Discrimination of the geographical origin of beef by (1)H NMR-based metabolomics

Authentication of aged beef in terms of aging time and aging type by 1H NMR spectroscopy

Meat authenticity addresses parameters such as species, breed, sex, housing system and postmortem treatment. Seventy-four beef backs from two breeds ('Fleckvieh' and 'Schwarzbunt') and three cattle types (heifer, cow, young bull) were dry-aged and wet-aged up to 28 days and analyzed by 1H NMR spectroscopy. Statistical models based on partial least squares regression and discriminant analysis were performed to classify the beef samples by breed, cattle type, aging time, and aging type based on their 1H NMR spectra. The aging time of beef samples can be predicted with an error ± 2.28 days. The cattle type model has an accuracy of cross-validation of 99.2 %, the breed models of 100 % and the aging type model for 28-days aged samples of 99.6 %. These models allow the authentication of beef samples in terms of breed, cattle type, aging time, and aging type with a single 1H NMR measurement.

Metabolomic characterization of Liancheng white and Cherry Valley duck breast meat and their relation to meat quality

Liancheng white duck is a typical local duck breed in Fujian Province famous for its meat traits. To better understand how meat quality varies with breed, the chemical composition of breast meats of Liancheng white ducks (LD) and Cherry Valley ducks (CD) were examined using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS).The correlation between meat quality and the differential metabolites was further analyzed. The results showed that the effects of breed on duck breast meat were significant for pH, color, cooking loss, and shear force. Liancheng white duck breast meat exhibited a higher shear force and pH, and lower cooking loss and lightness (L*24), redness (a*24), and yellowness (b*24) than CD. Metabolomic analysis revealed significant differences between the meat extracts from the 2 duck breeds. A total of 49 and 57 significantly different metabolites were identified in positive and negative ion modes, respectively. These differentially accumulated metabolites (DAMs) could be divided into 28 classes, of which the 4 main categories were carbohydrates, amino acids, fatty acids, and eicosanoids. Liancheng white duck might have better nutritional and medicinal value considering the higher content of (4Z,7Z,10Z,13Z,16Z,19Z)-4,7,10,13,16,19-docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), eicosapentaenoic acid (EPA), and prostaglandinF3α (PGF3α), having anti-inflammatory orantioxidant effects. Carbohydrate concentration negatively correlated with pH24. The 4 metabolites positively correlated with the shear force. These results provide an overall perspective for bridging the gap between variation of duck meat quality and metabolites with respect to breed.

Biomarker Development for Identifying Mud Loach (Misgurnus mizolepis) Origin Country Using Untargeted Metabolite Profiling

Mud loach (Misgurnus mizolepis) has long been consumed in Korea. Recently, Chinese mud loaches were replaced with expensive Korean mud loaches, owing to taste and preference. Such issues occur in aquatic food distribution processes, leading to inferior food delivery. Previously, a study was conducted to confirm the origin of mud loaches using genetic analysis. However, untargeted metabolites profiling of mud loaches has not been reported. Untargeted metabolomics provides information on the overall metabolic profiling of a sample, allowing the identification of new metabolites. Here, we analyzed the metabolites of mud loaches of different geographical origins using liquid chromatography (LC)-quadrupole-time-of-flight mass spectrometry (MS). Orthogonal partial least squares discriminant analysis from LC/MS datasets showed a clear distinction between Korean and Chinese mud loaches, and univariate statistical analysis showed significantly different metabolites between them. N-acetylhistidine and anserine were selected as biomarkers for geographical origin discrimination using the receiver operating characteristic curve. N-acetylhistidine and anserine levels were significantly higher in Chinese than in Korean mud loaches. These results indicate that metabolic analysis can be used to discriminate between the geographical origins of mud loaches, curtailing the inadvertent substitution of mud loaches from different regions.

Integrating (Nutri-)Metabolomics into the One Health Tendency-The Key for Personalized Medicine Advancement

Metabolomics is an advanced technology, still under development, with multiple research applications, especially in the field of health. Individual metabolic profiles, the functionality of the body, as well as its interaction with the environment, can be established using this technology. The body's response to various external factors, including the food consumed and the nutrients it contains, has increased researchers' interest in nutrimetabolomics. Establishing correlations between diet and the occurrence of various diseases, or even the development of personalized nutrition plans, could contribute to advances in precision medicine. The interdependence between humans, animals, and the environment is of particular importance today, with the dramatic emergence and spread of zoonotic diseases, food, water and soil contamination, and the degradation of resources and habitats. All these events have led to an increase in risk factors for functional diseases, burdening global health. Thus, this study aimed to highlight the importance of metabolomics, and, in particular, nutrimetabolomics, as a technical solution for a holistic, collaborative, and precise approach for the advancement of the One Health strategy.

