultivation of melon varieties (C. melo L.) has always been intense. Over the years, the production of this fruit has involved a large number of selected and preserved varieties in the different local districts. Unfortunately, most of the characteristics of locally grown vegetable varieties do not match the food industry requirements. Moreover, the agricultural land abandon leads these varieties to quickly disappear, thus affecting the intraspecific biodiversity. In order to characterize the inter-variety diversity of sweet melon (C. melo L. ssp. melo group inodorus) and the potential differences in the nutritional quality of fruits, a first investigation on the juice of five sweet melon varieties (locally known as "allungato", "scurzune", "egiziano", "minna de monaca", "pinto"), cultivated exclusively in the Salento area, was performed by ¹H-NMR spectroscopy and Multivariate Analysis (MVA). The analysis grouped the samples into clusters according to the different variety. Interestingly, a different sugar (mono and disaccharides) content was observed among the grouped varieties, being sweetness the main characteristic of sweet melon quality and taste. A relative higher accumulation of monosaccharides (α-d and β-d glucose and α/β-d fructose) was found, in particular for the "minna de monaca" with respect to "allungato", "egiziano" and "pinto" varieties. Moreover, a marked high content of polyphenols and aromatic aminoacids as phenylalanine and tyrosine characterize the "allungato", "minna de monaca" and "pinto" varieties. An NMR-based metabolomic approach was used for the first time to describe these local landraces. This method may integrate other actions in order to achieving a reduction in the current rate of erosion of the biodiversity of Apulian horticultural species.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

In vitro and in silico characterisation of Lactobacillus paraplantarum D2-1, a starter culture for soymilk fermentation

Soymilk contains several functional nutrients and is thus a promising ingredient for production of functional foods. The present research aimed to study starter properties, functional characteristics and safety of Lactobacillus paraplantarum D2-1, a promising starter culture for soymilk fermentation. Strain D2-1 actively fermented soymilk within 24 h but had weak activity of additional acid production after 7 d. Succinate and acetoin, which could be linked to flavour and taste, were accumulated in fermented soymilk. In vitro study revealed that the organism has several beneficial properties, including high survival ability in artificial gastric juice, high abilities of mucus adhesion and biofilm formation and production of γ-aminobutyric acid and conjugated linoleic acid, without any significant risks for consumption. Genome sequencing supported the desirable metabolic properties of the strain. These results indicate that L. paraplantarum D2-1 is a suitable starter for soymilk fermentation and is a promising probiotic candidate that can be used safely.

The integration of multi-platform MS-based metabolomics and multivariate analysis for the geographical origin discrimination of Oryza sativa L

For the authentication of white rice from different geographical origins, the selection of outstanding discrimination markers is essential. In this study, 80 commercial white rice samples were collected from local markets of Korea and China and discriminated by mass spectrometry-based untargeted metabolomics approaches. Additionally, the potential markers that belong to sugars & sugar alcohols, fatty acids, and phospholipids were examined using several multivariate analyses to measure their discrimination efficiencies. Unsupervised analyses, including principal component analysis and k-means clustering demonstrated the potential of the geographical classification of white rice between Korea and China by fatty acids and phospholipids. In addition, the accuracy, goodness-of-fit (R²), goodness-of-prediction (Q²), and permutation test p-value derived from phospholipid-based partial least squares-discriminant analysis were 1.000, 0.902, 0.870, and 0.001, respectively. Random Forests further consolidated the discrimination ability of phospholipids. Furthermore, an independent validation set containing 20 white rice samples also confirmed that phospholipids were the excellent discrimination markers for white rice between two countries. In conclusion, the proposed approach successfully highlighted phospholipids as the better discrimination markers than sugars & sugar alcohols and fatty acids in differentiating white rice between Korea and China.

