Geographical characterization of polyfloral and acacia honeys by nuclear magnetic resonance and chemometrics

A comprehensive review of the current trends and recent advancements on the authenticity of honey

The authenticity of honey currently poses challenges to food quality control, thus requiring continuous modernization and improvement of related analytical methodologies. This review provides a comprehensively overview of honey authenticity challenges and related analytical methods. Firstly, direct and indirect methods of honey adulteration were described in detail, commenting the existing challenges in current detection methods and market supervision approaches. As an important part, the integrated metabolomic workflow involving sample processing procedures, instrumental analysis techniques, and chemometric tools in honey authenticity studies were discussed, with a focus on their advantages, disadvantages, and scopes. Among them, various improved microscale extraction methods, combined with hyphenated instrumental analysis techniques and chemometric data processing tools, have broad application potential in honey authenticity research. The future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

Spatially Correlated Nuclear Magnetic Resonance Profiles as a Tool for Precision Agriculture

Nuclear magnetic resonance (NMR) profiling, sample georeferentiaton, and geostatistics are applied to evaluate the spatial variability of metabolic expression of durum wheat in fields managed by precision agriculture. Durum wheat at three different vegetation stages, grown in two different places of the Basilicata region, in Italy, is analyzed by NMR. The spatial variability, within each field, of metabolites, quantified by NMR, is evidenced by appropriate geostatistic tools through the definition of a suitable metabolic index. Metabolic maps are compared to highlight the effects of soil and farming strategies.

Honey compositional convergence and the parallel domestication of social bees

Honey collection evolved from simple honey hunting to the parallel and independent domestication of different species of bees in various parts of the world. In this study, we investigate the extent to which the composition of Apis and stingless bee honeys has been a driver in the selection of different bee species for domestication in Mesoamerica (Mexico) and Asia (Thailand) using a sampling design that combines peak honey profiling by H1 NMR spectroscopy with the collection of honeys from domesticated and undomesticated bee species. Our results show that, independently of the region of the world considered, domesticated stingless bees produce honey whose compositional profiles differ from those of the non-domesticated species and exhibit more similarities towards honeys produced by the domesticated Apis species. Our results provide evidence for the first time that the search for natural sweeteners in the environment by our ancestors led to the parallel and independent domestication of social bees producing honeys with similar compositional profiles.

¹H-NMR Metabolic Profiling, Antioxidant Activity, and Docking Study of Common Medicinal Plant-Derived Honey

The purpose of this investigation was to determine ¹H-NMR profiling and antioxidant activity of the most common types of honey, namely, citrus honey (HC1) (Morcott tangerine L. and Jaffa orange L.), marjoram honey (HM1) (Origanum majorana L.), and clover honey (HT1) (Trifolium alexandrinum L.), compared to their secondary metabolites (HC2, HM2, HT2, respectively). By using a ¹H-NMR-based metabolomic technique, PCA, and PLS-DA multivariate analysis, we found that HC2, HM2, HC1, and HM1 were clustered together. However, HT1 and HT2 were quite far from these and each other. This indicated that HC1, HM1, HC2, and HM2 have similar chemical compositions, while HT1 and HT2 were unique in their chemical profiles. Antioxidation potentials were determined colorimetrically for scavenging activities against DPPH, ABTS, ORAC, 5-LOX, and metal chelating activity in all honey extract samples and their secondary metabolites. Our results revealed that HC2 and HM2 possessed more antioxidant activities than HT2 in vitro. HC2 demonstrated the highest antioxidant effect in all assays, followed by HM2 (DPPH assay: IC₅₀ 2.91, 10.7 μg/mL; ABTS assay: 431.2, 210.24 at 50 ug/mL Trolox equivalent; ORAC assay: 259.5, 234.8 at 50 ug/mL Trolox equivalent; 5-LOX screening assay/IC₅₀: 2.293, 6.136 ug/mL; and metal chelating activity at 50 ug/mL: 73.34526%, 63.75881% inhibition). We suggest that the presence of some secondary metabolites in HC and HM, such as hesperetin, linalool, and caffeic acid, increased the antioxidant activity in citrus and marjoram compared to clover honey.