1H nuclear magnetic resonance-based metabolomics study of serum and pectoralis major for different commercial chicken breeds

This study aimed to characterize the metabolic composition of four types of commercially available chicken breeds [village chicken, colored broiler (Hubbard), broiler (Cobb), and spent layers (Dekalb)] by ¹H NMR coupling and discriminate them using multivariate analysis. Five chickens were collected for each chicken breed based on the marketing age from the respective commercial farms. The orthogonal partial least squares discriminant analysis (OPLS-DA) results showed an obvious separation of local village chickens from the other breeds based on the metabolites present in their serum and meat (pectoralis major). The cumulative values of Q ², R ² X, and R ² Y of the OPLS-DA model for chicken serum were 0.722, 0.877, and 0.841. For the pectoralis major muscle, the cumulative values of Q ², R ² X, and R ² Y of the OPLS-DA model were reported as 0.684, 0.781, and 0.786, respectively. The quality of both OPLS-DA models was accepted by the cumulative values of Q ² ≥ 0.5 and R ² ≥ 0.65. The ¹H NMR result with multivariate analysis has successfully distinguished local village chicken from the other three commercial chicken breeds based on serum and pectoralis major muscle. Nonetheless, colored broiler (Hubbard) was not distinguished from broiler (Cobb) and spent layers (Dekalb) in serum and pectoralis major, respectively. The OPLS-DA assessment in this study identified 19 and 15 potential metabolites for discriminating different chicken breeds in serum and pectoralis major muscle, respectively. Some of the prominent metabolites identified include amino acids (betaine, glycine, glutamine, guanidoacetate, phenylalanine, and valine), nucleotides (IMP and NAD+), organic acids (lactate, malate, and succinate), peptide (anserine), and sugar alcohol (myo-inositol).

1H NMR Metabolic Profiling and Meat Quality in Three Beef Cattle Breeds from Northeastern Thailand

The increasing need for effective analytical tools to evaluate beef quality has prompted the development of new procedures to improve the animal sector's performance. In this study, three beef breeds-Thai native (TN), crossbred Brahman × Thai native (BT), and crossbred Charolais × Brahman (CB)-were compared in terms of their physicochemical and metabolic profiles. The findings demonstrated that TN beef was lighter and tougher than other beef. Beef odor was stronger in BT. In addition, CB beef was the most tender and had the highest intramuscular fat content. Twenty-one different metabolites were found overall through NMR and chemometric approaches. The primary factors contributing to the difference in OPLS-DA loading plots were acetylcholine, valine, adenine, leucine, phosphocreatine, β-hydroxypyruvate, ethanol, adenosine diphosphate, creatine, acetylcholine, and lactate. The multivariate analysis indicated that these metabolites in beef cattle breeds could be distinguished using NMR spectroscopy. The results of this study provide valuable information on the quality and meat metabolites of different breeds. This could help in the development of a more accurate assessment of the quality of beef in future research.

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.

Cheese characterization and authentication through lipid biomarkers obtained by high-resolution 1H NMR profiling

Food quality and safety are at the heart of consumers' concerns across the world. Dairy products, because of their large consumption, are fertile ground for fraudulent acts. This fact justifies the development of effective, accessible, and rapid analytical methods for their authentication. A high-resolution spectral treatment method previously developed by our team was applied to ¹H NMR spectra of cheese triacylglycerols. 178 Peaks were thus quantitated and successfully used in the construction of multivariate models for the quantitation of individual fatty acids and for the classification of cheese samples according to the producing species, to their origin and variety. Besides, several peaks related to the amount and position of anteisopentadecanoic, butyric, α-linolenic, myristoleic, rumenic, and vaccenic acids were, among others, specific biomarkers of cheese groups. For the first time in ¹H NMR, we were able to identify and to quantitate signals related to minor fatty acids within cheese triacylglycerols.

