Food Fingerprinting: Metabolomic Approaches for Geographical Origin Discrimination of Hazelnuts (Corylus avellana) by UPLC-QTOF-MS

Prospective exploration of hazelnut's unsaponifiable fraction for geographical and varietal authentication: A comparative study of advanced fingerprinting and untargeted profiling techniques

This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC-MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for "Tonda di Giffoni" vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models' regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.

Exploring the potential of high-resolution LC-MS in combination with ion mobility separation and surrogate minimal depth for enhanced almond origin authentication

Almonds (Prunus dulcisMill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis.

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

Corylus avellana "Nocciola Piemonte": metabolomics focused on polar lipids and phenolic compounds in fresh and roasted hazelnuts

The common hazel plant (Corylus avellana L., Betulaceae) is one of the most popular tree nuts widespread in Europe and Asia. In Italy, there are different cultivars among which the cultivar affording the valuable hazelnut "Tonda Gentile Trilobata," also known as "Tonda Gentile delle Langhe," covered by the Protected Geographical Indication (PGI) label "Nocciola Piemonte" (NP), known for its sweetness, cooked-bread aroma, and the low intensity of the burnt aroma. In order to obtain a detailed and in-depth characterization of the polar fraction of fresh (NPF) and roasted (NPR) kernels of NP the analysis of the n-butanol extracts by liquid chromatography coupled to electrospray ionization and high-resolution mass spectrometry (LC-ESI/HRMS) was carried out. Moreover, to evaluate the quantitative distribution of the most representative polar lipids in NPF and NPR, the analysis by liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) was performed. To unambiguously identify the phenolic compounds highlighted by the LC-ESI/HRMS profiles, they were isolated from the n-butanol extract and characterized by Nuclear Magnetic Resonance (NMR) experiments. Finally, the ability of the isolated compounds to exert radical scavenging activity and to inhibit the lipid peroxidation induced by H2O2 or H2O2/Fe2+ was tested by Trolox Equivalent Antioxidant Capacity (TEAC) and thiobarbituric acid reactive substances (TBARS) assays, respectively. The LC-ESI/HRMS allowed to ascertain the presence of phenolic compounds and multiple classes of polar lipids including phospholipids, glycolipids, sphingolipids, and oxylipins. The quantitative analysis highlighted in NPR fraction a lipid content three times higher than in NPF, evidencing lyso-phospholipids and phospholipids as the most represented lipid classes in both NPF and NPR, together accounting for 94 and 97% of the considered lipids, respectively. Furthermore, phytochemical analysis permitted to identify flavonoid and diarylheptanoid derivatives. In particular, quercetin 3-O-β-D-galactopyranosyl-(1→2)-β-D-glucopyranoside and myricetin-3-O-α-L-rhamnopyranoside showed the highest antioxidant activity, exhibiting TEAC values similar to that of quercetin, used as reference compound (2.00 ± 0.03 and 2.06 ± 0.03 mM vs 2.03 ± 0.03 mM, respectively). Moreover, most of the tested compounds were found to reduce lipid peroxidation induced by H2O2 and H2O2/Fe2+ more than curcumin used as positive control, with myricetin-3-O-α-L-rhamnopyranoside determining 44.4 % and 34.1 % inhibition percentage, respectively.

Food Monitoring: Limitations of Accelerated Storage to Predict Molecular Changes in Hazelnuts (Corylus avellana L.) under Realistic Conditions Using UPLC-ESI-IM-QTOF-MS