You Are What You Eat: Application of Metabolomics Approaches to Advance Nutrition Research

A healthy condition is defined by complex human metabolic pathways that only function properly when fully satisfied by nutritional inputs. Poor nutritional intakes are associated with a number of metabolic diseases, such as diabetes, obesity, atherosclerosis, hypertension, and osteoporosis. In recent years, nutrition science has undergone an extraordinary transformation driven by the development of innovative software and analytical platforms. However, the complexity and variety of the chemical components present in different food types, and the diversity of interactions in the biochemical networks and biological systems, makes nutrition research a complicated field. Metabolomics science is an "-omic", joining proteomics, transcriptomics, and genomics in affording a global understanding of biological systems. In this review, we present the main metabolomics approaches, and highlight the applications and the potential for metabolomics approaches in advancing nutritional food research.

Recent advances in analytical techniques for the detection of adulteration and authenticity of bee products - A review

Bee products have been considered as functional foods for a long time in China because of their wide range of biological activity. China has the largest number of bee colonies and the highest production of bee products in the world. Major bee products include honey, royal jelly, propolis and bee pollen. In recent years, consumption of bee products in China has been increasing due to an increased public awareness of their nutritional and health benefits. With the development of the Chinese economy and the improvement of people's living standards, high-end and gift-oriented products have become more popular and bee products are one of the options. However, the production of bee products cannot increase rapidly in short term and this is a driver for substantial economic-motivated adulteration. This is compounded by globalisation of supply chains which has also resulted in a rise in bee products fraud. These illicit products are eroding market prices and consumer trust, causing significant damage to the beekeeping industry. In order to provide information or solutions for regulators and consumers, in this article, we review he characteristics of bee products in China and the current situation regarding adulteration and authenticity of bee products. Moreover, advances in analytical techniques for detection of adulteration and authenticity of bee products including sensory techniques, DNA methods, isotope ratio mass spectrometry, spectroscopic techniques and mass spectrometry are reviewed. Finally, the applications and limitations of analytical methods in authentication are critically assessed. Suggestions are also put forward for the future management of China's bee products industry.

Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics

With the increase in soybean trade between countries, the intentional mislabeling of the origin of soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%–100% specificity, and 94.4%–100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.

Classification of the botanical and geographical origins of Chinese honey based on 1H NMR profile with chemometrics

In this paper, we report a newly developed non-target ¹H NMR detection associated with chemometrics method to classify the botanical and geographical origins of the monofloral Chinese honey. ¹H NMR tests of 218 monofloral honey samples of 8 classes (Acacia, Jujube, Linden, Longan, Orange, Rape, Sunflower, Vitex) collected in 2017-2019 across China were conducted under the optimal sample preparation conditions and NMR acquisition parameters. The whole profiles of NMR spectra instead of individual or partial signals from specific components were processed and extracted, then fed to SIMCA-P to classify the botanical and geographical origins through non-target statistical analysis. For the botanical origins, most of them could be classified clearly according to Principal Component Analysis (PCA) with both R² and Q² close to 1. Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) model could classify the honey floral types successfully with R²Y and Q² greater than 0.85. It is found that the integral bin for data extraction has no obvious influence on the classification. For the geographical origins, the classification at different geographical levels (providence and town) could be successfully distinguished by OPLS-DA model. The promising preliminary results with the geographical classification at 40 km level unambiguously demonstrate the application of this NMR-based multi-species non-targeted method for the honey authenticity. Successful result is obtained on a pilot prediction of the geographical classification. Comparing with the methods based on other techniques, the advantages of this reported one are less sample amount needed, simple preparation, short test time, and non-targeted multi-species detection.

Compositional identification and authentication of Chinese honeys by 1H NMR combined with multivariate analysis

Honey authentication has been becoming more and more important and necessary to the honey producers, the consumers and the market regulatory authority due to its favorite organoleptic and healthy properties, high value and increasing export but prevalent falsification practice for economic motivation in China and the potential health risk of adulterated honey. In this study, we obtained the spectral profiles of 90 authentic and 75 adulterated Chinese honey samples by means of high resolution nuclear magnetic resonance (NMR) spectroscopy, and 65 kinds of major and minor components in honey were identified and quantified from their NMR spectra. Combining with the multivariate statistical analyses including principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squared-discriminant analysis (OPLS-DA), the discrimination models were successfully established to identify the adulterated honeys from the authentic ones with an accurate rate of 97.6%. Furthermore, the corresponding volcano plot was used to screen out 8 components including proline, xylobiose, uridine, β-glucose, melezitose, turanose, lysine and an unknown component, which are responsible for the differentiation between the authentic and adulterated honeys and will help to control Chinese domestic honey market.