Nontargeted Analysis of Lipid Extracts Using 1H NMR Spectroscopy Combined with Multivariate Statistical Analysis to Discriminate between the Animal Species of Raw and Processed Meat

The animal species of raw meat and processed meat products was determined by ¹H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on comprehensive lipid extraction to capture nonpolar and polar (amphiphilic) fat components of meat. A nontargeted approach was used to analyze the ¹H NMR data, followed by a principal component analysis, linear discrimination analysis, and cross-validation embedded in a Monte Carlo re-sampling approach. A total of 437 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, and poultry) were collected to build and/or test the classification model. On average, 98% of the analyzed raw meat samples and 97% of the processed meat products were correctly classified with respect to meat species. Furthermore, relevant spectral regions to identify potential chemical markers such as linoleic acids, trans-fatty acids, and cholesterol for the meat species classification were described.

A Preliminary Nuclear Magnetic Resonance Metabolomics Study Identifies Metabolites that Could Serve as Diagnostic Markers of Major Depressive Disorder

BACKGROUND

The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.

METHODOLOGY

Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models.

RESULTS

In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.

CONCLUSION

Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

NMR-based metabolic profiling and discrimination of wild tropical tunas by species, size category, geographic origin, and on-board storage condition

Tunas are among the most traded and valued fish species, and good traceability of tuna products in the world market is needed to protect both consumers and tuna stocks. To that purpose, high-resolution proton nuclear magnetic resonance (¹H NMR) spectroscopy combined with multivariate data analysis was used to investigate the molecular components of the aqueous extract of white and red muscles in three species of wild tropical tuna species, namely yellowfin tuna (Thunnus albacares), skipjack tuna (Katsuwonus pelamis) and bigeye tuna (T. obesus). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) applied to the processed ¹H NMR spectra showed significant separation according to the species and size category (i.e., small tunas < 80 cm fork length vs large tunas > 80 cm fork length), the storage conditions on-board the purse-seine vessels (i.e., brine- vs deep-freezing), and the geographical origin (i.e., where the tuna was caught: Mozambique Channel vs western-central Indian Ocean). The major groups of metabolites responsible for differentiation in PLS-DA score plots were the dipeptides (anserine, carnosine) and organic acids (lactate, creatine/phosphocreatine) in the white muscle, and the free amino acids, essential nutrients (choline and its derivatives, phosphatidylethanolamine), dipeptides and organic acids in the red muscle. Our results showed that NMR-based metabolomics is a powerful tool to efficiently discriminate specific profiles among wild tuna species, raw muscle tissues, fish storage conditions and tuna geographical origin.

Analyzing the Metabolomic Profile of Yellowtail (Seriola quinquerdiata) by Capillary Electrophoresis-Time of Flight Mass Spectrometry to Determine Geographical Origin

Country-of-origin violations have occurred in which some merchants have fraudulently sold cheap Japanese yellowtail (Seriola quinqueradiata) by presenting them as domestic Korean products. There are many methods for determining the origins of marine organisms, such as molecular genetic methods and isotope analysis. However, this study aimed to develop a method for determining the origins of aquatic products using metabolite analysis technology. Ten yellowtail each from Korea and Japan were analyzed by capillary electrophoresis–time of flight/mass spectrometry (CETOF/MS). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) results showed highly differing aspects between the Korean and Japanese samples. In the tricarboxylic acid (TCA) cycle, citric, malic, oxaloglutaric, and fumaric acids exhibited significant differences between Korean and Japanese yellowtail. Sixteen of the twenty essential amino acids analyzed as metabolites also differed significantly. All amino acids were involved in protein digestion, absorption, and metabolism. All 16 amino acid contents were higher in Japanese yellowtail than in Korean yellowtail, except for glutamine. The fasting period was found to be the biggest factor contributing to the difference in amino acid contents, in addition to environmental factors (including feeding habits). These significant differences indicated that metabolomics could be used to determine geographical origin.

Current State of Metabolomics Research in Meat Quality Analysis and Authentication

In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using ¹H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

Nuclear Magnetic Resonance (NMR)-Based Quantification on Flavor-Active and Bioactive Compounds and Application for Distinguishment of Chicken Breeds

The purpose of this study was to use ¹H nuclear magnetic resonance (¹H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using ¹H-¹³C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R²=0.945; Q²=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both ¹H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.

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.