Accelerated storage is routinely used with pharmaceuticals to predict stability and degradation patterns over time. The aim of this is to assess the shelf life and quality under harsher conditions, providing crucial insights into their long-term stability and potential storage issues. This study explores the potential of transferring this approach to food matrices for shelf-life estimation. Therefore, hazelnuts were stored under accelerated short-term and realistic long-term conditions. Subsequently, they were analyzed with high resolution mass spectrometry, focusing on the lipid profile. LC-MS analysis has shown that many unique processes take place under accelerated conditions that do not occur or occur much more slowly under realistic conditions. This mainly involved the degradation of membrane lipids such as phospholipids, ceramides, and digalactosyldiacylglycerides, while oxidation processes occurred at different rates in both conditions. It can be concluded that a food matrix is far too complex and heterogeneous compared to pharmaceuticals, so that many more processes take place during accelerated storage, which is why the results cannot be used to predict molecular changes in hazelnuts stored under realistic conditions.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Comparative Analysis of LC-ESI-IM-qToF-MS and FT-NIR Spectroscopy Approaches for the Authentication of Organic and Conventional Eggs

The importance of animal welfare and the organic production of chicken eggs has increased in the European Union in recent years. Legal regulation for organic husbandry makes the production of organic chicken eggs more expensive compared to conventional husbandry and thus increases the risk of food fraud. Therefore, the aim of this study was to develop a non-targeted lipidomic LC-ESI-IM-qToF-MS method based on 270 egg samples, which achieved a classification accuracy of 96.3%. Subsequently, surrogate minimal depth (SMD) was applied to select important variables identified as carotenoids and lipids based on their MS/MS spectra. The LC-MS results were compared with FT-NIR spectroscopy analysis as a low-resolution screening method and achieved 80.0% accuracy. Here, SMD selected parts of the spectrum which are associated with lipids and proteins. Furthermore, we used SMD for low-level data fusion to analyze relations between the variables of the LC-MS and the FT-NIR spectroscopy datasets. Thereby, lipid-associated bands of the FT-NIR spectrum were related to the identified lipids from the LC-MS analysis, demonstrating that FT-NIR spectroscopy partially provides similar information about the lipidome. In future applications, eggs can therefore be analyzed with FT-NIR spectroscopy to identify conspicuous samples that can subsequently be counter-tested by mass spectrometry.

UPLC-MS/MS profiling, antioxidant and anti-inflammatory activities, and potential health benefits prediction of phenolic compounds in hazel leaf

Hazel leaf, one of the by-products of hazelnut, which is widely used in traditional folk medicine around the world. In the present study, the profile of free, conjugated, and bound phenolic compounds from hazel leaf was detected and their antioxidant and anti-inflammatory activities were investigated. The potential health benefits of different phenolic compounds were also predicted. The results showed that the 35 phenolic substances of free, conjugated and bound forms were identified including phenolic acids, flavonoids and catechins. Most of the hazel leaf phenolics were presented in free form, followed by conjugated and bound form. All the fractions effectively inhibited the production of reactive oxygen species and malondialdehyde in TBHP-stimulated human umbilical vein endothelial cells by enhancing endogenous superoxide dismutase, and accordingly alleviated inflammatory cytokines (NO, IL-1β, TNF-α, and IL-6) in LPS-stimulated RAW264.7 cells, showing obvious antioxidant and anti-inflammatory capacity. Moreover, combined with network pharmacology, the potential therapeutic effects and functional pathways of hazel leaf phenolics were predicted, which provided value basis for exploring their treatment on diseases and developing health products in the future.

Lipids in a Nutshell: Quick Determination of Lipid Content in Hazelnuts with NIR Spectroscopy