NMR-based untargeted metabolomic study of hydrogen peroxide-induced development and diapause termination in brine shrimp

Artemia diapause has been extensively studied in embryonic biology for a long time. It has been demonstrated that hydrogen peroxide (H₂O₂) can increase the hatching rate in the development and diapause termination of Artemia cysts. This study used an untargeted ¹H NMR-based metabolomic approach to explore the physiological regulation of H₂O₂ in initiating the development and terminating the diapause of Artemia cysts. This experiment was divided into two parts. In the first part, we analyzed three groups with or without H₂O₂ as control-0h, control-5h and H₂O₂ (180μM)-5h; in the second part, after 7-d incubation, the non-hatching cysts were treated with different H₂O₂ concentrations as low as 180μM and as high 1800μM. The results showed that arginine and proline metabolism were up-regulated after 5h, and H₂O₂ up-regulated valine, leucine and isoleucine biosynthesis in the development of cysts. In the second part, low H₂O₂ (180μM) showed alanine, aspartate and glutamate metabolism, but high H₂O₂ (1800μM) also up-regulated arginine and proline metabolism, as in the control group without H₂O₂ stimulus. These results suggest that enough H₂O₂ can catalyze cell transcription and translation in Artemia cysts, and it improves the cell growth rate, thus allowing embryo cells to grow again.

Rapid discrimination of strain-dependent fermentation characteristics among Lactobacillus strains by NMR-based metabolomics of fermented vegetable juice

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods.

Integrated Analytical and Statistical Two-Dimensional Spectroscopy Strategy for Metabolite Identification: Application to Dietary Biomarkers

A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately, the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol), and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved 1H NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g., 2D-NMR spectroscopy, solid-phase extraction, liquid chromatography prefaced mass spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D 1H NMR as part of routine metabolic profiling in large data sets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resources.

A survey of metabolic changes in potato leaves by NMR-based metabolic profiling in relation to resistance to late blight disease under field conditions

Non-targeted nuclear magnetic resonance (NMR)-based metabolic profiling was applied to potato leaves to survey metabolic changes associated with late blight resistance under field conditions. Potato plants were grown in an experimental field, and the compound leaves with no visible symptoms were collected from 20 cultivars/lines at two sampling time points: (i) the time of initial presentation of symptoms in susceptible cultivars and (ii) 12 days before this initiation. ¹ H NMR spectra of the foliar metabolites soluble in deuterium oxide- or methanol-d₄ -based buffers were measured and used for multivariate analysis. Principal component analysis for six cultivars at symptom initiation showed a class separation corresponding to their levels of late blight resistance. This separation was primarily explained by higher levels of malic acid, methanol, and rutin and a lower level of sucrose in the resistant cultivars than in the susceptible ones. Partial least squares regression revealed that the levels of these metabolites were strongly associated with the disease severity measured in this study under field conditions. These associations were observed only for the leaves harvested at the symptom initiation stage, but not for those collected 12 days beforehand. Subsequently, a simple, alternative enzymatic assay for l-malic acid was used to estimate late blight resistance, as a model for applying the potential metabolic marker obtained. This study demonstrated the potential of metabolomics for field-grown plants in combination with targeted methods for quantifying marker levels, moving towards marker-assisted screening of new cultivars with durable late blight resistance. Copyright © 2016 John Wiley & Sons, Ltd.

NMR-Based Metabolic Profiling of Field-Grown Leaves from Sugar Beet Plants Harbouring Different Levels of Resistance to Cercospora Leaf Spot Disease