Food Profiling: Determination of the Geographical Origin of Walnuts by 1H NMR Spectroscopy Using the Polar Extract

Walnuts, with their health-promoting ingredients, are among the most popular nuts, and practicable methods for determining their geographical origin are needed to tackle food fraud. Authentic walnut samples (235, Juglans Regia L.) from different harvest years (2016-2019) and countries were analyzed by ¹H NMR spectroscopy in combination with chemometric methods to determine their geographical origin. Two sample groups were analyzed at a time with a support vector machine algorithm to obtain two-class classifier models. In total, nine two-class models were built (e.g., Germany/China, France/Germany, and USA/Switzerland), and a repeated nested cross-validation was performed. The models obtained showed high accuracies from 78.0% (±2.3%) to 96.6% (±0.6%). Furthermore, identification of potential chemical markers in the walnut extract was performed.

Analysis of aging type- and aging time-related changes in the polar fraction of metabolome of beef by 1H NMR spectroscopy

The tenderness and taste of beef is improved by either dry- or wet-aging or a combination of both. The objective was to develop a validated method for detecting differences in the polar fraction of metabolome in dry-aged and wet-aged beef over the aging time and quantifying the metabolites of interest by ¹H NMR spectroscopy using beef. Sixty strip loin (M. longissimus dorsi) samples aged in different ways (wet-aging vs. dry-aging) and aging times (0, 7, 14, 21, 28 days) were analyzed. The aging type could be defined by linear discriminant analysis with an accuracy of 95%. Ten (lactic acid, alanine, methionine, fumaric acid, inosine, inosine monophosphate, creatine, betaine, carnosine and hypoxanthine) out of eighteen metabolites differ significantly (p < 0.05) in content depending on the aging type. Fifteen metabolites in dry-aged and ten in wet-aged beef correlate with the aging time (r > 0.7, <-0.7), which shows significant aging time-related effects on the polar fraction of metabolome.

The application of 1H NMR to explore the taste difference caused by taste-active metabolites of different Chinese sauce-stewed beef

In this study, we developed a method for the quantification of taste-active metabolites of Chinese commercial sauce-stewed beef by using 1H NMR spectroscopy coupled with multivariate data analysis. This method was applied to analyze the taste difference which caused by taste-active metabolites of different Chinese sauce-stewed beef. Beef samples demonstrated to consist of 25 metabolites, including amino acids, sugars, organic acids, nucleic aides, and their derivatives. PC1 and PC2 explained a total of 85.1 and 13.1% of variables, respectively. Metabolites such as isoleucine, histidine, glutamate, pyroglutamate, sucrose, lactate, creatine, carnitine, and creatinine were kept at a higher levels compared with other metabolites in the four products. Sensory evaluation was also done to help analyze the taste difference. This microcosmic approach of using high-resolution NMR spectrometry to analyze beef products has rarely been reported. This work established a feasible method to distinguish the taste difference of different Chinese sauce-stewed beef.

Mass spectrometry-based untargeted metabolomics approach for differentiation of beef of different geographic origins

Beef is a common staple food in many countries, and there is a growing concern over misinformation of beef products, such as false claims of origin, species and production methods. In this study, we used a mass spectrometry-based metabolomics approach to study the metabolite profiles of beef samples purchased from local retailers in Hong Kong. Using multivariate analysis, beef samples from different a) geographical origins, namely the United States (US), Japan and Australia, and b) feeding regimes could be differentiated. We identified twenty-four metabolites to distinguish beef samples from different countries, ten metabolites to identify Angus beef samples from others and seven metabolites to discriminate Australian beef produced by the organic farming from that produced using other farming modes. Based on results of this study, it is concluded that metabolomics provides an efficient strategy for tracing and authenticating beef products to ensure their quality and to protect consumer rights.

Phosphorus NMR and Its Application to Metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

1 H-NMR metabolomics profiling of recombinant tobacco plants holding a promoter of a sesquiterpene cyclase

INTRODUCTION

Nicotiana tabacum is a plant model intensively used in the bio-engineering pharmaceutical industry as a platform to produce drugs and therapeutic agents. Currently, no information regarding the non-targeted metabolome of transgenic tobacco containing recombinant regulatory sequences is available.

OBJECTIVE

To compare the proton nuclear magnetic resonance (1 H-NMR) metabolomics profiling of a recombinant Nicotiana tabacum strain containing a promoter of a sesquiterpene cyclase from Capsicum annuum driving GUS expression, versus wild-type samples. Methodology The non-targeted 1 H-NMR metabolome was obtained and processed by principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). The differential metabolites were quantified by quantitative NMR.