Hazelnuts (Corylus avellana L.) are among the most consumed dry fruits all over the world. Their commercial quality is defined, above all, by origin and dimension, as well as by lipid content. Evaluation of this parameter is currently performed with chemical methods, which are expensive, time consuming, and complex. In the present work, the near-infrared (NIR) spectroscopy, using both a benchtop research spectrometer and a retail handheld instrument, was evaluated in comparison with the traditional chemical approach. The lipid content of hazelnuts from different growing regions of origin (Italy, Chile, Turkey, Georgia, and Azerbaijan) was determined with two NIR instruments: a benchtop FT-NIR spectrometer (Multi Purpose Analyser-MPA, by Bruker), equipped with an integrating sphere and an optic fibre probe, and the pocket-sized, battery-powered SCiO molecular sensor (by Consumer Physics). The Randall/Soxtec method was used as the reference measurement of total lipid content. The collected NIR spectra were inspected through multivariate data analysis. First, a Principal Component Analysis (PCA) model was built to explore the information contained in the spectral datasets. Then, a Partial Least Square (PLS) regression model was developed to predict the percentage of lipid content. PCA showed samples distributions that could be linked to their total crude fat content determined with the Randall/Soxtec method, confirming that a trend related to the lipid content could be detected in the spectral data, based on their chemical profiles. PLS models performed better with the MPA instrument than SCiO, with the highest R² of prediction (R²_PRED = 0.897) achieved by MPA probe, while this parameter for SCiO was much lower (R²_PRED = 0.550). Further analyses are necessary to evaluate if more acquisitions may lead to better performances when using the SCiO portable spectrometer.

Food metabolomics: Latest hardware-developments for nontargeted food authenticity and food safety testing

The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome. Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.

Food Authentication: Truffle Species Classification by non-targeted Lipidomics Analyzes using Mass Spectrometry assisted by Ion Mobility Separation

Truffles are appreciated as food all over the world because of their extraordinary aroma. However, quantities are limited and successful cultivation in plantations is very labor-intensive and expensive, or even...

Non-Targeted LC-MS Metabolomics Approach towards an Authentication of the Geographical Origin of Grain Maize (Zea mays L.) Samples

Safety along the food and feed supply chain is an emerging topic and closely linked to the ability to analytical trace the geographical origin of food or feed. In this study, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole-time-of-flight mass spectrometry was used to trace back the geographical origin of 151 grain maize (Zea mays L.) samples from seven countries using a high resolution non-targeted metabolomics approach. Multivariate data analysis and univariate statistics were used to identify promising marker features related to geographical origin. Classification using only 20 selected markers with the Random Forest algorithm led to 90.5% correctly classified samples with 100 times repeated 10-fold cross-validation. The selected markers were assigned to the class of triglycerides, diglycerides and phospholipids. The marker set was further evaluated for its ability to separate between one sample class and the rest of the dataset, yielding accuracies above 89%. This demonstrates the high potential of the non-polar metabolome to authenticate the geographic origin of grain maize samples. Furthermore, this suggests that focusing on only a few lipids with high potential for grain maize authentication could be a promising approach for later transfer of the method to routine analysis.

Pouteria lucuma Pulp and Skin: In Depth Chemical Profile and Evaluation of Antioxidant Activity

Pouteria lucuma Ruiz and Pav., known as the ‘Gold of the Incas’ or ‘lucuma’, is a subtropical fruit belonging to the Sapotaceae family, with a very sweet flavor, used to prepare cakes, ice creams as well as in the baking and dairy industries. To date, the content of primary metabolites is known, but little information is reported about the composition in specialized metabolites. Moreover, no study is reported on skin which represent an important agricultural waste due to the high demand for lucuma. In order to have a preliminary metabolite profile of Pouteria lucuma, the extracts of pulp and skin have been analyzed by LC-ESI/LTQOrbitrap/MS/MS in negative ion mode. The careful analysis of the accurate masses, of the molecular formulas and of the ESI/MS spectra allowed to identify specialized metabolites belonging to phenolic, flavonoid and polar lipid classes. The LC-MS/MS analysis guided the isolation of compounds occurring in the pulp extract whose structures have been characterized by spectroscopic methods including 1D- and 2D-NMR experiments and ESI-MS analysis. Furthermore, the phenolic content of the extracts along with the antioxidant activity of extracts and isolated compounds was evaluated.