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.

Classification of Raw Stingless Bee Honeys by Bee Species Origins Using the NMR- and LC-MS-Based Metabolomics Approach

The official standard for quality control of honey is currently based on physicochemical properties. However, this method is time-consuming, cost intensive, and does not lead to information on the originality of honey. This study aims to classify raw stingless bee honeys by bee species origins as a potential classifier using the NMR-LCMS-based metabolomics approach. Raw stingless bee honeys were analysed and classified by bee species origins using proton nuclear magnetic resonance (¹H-NMR) spectroscopy and an ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-QTOF MS) in combination with chemometrics tools. The honey samples were able to be classified into three different groups based on the bee species origins of Heterotrigona itama, Geniotrigona thoracica, and Tetrigona apicalis. d-Fructofuranose (H. itama honey), β-d-Glucose, d-Xylose, α-d-Glucose (G. thoracica honey), and l-Lactic acid, Acetic acid, l-Alanine (T. apicalis honey) ident d-Fructofuranose identified via ¹H-NMR data and the diagnostic ions of UHPLC-QTOF MS were characterized as the discriminant metabolites or putative chemical markers. It could be suggested that the quality of honey in terms of originality and purity can be rapidly determined using the classification technique by bee species origins via the ¹H-NMR- and UHPLC-QTOF MS-based metabolomics approach.

Hydrophobic Amino Acid Content in Onions as Potential Fingerprints of Geographical Origin: The Case of Rossa da Inverno sel. Rojo Duro

In this study, we were interested in comparing the amino acid profile in a specific variety of onion, Rossa da inverno sel. Rojo Duro, produced in two different Italian sites: the Cannara (Umbria region) and Imola (Emilia Romagna region) sites. Onions were cultivated in a comparable manner, mostly in terms of the mineral fertilization, seeding, and harvesting stages, as well as good weed control. Furthermore, in both regions, the plants were irrigated by the water sprinkler method and subjected to similar temperature and weather conditions. A further group of Cannara onions that were grown by micro-irrigation was also evaluated. After the extraction of the free amino acid mixture, an ion-pairing reversed-phase high performance liquid chromatography-evaporative light scattering detector (IP-RP HPLC-ELSD) method allowed for the separation and detection of almost all the standard proteinogenic amino acids. However, only the peaks corresponding to leucine (Leu), phenylalanine (Phe), and tryptophan (Trp), were present in all the investigated samples and they were unaffected from the matrix interfering peaks. The use of the beeswarm/box plots revealed that the content of Leu and Phe were markedly influenced by the geographical origin of the onions (with *** p << 0.001 for Phe), but not by the irrigation procedure. The applied HPLC method was validated in terms of the specificity, the linearity (a logarithm transformation was applied for the method linearization), the limit of detection (LOD) and limit of quantification (LOQ), the accuracy (≥90% for inter-day Recovery percentage), and the precision (≤10.51 for the inter-day RSD percentage), before the quantitative assay of Leu, Phe, and Trp in the onion samples. These preliminary findings are a good starting point for considering the quantity of the specific amino acids in the Rossa da inverno sel. Rojo Duro variety as a fingerprint of its geographical origin.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

Proton NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work, we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a geographic information system (GIS). Nuclear magnetic resonance spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work, we describe the methods and the algorithms that permit to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by ¹ H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built an LDA model that provides a classification ability up to 99% . A comparison between the variables selected in the geostatistics and classification steps is finally performed. Copyright © 2016 John Wiley & Sons, Ltd.

Objective Definition of Monofloral and Polyfloral Honeys Based on NMR Metabolomic Profiling

In this paper, a remarkably precise, simple, and objective definition of monofloral and polyfloral honey based on NMR metabolomics is proposed. The spectra of organic extracts of 983 samples of 16 botanical origins were used to derive one-versus-all OPLS-DA classification models. The predictive components of the statistical models reveal not only the principal but also the secondary floral origins present in a sample of honey, a novel feature with respect to the methods present in the literature that are able to confirm the authenticity of monofloral honeys but not to characterize a mixture of honey types. This result descends from the peculiar features of the chloroform spectra that show diagnostic resonances for almost each botanical origin, making these NMR spectra suitable fingerprints. The reliability of the method was tested with an additional 120 samples, and the class assignments were compared with those obtained by traditional analysis. The two approaches are in excellent agreement in identifying the floral species present in honeys and in the botanical classification. Therefore, this NMR method may prove to be a valid solution to the huge limitations of traditional classification, which is very demanding and complex.