RESULTS

PCA and OPLS-DA revealed 37 metabolites including 16 discriminant compounds for transgenic samples. Ethanol (0.4 mg g-1 ), the main differential compound, was exclusively detected in transgenic tobacco; however, high levels of formate (0.28 mg g-1 ) and acetate (0.3 mg g-1 ) were simultaneously observed in the same group of samples. Cembratriene-4,6-diol, an antitumour and neuroprotective compound, and capsidiol, a known phytoalexin, increased by about 30% in transgenic samples. In addition, the endogenous levels of the antioxidant caffeoylquinic acid isomers increased by 50% in comparison to those of wild-type tobaccos.

CONCLUSION

Our results support the occurrence of metabolic differences between wild type and transgenic tobacco containing a promoter of a Capsicum sesquiterpene cyclase gene. Interestingly, the recombinant transgenic strain studied accumulated high amounts of added value compounds with biological activity.

The Bovine Metabolome

From an animal health perspective, relatively little is known about the typical or healthy ranges of concentrations for many metabolites in bovine biofluids and tissues. Here, we describe the results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including serum, ruminal fluid, liver, Longissimus thoracis (LT) muscle, semimembranosus (SM) muscle, and testis tissues. Using nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–tandem mass spectrometry (LC–MS/MS), and inductively coupled plasma–mass spectrometry (ICP–MS), we were able to identify and quantify more than 145 metabolites in each of these biofluids/tissues. Combining these results with previous work done by our team on other bovine biofluids, as well as previously published literature values for other bovine tissues and biofluids, we were able to generate quantitative reference concentration data for 2100 unique metabolites across five different bovine biofluids and seven different tissues. These experimental data were combined with computer-aided, genome-scale metabolite inference techniques to add another 48,628 unique metabolites that are biochemically expected to be in bovine tissues or biofluids. Altogether, 51,801 unique metabolites were identified in this study. Detailed information on these 51,801 unique metabolites has been placed in a publicly available database called the Bovine Metabolome Database.

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)-mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality.

1H-NMR based metabolomics reveals the nutrient differences of two kinds of freshwater fish soups before and after simulated gastrointestinal digestion

Soups show diverse health functions, which could be linked to their original nutrient profiles and metabolites derived from digestion. NMR spectroscopy is a robust and rapid method that unveils or identifies the chemical composition of food or food-derived metabolites. In the current study, the 1H-NMR spectroscopy approach was applied to identify the differences in metabolic profiling of two kinds of home-cooked freshwater fish soups (crucian carp and snakehead fish) before and after in vitro gastrointestinal digestion. The nutritional profiles of these soups were studied using the 1H-NMR method for the first time. Two metabolomics methods, PCA (Principal Component Analysis) and OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis), were used to analyze the data. On the whole, levels of amino acid metabolites such as valine (Val), tyrosine, choline, taurine (Tau) and glycine were higher in the crucian carp soup, whereas higher levels of fatty acids and unsaturated fatty acids were found in the snakehead soup. Furthermore, the high content of seven metabolites valine, leucine, EPA C20:5 (PUFA eicosapentaenoic acid), acetic acid, taurine, GPCho (phosphatidylcholine) and creatine showed an upward trend after simulated gastrointestinal digestion. The results demonstrate that the 1H-NMR metabolic profile of different fish soups can shed some light on our understanding of food functional properties and dietary therapy. Furthermore, changes of metabolites in digested fish soups could reveal information about chemical compounds which play important roles in the body.

An Untargeted Metabolomics Investigation of Jiulong Yak (Bos grunniens) Meat by 1H-NMR

Yak represents the main meat source for Tibetan people. This work aimed to investigate the metabolome of raw meat from Jiulong yaks, focusing on specimens farmed and harvested locally through traditional procedures. Untargeted nuclear magnetic resonance spectroscopy (¹H-NMR) was selected as the analytical platform. Samples from longissimus thoracis, trapezius, triceps brachii and biceps femoris muscles, with different prevalences of red and white fibers, were selected. Among the fifty-three metabolites quantified in each of them, carnitine, carnosine, creatine and taurine are known for their bioactive properties. Twelve molecules were found to be differently concentrated in relation to muscle type. Longissimus thoracis, compared to biceps femoris, had higher concentrations of carnosine and formate and lower concentrations of mannose, inosine, threonine, IMP, alanine, valine, isoleucine, tyrosine, phenylalanine and leucine. A metabolic pathway analysis suggested that the main pathways differing among the muscles were connected to the turnover of amino acids. These results contribute to a deeper understanding of yak raw meat metabolism and muscle type differences, which can be used as an initial reference for the meat industry to set up muscle-specific investigations. The possibility of simultaneously quantifying several bioactive compounds suggests that these investigations could revolve around meat’s nutritional value.