Integration of Untargeted and Pseudotargeted Metabolomics for Authentication of Three Shrimp Species Using UHPLC-Q-Orbitrap

In this work, an untargeted and pseudotargeted metabolomics combination approach was used for authentication of three shrimp species (Litopenaeus vanmamei, Penaeus japonicus, and Penaeus monodon). The monophasic extraction-based untargeted metabolomics approach enabled comprehensive-coverage and high-throughput analysis of shrimp tissue and revealed 26 potential markers. The pseudotargeted metabolomics approach confirmed 21 markers (including 9 key markers), which realized at least putative identification. The 21 confirmed markers, as well as 9 key markers, were used to develop PLS-DA models, correctly classifying 60/60 testing samples. Furthermore, DD-SIMCA and PLS-DA models were integrated based on the 9 key markers, with 59/60 and 20/20 samples of the species that were involved and uninvolved in model training correctly classified. The results demonstrated the potential of this untargeted and pseudotargeted metabolomics combination approach for shrimp species authentication.

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.

Effect of hot air and infrared roasting on hazelnut allergenicity

Roasting is known to affect the protein profile and allergenicity of hazelnuts (Corylus avellana cv TGL). The aim of the study was to investigate whether roasting techniques based on different heat transfer methods (hot air and infrared), differently affect the protein solubility and the IgE-binding capacities of both the soluble and insoluble hazelnut protein fractions. The immune-reactivity of the Cor a 9, Cor a 11 and Cor a 14 allergens resulted to be stable after roasting at 140 °C, for both types of treatment, while roasting at 170 °C caused a reduction in IgE-binding, which was particularly noticeable after infrared processing, that led to an almost complete disappearance of allergenicity. Microscopical analyses showed that roasting caused cytoplasmic network disruption, with a loss of lipid compartmentalization, as well as an alteration of the structure of the protein bodies and of the cell wall organization.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

The Inorganic Component as a Possible Marker for Quality and for Authentication of the Hazelnut's Origin

The inorganic component of hazelnuts was considered as a possible marker for geographical allocation and for the assessment of technological impact on their quality. The analyzed samples were Italian hazelnuts of the cultivar Tonda Gentile Romana and Turkish hazelnuts of the cultivars Tombul, Palaz and Çakildak. The hazelnuts were subjected to different drying procedures and different conservative methods. The concentration of 13 elements, namely Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, P, Sn, Sr and Zn, were quantified by inductively coupled plasma optical emission spectroscopy (ICP-OES). All the samples were previously digested in a microwave oven. Before proceeding with the analysis of the samples, the whole procedure was optimized and tested on a certified reference material. The results show that the inorganic component: (i) can represent a fingerprint, able to identify the geographical origin of hazelnuts, becoming an important quality marker for consumer protection; (ii) is strongly influenced by the treatments undergone by the investigated product during all the processing stages. A pilot study was also carried out on hazelnuts of the cultivar Tonda Gentile Trilobata Piemontese, directly harvested from the plant during early development to maturity and analyzed to monitor the element concentration over time.

Polar Lipids in Starch-Rich Commodities to be Analyzed with LC-MS-Based Metabolomics-Optimization of Ionization Parameters and High-Throughput Extraction Protocols

Metabolomics-based approaches are still receiving growing attention with regard to food authenticity testing. Such studies require enormous sample numbers with negligible experimental or analytical variations to obtain statistically reliable results. In this context, an extraction protocol in line with optimized ionization parameters was developed in consideration of potential starch-derived matrix effects focusing on the polar lipids of potatoes. Therefore, well-known extractions (Bligh and Dyer, Folch, Matyash, and a n-hexane-based procedure) were compared in a non-targeted and a targeted approach regarding the extractability of their lipids such as phosphatidylcholines, phosphatidylethanolamines, galacto- and glucocerebrosides, di- and triglycerides, and acylated steryl glucosides. The selected Folch method was also scrutinized in view of its ability to remove the matrix’s starch and consequently improved by substituting trichlormethane with ethyl acetate as a “greener” Folch approach. Moreover, the challenge of starch-derived contamination and imminent ion suppression in the electrospray ionization source (ESI) was addressed by an optimization of ionization parameters varying desolvation settings, removing injection peaks, and increasing the angles and distances of the ESI-device. Long-term stability tests over five days were performed successfully with a combination of appropriate extraction and decreased desolvation settings during ionization. In conclusion, the present methodology provided the basis for on-going large-scale metabolomic studies with respect to the botanical origin of potatoes using UPLC-IMS-QToF (ultra-high performance liquid chromatography ion mobility spectroscopy quadrupole-time of flight mass spectrometer).