Cercospora leaf spot (CLS) is one of the most serious leaf diseases for sugar beet (Beta vulgaris L.) worldwide. The breeding of sugar beet cultivars with both high CLS resistance and high yield is a major challenge for breeders. In this study, we report the nuclear magnetic resonance (NMR)-based metabolic profiling of field-grown leaves for a subset of sugar beet genotypes harbouring different levels of CLS resistance. Leaves were collected from 12 sugar beet genotypes at four time points: seedling, early growth, root enlargement, and disease development stages. ¹H-NMR spectra of foliar metabolites soluble in a deuterium-oxide (D₂O)-based buffer were acquired and subjected to multivariate analyses. A principal component analysis (PCA) of the NMR data from the sugar beet leaves shows clear differences among the growth stages. At the later time points, the sugar and glycine betaine contents were increased, whereas the choline content was decreased. The relationship between the foliar metabolite profiles and resistance level to CLS was examined by combining partial least squares projection to latent structure (PLS) or orthogonal PLS (OPLS) analysis and univariate analyses. It was difficult to build a robust model for predicting precisely the disease severity indices (DSIs) of each genotype; however, GABA and Gln differentiated susceptible genotypes (genotypes with weak resistance) from resistant genotypes (genotypes with resistance greater than a moderate level) before inoculation tests. The results suggested that breeders might exclude susceptible genotypes from breeding programs based on foliar metabolites profiled without inoculation tests, which require an enormous amount of time and effort.

NMR window of molecular complexity showing homeostasis in superorganisms

NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.

Recent applications of NMR in food and dietary studies

Over the last decade, a wide variety of new foods have been introduced into the global marketplace, many with health benefits that exceed those of traditional foods. Simultaneously, a wide range of analytical technologies has evolved that allow greater capability for the determination of food composition. Nuclear magnetic resonance (NMR), traditionally a research tool used for structural elucidation, is now being used frequently for metabolomics and chemical fingerprinting. Its stability and inherent ease of quantification have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. In addition, NMR fingerprints have been used to differentiate cultivars, evaluate sensory properties of food and investigate the influence of growing conditions on food crops. Here we review the latest applications of NMR in food analysis. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes

Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based tool focusing on accumulating Nuclear Magnetic Resonance (NMR) spectra from various types of food processing. Therefore, we have developed a novel web-based tool, FoodPro, that includes a food NMR spectrum database and computes covariance and correlation spectra to tasting and hardness. As a result, FoodPro has accumulated 236 aqueous (extracted in D₂O) and 131 hydrophobic (extracted in CDCl₃) experimental bench-top 60-MHz NMR spectra, 1753 tastings scored by volunteers, and 139 hardness measurements recorded by a penetrometer, all placed into a core database. The database content was roughly classified into fish and vegetable groups from the viewpoint of different spectrum patterns. FoodPro can query a user food NMR spectrum, search similar NMR spectra with a specified similarity threshold, and then compute estimated tasting and hardness, covariance, and correlation spectra to tasting and hardness. Querying fish spectra exemplified specific covariance spectra to tasting and hardness, giving positive covariance for tasting at 1.31 ppm for lactate and 3.47 ppm for glucose and a positive covariance for hardness at 3.26 ppm for trimethylamine N-oxide.

Untargeted NMR Spectroscopic Analysis of the Metabolic Variety of New Apple Cultivars

Metabolome analyses by NMR spectroscopy can be used in quality control by generating unique fingerprints of different species. Hundreds of components and their variation between different samples can be analyzed in a few minutes/hours with high accuracy and low cost of sample preparation. Here, apple peel and pulp extracts of a variety of apple cultivars were studied to assess their suitability to discriminate between the different varieties. The cultivars comprised mainly newly bred varieties or ones that were brought onto the market in recent years. Multivariate analyses of peel and pulp extracts were able to unambiguously identify all cultivars, with peel extracts showing a higher discriminative power. The latter was increased if the highly concentrated sugar metabolites were omitted from the analysis. Whereas sugar concentrations lay within a narrow range, polyphenols, discussed as potential health promoting substances, and acids varied remarkably between the cultivars.