Gas Chromatography-Mass Spectrometry-Based Metabolomic Analysis of Wagyu and Holstein Beef

Japanese Black cattle (Wagyu) beef is characterized by high intramuscular fat content and has a characteristic sweet taste. However, the chemical components for characterizing the sweet taste of Wagyu beef have been unclear. In this experiment, we conducted a metabolomic analysis of the longissimus muscle (sirloin) in Wagyu and Holstein cattle to determine the key components associated with beef taste using gas chromatography–mass spectrometry (GC-MS). Holstein sirloin beef was characterized by the abundance of components such as glutamine, ribose-5-phosphate, uric acid, inosine monophosphate, 5-oxoproline, and glycine. In contrast, Wagyu sirloin beef was characterized by the abundance of sugar components (maltose and xylitol). Dietary fat is known to increase the intensity of sweet taste. These results suggest that the sweet taste of Wagyu beef is due to the synergetic effects of higher sugar components and intramuscular fat.

Discrimination of Cultivated Regions of Soybeans (Glycine max) Based on Multivariate Data Analysis of Volatile Metabolite Profiles

Soybean (Glycine max) is a major crop cultivated in various regions and consumed globally. The formation of volatile compounds in soybeans is influenced by the cultivar as well as environmental factors, such as the climate and soil in the cultivation areas. This study used gas chromatography-mass spectrometry (GC-MS) combined by headspace solid-phase microextraction (HS-SPME) to analyze the volatile compounds of soybeans cultivated in Korea, China, and North America. The multivariate data analysis of partial least square-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were then applied to GC-MS data sets. The soybeans could be clearly discriminated according to their geographical origins on the PLS-DA score plot. In particular, 25 volatile compounds, including terpenes (limonene, myrcene), esters (ethyl hexanoate, butyl butanoate, butyl prop-2-enoate, butyl acetate, butyl propanoate), aldehydes (nonanal, heptanal, (E)-hex-2-enal, (E)-hept-2-enal, acetaldehyde) were main contributors to the discrimination of soybeans cultivated in China from those cultivated in other regions in the PLS-DA score plot. On the other hand, 15 volatile compounds, such as 2-ethylhexan-1-ol, 2,5-dimethylhexan-2-ol, octanal, and heptanal, were related to Korean soybeans located on the negative PLS 2 axis, whereas 12 volatile compounds, such as oct-1-en-3-ol, heptan-4-ol, butyl butanoate, and butyl acetate, were responsible for North American soybeans. However, the multivariate statistical analysis (PLS-DA) was not able to clearly distinguish soybeans cultivated in Korea, except for those from the Gyeonggi and Kyeongsangbuk provinces.

Untargeted and Targeted Discrimination of Honey Collected by Apis cerana and Apis mellifera Based on Volatiles Using HS-GC-IMS and HS-SPME-GC-MS

Fraudulent acts regarding honey authenticity that use Apis mellifera honey as a substitute for Apis cerana honey have garnered considerable concern in China and triggered a trust crisis from consumers. In this study, untargeted metabolomics analysis was carried out based on volatile fractions in honey from A. cerana and A. mellifera using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). Honey from A. cerana and A. mellifera was discriminated by HS-GC-IMS profiling, principal component analysis, and orthogonal partial least-squares discrimination analysis. Tentative markers were identified from p-values and the variable importance in projection analysis and confirmed using the retention index, mass fragments, and reference standards by gas chromatography-mass spectrometry (GC-MS). A targeted method was established using the headspace solid phase coupled with microextraction GC-MS (HS-SPME-GC-MS) to quantitate the markers. The results demonstrated that the developed untargeted and targeted metabolomics approach performed well when discriminating honey from A. cerana and A. mellifera.