Metabolite variation in three edible Italian Allium cepa L. by NMR-based metabolomics: a comparative study in fresh and stored bulbs

INTRODUCTION

In fruits and vegetables, comparative analysis of metabolic plant profiles has a high potential for quality control of active components. Onion (Allium cepa L.) is used fresh or stored as food, spice, and in traditional medicine. Its metabolic content, often with nutraceutical value, makes its level an important factor in agronomic production.

OBJECTIVE

To describe for the first time the metabolome of "San Pietro" white onion (WP), and compare its chemical profile with the red onion var. Tropea (RT) and the yellow onion var. Montoro (CM). Furthermore, we also aim to obtain a multivariate model based on NMR fingerprints to discriminate the three Italian A. cepa L. cultivars.

METHODS

For the chemical fingerprinting we used NMR-based metabolomics. We investigated the aqueous and chloroform extracts of fresh onion at harvesting time, and after 9-month storage. Principal component analysis (PCA), Partial least squares discriminant analysis (PLS-DA) and Orthogonal partial least squares (OPLS-DA) were used to build reliable models.

RESULTS

We obtained a clear discrimination of A. cepa L. varieties for the fresh and stored batches. The statistical model highlighted higher levels of fructo-oligosaccharides (FOS) in the fresh WP; RT showed a high content of glucose, citrate and amino acids, while CM had many sulfur components. In the stored samples (CMS, RTS), carbohydrates and sulfur components decreased, while in WPS the free monosaccharides concentration increased. Linoleic acid was overexpressed in the apolar extracts of CMF and WPF cultivars.

CONCLUSION

Metabolomics allows a reliable differentiation among onion varieties, and highlights the potential of fingerprinting for food authentication purposes.

Highly geographical specificity of metabolomic traits among Korean domestic soybeans (Glycine max)

Classification and characterization of agricultural products at molecular levels are important but often impractical with genotyping, particularly for soybeans that have numerous types of variety and landraces. Alternatively, metabolic signature, a determinant for nutritional value, can be the good molecular indicator, which reflects cultivation region-dependent factors such as climate and soil. Accordingly, we analyzed the integrative metabolic profiles of Korean soybeans cultivated in 7 different provinces (representative production areas), and explored the potential association with geographic traits. A total of 210 primary and secondary metabolites were profiled using gas-chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid-chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). Despite the partial heterogeneity of the soybean varieties, the metabolomic phenotypic analysis based on multivariate statistics inferred the chemical compositional characteristics was primarily governed by the regional specificity. The OPLS-DA model proposed biomarker cluster re-composed with 5 metabolites (tryptophan, malonylgenistin, malonyldaidzin, N-acetylornithine, and allysine) (AUCs = 0.870-1.0). The most distinctive metabolic profiles were identified with the soybeans of Gunsan (middle-western coast) and Daegu (east-southern inland area), which were best characterized by the highest contents of isoflavones and amino acids, respectively. Further interrogation on geographic data suggested the combinatorial association of region-specific metabolic features with general soil texture and climate traits (total rainfall and average annual temperature).