Metabolic profiling by (1)H NMR of ground beef irradiated at different irradiation doses

This work describes a metabolic profiling study of non-irradiated and irradiated beef (at 2.5, 4.5 and 8 kGy) using (1)H NMR and chemometrics. The assignment of all major NMR signals of the aqueous/methanolic extracts was performed. A comprehensive multivariate data analysis proved the ability to distinguish between the irradiated and non-irradiated beef. Classification trees revealed that three metabolites (glycerol, lactic acid esters and tyramine or a p-substituted phenolic compound) are important biomarkers for classification of the irradiated and non-irradiated beef samples. Overall, the achieved metabolomic results show that the changes in the metabolic profile of meat provide a valuable insight to be used in detecting irradiated beef. The use of the NMR-based approach simplifies sample preparation and decrease the time required for analysis, compared to available official analytical procedures.

1H NMR profiling as an approach to differentiate conventionally and organically grown tomatoes

This study describes the approach of (1)H NMR profiling for the authentication of organically produced tomatoes (Solanum lycopersicum). Overall, 361 tomato samples of two different cultivars and four different producers were regularly analyzed during a 7 month period. The results of principal component analysis showed a significant trend for the separation between organically and conventionally produced tomatoes (p < 0.001 using the t test). Linear discriminant analysis demonstrated good discrimination between the growing regimens, and external validation showed 100% correctly classified tomato samples. Further validation studies, however, also disclosed unexpected differences between individual producers, which interfere with the aim of predicting the cultivation method, yet the results indicate significant differences between (1)H NMR spectra of organically and conventionally grown tomatoes.

Qualitative and Quantitative Control of Honeys Using NMR Spectroscopy and Chemometrics

400 MHz nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis techniques were used in the context of food surveillance to measure 328 honey samples with 1H and 13C NMR. Using principal component analysis (PCA), clusters of honeys from the same botanical origin were observed. The chemical shifts of the principal monosaccharides (glucose and fructose) were found to be mostly responsible for this differentiation. Furthermore, soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) could be used to automatically classify spectra according to their botanical origin with 95–100% accuracy. Direct quantification of 13 compounds (carbohydrates, aldehydes, aliphatic and aromatic acids) was additionally possible using external calibration curves and applying TSP as internal standard. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of botanical origin and quantification of selected constituents of honeys.

PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study

BACKGROUND

Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research.

RESULTS

Here we proposed a metabolomic approach based on the combination of (1)H high-resolution magic angle spinning-nuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin.

CONCLUSION

Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin.

The application of high resolution diffusion NMR to the analysis of manuka honey

The application of DOSY (Diffusion Ordered SpectroscopY) NMR as a technique for the virtual separation of key components of manuka honey and the implications for future discriminatory analysis of honey types is reported for the first time. The scope and the limitations of DOSY NMR are considered using the recently conceived DOSY Tool Box processing software and preliminary anti-bacterial data for the different honey types is reported.

NMR characterization of saccharides in Italian honeys of different floral sources

The saccharide profiles of 5 different botanical species in 86 Italian honey samples were investigated by ¹H and ¹H-¹³C NMR spectroscopy. Nineteen saccharides were identified in the aqueous extracts, namely, fructose, glucose, gentiobiose, isomaltose, kojibiose, maltose, maltulose, melibiose, nigerose, palatinose, sucrose, turanose, erlose, isomaltotriose, kestose, maltotriose, melezitose, raffinose, and maltotetraose. PCA performed on NMR spectral regions, in particular between 4.400 and 5.700 ppm and the fructose signal at 4.050 ppm, revealed a partial sample grouping. The score contribution plots derived from PCA performed using the mean values for the buckets of the anomeric region for each floral source allowed the identification of saccharides characterizing different honeys. OPLS-DA models were further evaluated to confirm the previous findings. OPLS-DA models were also built to highlight differences between polyfloral and high mountain polyfloral honeys and between high mountain polyfloral and rhododendron honeys, both collected at high altitude; S-plots highlighted the characteristic saccharides.