NMR-based metabolic profiling and comparison of Japanese persimmon cultivars

Persimmons are a traditional, autumnal, and healthy fruit commonly consumed in Japan and East Asia based on the saying, "a persimmon a day keeps the doctor away." The differences in metabolites among five major Japanese persimmon cultivars were investigated using a nuclear magnetic resonance (NMR)-based metabolomics approach. By using a broadband water suppression enhanced through T₁ effects (WET) method for the sensitive detection of minor metabolites, better discrimination among cultivars and more informative details regarding their metabolic differences have been achieved compared to those achieved in conventional ¹H NMR sequences. Among the nonastringent cultivars analyzed, the Taishu cultivar has the highest abundance of amino acids. The Matsumotowase-Fuyu cultivar contains ethyl-β-glycosides as characteristic components, which may relate to fruit softening. Citric acid concentration is higher in Maekawa Jiro than in other nonastringent cultivars. Among the two astringent cultivars analyzed, ethanol was significantly higher in Hiratanenashi than in Yotsumizo, which indicates different reactivity during deastringency treatments. The present study proposes an efficient and relatively quantitative metabolomics approach based on broadband WET NMR spectra.

Age-specific urinary metabolite signatures and functions in patients with major depressive disorder

Major depressive disorder (MDD) patients in different age ranges might have different urinary metabolic phenotypes, because age could significantly affect the physiological and psychological status of person. Therefore, it was very important to take age into consideration when studying MDD. Here, a dual platform metabolomic approach was performed to profile urine samples from young and middle-aged MDD patients. In total, 18 and 15 differential metabolites that separately discriminated young and middle-aged MDD patients, respectively, from their respective HC were identified. Only ten metabolites were significantly disturbed in both young and middle-aged MDD patients. Meanwhile, two different biomarker panels for diagnosing young and middle-aged MDD patients, respectively, were identified. Additionally, the TCA cycle was significantly affected in both young and middle-aged MDD patients, but the Glyoxylate and dicarboxylate metabolism and phenylalanine metabolism were only significantly affected in young and middle-aged MDD patients, respectively. Our results would be helpful for developing age-specific diagnostic method for MDD and further investigating the pathogenesis of this disease.

Potential geo-discriminative tools to trace the origins of the dried slices of shiitake (Lentinula edodes) using stable isotope ratios and OPLS-DA

The Lentinus edodes species, known as shiitake, has a history of extensive use in many cuisines in several East Asian countries owing to its unique and pleasant flavor and texture. As international food trade increases, reliable discrimination of geographical origin is becoming increasingly crucial in Korea to identify cheaper imported Chinese shiitake. Herein, stable isotope ratios (i.e., δ¹³C, δ¹⁵N, δ¹⁸O, and δ³⁴S) were measured with a stable isotope ratio mass spectrometer, and a geographical discrimination method using orthogonal projection to latent structure-discriminant analysis was developed. The externally validated discrimination method showed excellent predictability (Q²_cum = 0.881) and illustrated that δ¹⁸O and δ¹⁵N were important isotope markers for the geographical discrimination of dried shiitake slices. This study extends the knowledge of geographical differences between China and Korea evidenced by the shiitake isotope signatures, thereby contributing to potential geographical authentication with broader applications for international shiitake markets.

Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree

Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.

1H NMR-based fingerprinting of eleven Mexican Capsicum annuum cultivars

Approximately 90% of the chili peppers consumed in the world are harvested in Mexico. The present article describes the untargeted ¹H NMR-based metabolomic profiling of 11 cultivars of Capsicum annuum species which are routinely consumed worldwide. The metabolomic fingerprinting detected via ¹H NMR contained 44 metabolites including sugars, amino acids, organic acids, polyphenolic acids and alcohols which were identified by comparison with the literature data, with Chenomx database and by 2D NMR. Statistical approaches based on principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the Capsicum annuum cultivars according to their metabolite profile. LDA revealed metabolomic differences and similarities among Capsicum annuum cultivars, whereas hierarchical cluster analysis (HCA) significantly separated the cultivars according to the phylogenetic trees obtained. Substantial endogenous levels of free amino acids and carbohydrates were detected in all the studied cultivars but interestingly, Capsicum annuum cv. mirasol and C. annuum cv. chilaca contained almost three-fold more endogenous levels of vitamin C than the other cultivars. Considering that this antioxidant was found in crude aqueous extracts, its abundance could be directly proportional to its bioavailability for human nutrition. The results suggest that ¹H NMR is an effective method to determine differences among cultivars of the Capsicum annuum species.