Profiling and Simultaneous Quantitative Determination of Anthocyanins in Wild Myrtus communis L. Berries from Different Geographical Areas in Sardinia and their Comparative Evaluation

INTRODUCTION

Myrtus communis L. (Myrtaceae) is a self-seeded shrub, widespread in Sardinia, with anti-inflammatory, antiseptic, antimicrobial, hypoglycemic and balsamic properties. Its berries, employed for the production of sweet myrtle liqueur, are characterised by a high content of bioactive polyphenols, mainly anthocyanins. Anthocyanin composition is quite specific for vegetables/fruits and can be used as a fingerprint to determine the authenticity, geographical origin and quality of raw materials, products and extracts.

OBJECTIVE

To rapidly analyse and determine anthocyanins in 17 samples of Myrtus communis berries by developing a platform based on the integration of UHPLC-MS/MS quantitative data and multivariate analysis with the aim of extracting the most information possible from the data.

METHODOLOGY

UHPLC-ESI-MS/MS methods, working in positive ion mode, were performed for the detection and determination of target compounds in multiple reaction monitoring (MRM) mode. Optimal chromatographic conditions were achieved using an XSelect HSS T3 column and a gradient elution with 0.1% formic acid in water and 0.1% formic acid in acetonitrile. Principal component analysis (PCA) was applied to the quantitative data to correlate and discriminate 17 geographical collections of Myrtus communis.

RESULTS

The developed quantitative method was reliable, sensitive and specific and was successfully applied to the quantification of 17 anthocyanins. Peonidin-3-O-glucoside was the most abundant compound in all the extracts investigated.

CONCLUSION

The developed methodology allows the identification of quali-quantitative differences among M. communis samples and thus defines the quality and value of this raw material for marketed products. Moreover, the reported data have an immediate commercial value due to the current interest in developing antioxidant nutraceuticals from Mediterranean plants, including Sardinian Myrtus communis. Copyright © 2016 John Wiley & Sons, Ltd.

Dietary intake of heat-killed Lactococcus lactis H61 delays age-related hearing loss in C57BL/6J mice

Age-related hearing loss (AHL) is a common disorder associated with aging. In this study, we investigated the effect of the intake of heat-killed Lactococcus lactis subsp. cremoris H61 (strain H61) on AHL in C57BL/6J mice. Measurement of the auditory brainstem response (ABR) demonstrated that female mice at 9 months of age fed a diet containing 0.05% strain H61 for 6 months maintained a significantly lower ABR threshold than control mice. The age-related loss of neurons and hair cells in the cochlea was suppressed by the intake of strain H61. Faecal analysis of bacterial flora revealed that the intake of strain H61 increased the prevalence of Lactobacillales, which is positively correlated with hearing ability in mice. Furthermore, plasma fatty acid levels were negatively correlated with hearing ability. Overall, the results supported that the intake of heat-killed strain H61 for 6 months altered the intestinal flora, affected plasma metabolite levels, including fatty acid levels, and retarded AHL in mice.

Application of metabolomics to toxicology of drugs of abuse: A mini review of metabolomics approach to acute and chronic toxicity studies

Metabolomics has been widely applied to toxicological fields, especially to elucidate the mechanism of action of toxicity. In this review, metabolomics application with focus on the studies of chronic and acute toxicities of drugs of abuse like stimulants, opioids and the recently-distributed designer drugs will be presented in addition to an outline of basic analytical techniques used in metabolomics. Limitation of metabolomics studies and future perspectives will be also provided.

Untargeted NMR-based methodology in the study of fruit metabolites

In this review, fundamental aspects of the untargeted NMR-based methodology applied to fruit characterization are described. The strategy to perform the structure elucidation of fruit metabolites is discussed with some examples of spectral assignments by 2D experiments. Primary ubiquitous metabolites as well as secondary species-specific metabolites, identified in different fruits using an untargeted ¹H-NMR approach, are summarized in a comprehensive way. Crucial aspects regarding the quantitative elaboration of spectral data are also discussed. The usefulness of the NMR-based metabolic profiling was highlighted using some results regarding quality, adulteration, varieties and geographical origin of fruits and fruit-derived products such as juices.