Identification of milk mixtures by 1H NMR profiling

Nuclear magnetic resonance profiling, combined with a single-layer artificial neural network, is used for the evaluation of the content of mixtures of different kinds of milk. In particular, aqueous fractions of cow and sheep milk mixtures are analyzed by (1) H NMR. The spectral differences are highlighted by an analysis of the variance and a principal component analysis. The species classification problem is solved by a linear discriminant analysis. The quantification of the relative amount of the milk of two different species is then achieved by solving the appropriate multilinear problem.

NMR based geographical characterization of roasted coffee

The increasing attention for food quality and safety led to develop several analytical techniques suitable to address these demands. Coffee has been already demonstrated to represent a worldwide appreciated beverage and its high economical value could induce frauds or adulteration practices involving both compositional and geographical aspects. In the last years, metabolic profiling revealed to be suitable to face the quality determination of food matrices and NMR confirmed its potentiality in metabolites characterization. The present study reports the capability of NMR spectroscopy to investigate the metabolite content of roasted Coffea arabica samples from the three main production areas, America, Africa and Asia. OPLS-DA models performed on (1)H NMR data led to a clear separation of samples according to their origin: fatty acids, chlorogenic acids and lactate and finally acetate and trigonelline resulted the main compounds characterizing the American, African and Asian samples respectively. The analytical approach here presented confirmed the potentiality of the joined NMR analysis and statistical treatments in quality determination of food matrices.

Nuclear magnetic resonance profiling of wine blends

Nuclear magnetic resonance (NMR) profiling is used for characterization of monocultivar binary wine mixtures. Classification and quantification of the relative amount of wine in the mixture are made in two steps. First, each sample is classified as a mixture of a determined type by solving the appropriate classification problem using NMR profiles. The relative amount of the two corresponding monovarietal wines is then evaluated by multilinear regression of a selected set of NMR variables. Linear discriminant analysis (LDA), used in the classification step, gives a very good separation among the different mixture classes. On the other hand, a single layer artificial neural network, used to solve the multilinear problem, gives the relative amount of wine type in the mixture with a precision of about 10%.

Evaluation of the effect of a maternal rearing system on the odour profile of meat from suckling lamb

The aim was to evaluate the effect of maternal rearing system on the chemical composition and odour profile of meat from suckling lamb. Twenty-four single male suckling lambs raised exclusively on maternal milk came from two rearing systems (extensive and intensive), and from Castellana and Churra breeds. The fatty acid composition of lamb meat from both breeds was affected by the rearing system, and Churra lamb reared under the extensive system had noticeable changes in odour attributes as detected by panellists and an electronic nose. Using partial least squares-discriminant analysis, the electronic nose was able to discriminate accurately between samples of Churra and Castellana meat according to their production system. However, it was not able to correctly classify meat samples from the extensive and intensive systems when both breeds were analysed together. The results suggest a significant effect of the rearing system and breed on suckling lamb meat quality traits.

Geographical origin of durum wheat studied by 1H-NMR profiling

(1)H-Nuclear magnetic resonance (NMR) spectroscopy was used to detect metabolic profiles of wheat flour samples of different geographical and botanical origin. The NMR profiles were analyzed by multivariate statistical techniques in order to establish the origin of the samples. A linear model, able to discriminate among three different locations, was built achieving a prediction level of about 80% of correctly assigned samples. The principal classes of compounds responsible for the geographic origin discrimination were individuated in aromatic compounds and amino acids. The statistical modeling also indicated that botanical origin information is very poor in the NMR profiles of the analyzed wheat samples.

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

The geographical origin of beef is of increasing interest to consumers and producers due to "mad cow" disease and the implementation of the Free Trade Agreement (FTA). In this study, (1)H NMR spectroscopy coupled with multivariate statistical analyses was used to differentiate the geographical origin of beef samples. Principal component analysis (PCA) and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) showed significant separation between extracts of beef originating from four countries: Australia, Korea, New Zealand, and the United States. The major metabolites responsible for differentiation in OPLS-DA loading plots were succinate and various amino acids including isoleucine, leucine, methionine, tyrosine, and valine. A one-way ANOVA was performed to statistically certify the difference in metabolite levels. The data suggest that NMR-based metabolomics is an efficient method to distinguish fingerprinting difference between raw beef samples, and several metabolites including various amino acids and succinate can be possible biomarkers for discriminating the geographical origin of beef.