Optimization of 1D 1H Quantitative NMR (Nuclear Magnetic Resonance) Conditions for Polar Metabolites in Meat

The objective of this study was to establish an optimized 1D ¹H quantitative nuclear magnetic resonance (qNMR) analytical method for analyzing polar metabolites in meat. Three extraction solutions [0.6 M perchloric acid, 10 mM phosphate buffer, water/methanol (1:1)], three reconstitution buffers [20 mM 3-morpholinopropane-1-sulfonic acid, 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid, phosphate buffer], and two pulse programs (zg30, noesypr1d) were evaluated. Extraction with 0.6 M perchloric acid and 20 mM phosphate resulted in a stable baseline and no additional overlap for quantifying polar metabolites in chicken breast. In qNMR analysis, zg30 pulse program (without water-suppression) showed smaller relative standard deviation (RSD) and faster running time than noesypr1d (water-suppression). High-performance liquid chromatography was compared with qNMR analyses to validate accuracy. The zg30 pulse program showed good accuracy and lower RSD. The optimized qNMR method was able to apply for beef and pork samples. Thus, an optimized 1D ¹H qNMR method for meat metabolomics was established.

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 Spectroscopy for Determination of the Geographical Origin of Hazelnuts

A total of 262 authentic samples was analyzed by ¹H NMR spectroscopy for the geographical discrimination of hazelnuts ( Corylus avellana L.) covering samples from five countries (Germany, France, Georgia, Italy, and Turkey) and the harvest years 2013-2016. This article describes method development starting with an extraction protocol suitable for separation of polar and nonpolar metabolites in addition to reduction of macromolecular components. Using the polar fraction for data analysis, principle component analysis was applied and used to monitor sample preparation and measurement. Several machine learning algorithms were tested to build a classification model. The best results were obtained by a linear discrimination analysis applying a random subspace algorithm. The division of the samples in a trainings set and a test set yielded a cross validation accuracy of 91% for the training set and an accuracy of 96% for the test set. The identification of key features was carried out by Kruskal-Wallis test and t test. A feature assigned to betaine exhibits a significant level for the classification of all five countries and is considered a possible candidate for the development of targeted approaches. Further, the results were compared to a previously published study based on LC-MS analysis of nonpolar metabolites. In summary, this study shows the robustness and high accuracy of a discrimination model based on NMR analysis of polar metabolites.

Skeletal muscle metabolomics and blood biochemistry analysis reveal metabolic changes associated with dietary amino acid supplementation in dairy calves

The effects of different amino acid (AA) supplementations of milk protein-based milk replacers in pre-ruminant calves from 3 days to 7 weeks of age were studied. Animals were divided into 4 groups: Ctrl) Control group fed with milk protein-based milk replacer without supplementation; GP) supplementation with 0.1% glycine and 0.3% proline; FY) supplementation with 0.2% phenylalanine and 0.2% tyrosine; MKT) supplementation with 0.62% lysine, 0.22% methionine and 0.61% threonine. For statistical analysis, t-test was used to compare AA-supplemented animals to the Ctrl group. At week 7, body weight and average daily gain (ADG) were measured and blood samples and skeletal muscle biopsies were taken. Blood biochemistry analytes related to energy metabolism were determined and it was shown that MKT group had higher serum creatinine and higher plasma concentration of three supplemented AAs as well as arginine compared with the Ctrl group. GP group had similar glycine/proline plasma concentration compared with the other groups while in FY group only plasma phenylalanine concentration was higher compared with Control. Although the AA supplementations in the GP and FY groups did not affect average daily gain and metabolic health profile from serum, the metabolome analysis from skeletal muscle biopsy revealed several differences between the GP-FY groups and the Ctrl-MKT groups, suggesting a metabolic adaptation especially in GP and FY groups.

1H-NMR metabolomics analysis of nutritional components from two kinds of freshwater fish brain extracts

There are a number of bioactive compounds in freshwater fish brains, and their functional roles have not been clearly elucidated. NMR-based metabolic profiling could enable rapid characterization of the nutritional composition a fish's brain. Here, two kinds of freshwater fish brains were investigated, crucian carp and yellow catfish. A ¹H-NMR based metabolomic approach was used to illustrate the nutritional components of these two kinds of brain. At first, the microwave method was utilized to cease the activity of the enzymes in the brain, and the chemicals were extracted for NMR analysis. These two kinds of brain had significant differences in metabolic patterns, and the chemical compositions of the yellow catfish brain were similar to those of rodent and human brains. Furthermore, most of the different metabolites were significantly higher in the yellow catfish, except for acetamide. This study could provide comprehensive information regarding the utilization of fish heads during processing of fish and dietary nutrition guidance.

There are a number of bioactive compounds in freshwater fish brains, and their functional roles have not been clearly elucidated. NMR-based metabolic profiling could provide a rapid characterization of a fish brain's nutritional composition.