Metabolomics-Based Approach for the Discrimination of Potato Varieties ( Solanum tuberosum) using UPLC-IMS-QToF

One hundred eighty-two authentic potato samples ( Solanum tuberosum) of known variety were collected from various German regions in 2016 and 2017. Samples were extracted with a liquid-liquid-extraction protocol that included isopropanol, methanol, and water in order to focus on lipophilic metabolites. The analysis of nonpolar extracts was performed using an UPLC-IMS-QToF-MS system; data sets obtained were evaluated via multivariate data analysis. A selection of 14 key metabolites with a significant difference in their abundance profiles was identified. This set of markers contained four hydroxylated glucocerebrosides, two phosphoinositols, one phosphocholine, and seven acylated sterol glucosides based on stigmasterol and β-sitosterol, which primarily enable the varietal discrimination. Fragments and neutral losses commonly appearing within one class or subclass of lipids were summarized within a new database that included ion mobility data. The performance of the approach was verified with twenty-nine commercial potato samples.

Estimating effects of yeast extract compositions on Escherichia coli growth by a metabolomics approach

Bioprocess stability depends on the variety of yeast extract, which varies from lot-to-lots and between brands, thereby leading to variable bacterial growth and productivity in manufacturing processes. As a model experiment for good stability of bioprocesses, Escherichia coli growth in media containing different brands of yeast extract was evaluated and predicted using component-profiling and multivariate data analysis (metabolomics approach). The components of yeast extract were extracted from media containing varying concentrations of yeast extract and analyzed using gas chromatography-mass spectrometer. The yeast extract was categorized into three clades by principal component analysis (PCA). The E. coli growth using yeast extract showed approximately 30% difference at equivalent amount of supplementation. The bacterial growth in the media was estimated for the component profiles by partial least squares regression analysis (PLS-R). A predictive model was developed from the relationship between bacterial growth (as subjective attributes) and component profiles (as objective attributes), and correlation coefficients were calculated. Most of the amino acids in the media stimulated growth; however, methionine had negative effect on growth. In a culture validation, Asp, Val, Glu, and Try stimulated the bacterial growth, but Met inhibited. The other amino acids tested, Ser, Ile, Asp, Lys, Phe, Leu, Thr, and Gly did not show significant effects on the growth. The results indicate that the metabolomics approach can provide useful feedback information to improve the cultivation.

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.

Phenolic variation among Chamaecrista nictitans subspecies and varieties revealed through UPLC-ESI(-)-MS/MS chemical fingerprinting

INTRODUCTION

Comparative analysis of metabolic features of plants has a high potential for determination of quality control of active ingredients, ecological or chemotaxonomic purposes. Specifically, the development of efficient and rapid analytical tools that allow the differentiation among species, subspecies and varieties of plants is a relevant issue. Here we describe a multivariate model based on LC-MS/MS fingerprinting capable of discriminating between subspecies and varieties of the medicinal plant Chamaecrista nictitans, a rare distributed species in Costa Rica.

METHODS

Determination of the chemical fingerprint was carried out on a LC-MS (ESI-QTOF) in negative ionization mode, main detected and putatively identified compounds included proanthocyanidin oligomers, several flavonoid C- and O-glycosides, and flavonoid acetates. Principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and cluster analysis of chemical profiles were performed.

RESULTS

Our method showed a clear discrimination between the subspecies and varieties of Chamaecrista nictitans, separating the samples into four fair differentiated groups: M1 = C. nictitans ssp. patellaria; M2 = C. nictitans ssp. disadena; M3 = C. nictitans ssp. nictitans var. jaliscensis and M4 = C. nictitans ssp. disadena var. pilosa. LC-MS/MS fingerprint data was validated using both morphological characters and DNA barcoding with ITS2 region. The comparison of the morphological characters against the chemical profiles and DNA barcoding shows a 63% coincidence, evidencing the morphological similarity in C. nictitans. On the other hand, genetic data and chemical profiles grouped all samples in a similar pattern, validating the functionality of our metabolomic approach.

CONCLUSION

The metabolomic method described in this study allows a reliably differentiation between subspecies and varieties of C. nictitans using a straightforward protocol that lacks extensive purification steps.

Food Authentication: Small-Molecule Profiling as a Tool for the Geographic Discrimination of German White Asparagus

For the first time, a non-targeted metabolomics approach by means of ultraperformance liquid chromatography coupled to electrospray quadruple time-of-flight mass spectrometry was chosen for the discrimination of geographical origins of white asparagus samples ( Asparagus officinalis). Over a period of four harvesting periods (4 years), approximately 400 asparagus samples were measured. Initially, four different liquid chromatography-mass spectrometry methods were used to detect as many metabolites as possible and to assess which method is most suitable. The most relevant marker compounds were linked to the influence of different plant stress parameters and climate effects. Some of the samples were also analyzed by isotope-ratio mass spectrometry (IRMS), which is the current gold standard for the discrimination of the geographical origin of asparagus. In summary, the analysis of the metabolome was proven to be quite suitable to determine the geographical origin of asparagus and seems to provide better interpretable results than IRMS studies.

Food fingerprints - A valuable tool to monitor food authenticity and safety

In recent years, food frauds and adulterations have increased significantly. This practice is motivated by fast economical gains and has an enormous impact on public health, representing an important issue in food science. In this context, this review has been designed to be a useful guide of potential biomarkers of food authenticity and safety. In terms of food authenticity, we focused our attention on biomarkers reported to specify different botanical or geographical origins, genetic diversity or production systems, while at the food safety level, molecular evidences of food adulteration or spoilage will be highlighted. This report is the first to combine results from recent studies in a format that allows a ready overview of metabolites (<1200 Da) and potentially molecular routes to monitor food authentication and safety. This review has therefore the potential to unveil important aspects in food adulteration and safety, contributing to improve the current regulatory frameworks.

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.

Gas chromatography coupled with mass spectrometry-based metabolomics for the classification of tempe from different regions and production processes in Indonesia

Tempe, a fermented soybean originally from Indonesia, is an excellent protein source with high nutritional quality. Differences in the production process and unique fermentation condition in different regions result in varieties of tempe. Despite its high cultural and economic values, there are very few studies on the characterization of tempe based on the differences of production process and geographical origin. Metabolomics is a powerful tool assessing food quality, food safety, and determination of origin and varietal differences. In this study, metabolomics is applied for the study of Indonesian tempe obtained from various regions and different production processes. Seventeen samples were collected from 6 different cities in Java Island, which were produced by local tempe crafters (traditional), semi-modern industry and modern industry. Untargeted metabolomics by gas chromatography coupled with mass spectrometry (GC/MS) was implemented to discriminate various kinds of tempe and identify metabolites that are associated with these differences. Results showed that tempe produced in different places clustered together according to the cities and their production category. Sugars and amino acids groups were found to be primary compounds that contributed to this result. This is the first report that address the metabolic differences between different varieties of tempe from different regions and production processes. The knowledge from this study is important for future development of tempe production.

Fecal Metabolomics of Type 2 Diabetic Rats and Treatment with Gardenia jasminoides Ellis Based on Mass Spectrometry Technique

Modern studies have indicated Gardenia jasminoides Ellis (G. jasminoides) showed positive effect in treating type 2 diabetes mellitus (T2DM). In this study, 60 streptozotocin-induced T2DM rats were divided into four groups: type 2 diabetes control group, geniposide-treated group, total iridoid glycosides-treated group, and crude extraction of gardenlae fructus-treated group. The other ten healthy rats were the healthy control group. During 12 weeks of treatment, rat's feces samples were collected for the metabolomics study based on mass spectrometry technique. On the basis of the fecal metabolomics method, 19 potential biomarkers were screened and their relative intensities in each group were compared. The results revealed G. jasminoides mainly regulated dysfunctions in phenylalanine metabolism, tryptophan metabolism, and secondary bile acid biosynthesis pathways induced by diabetes. The current study provides new insight for metabonomics methodology toward T2DM, and the results show that feces can preferably reflect the liver and intestines disorders.