Food Targeting: Geographical Origin Determination of Hazelnuts (Corylus avellana) by LC-QqQ-MS/MS-Based Targeted Metabolomics Application

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

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.

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

Untargeted Metabolomics Analysis Revealed the Difference of Component and Geographical Indication Markers of Panax notoginseng in Different Production Areas

Panax notoginseng (P. notoginseng) has excellent medicinal and food dual-use characteristics. However, P. notoginseng with a unique origin label has become the target of fraud because of people confusing or hiding its origin. In this study, an untargeted nuclear magnetic resonance (NMR)-based metabolomics approach was used to discriminate the geographical origins of P. notoginseng from four major producing areas in China. Fifty-two components, including various saccharides, amino acids, saponins, organic acids, and alcohols, were identified and quantified through the NMR spectrum, and the area-specific geographical identification components were further screened. P. notoginseng from Yunnan had strong hypoglycemic and cardiovascular protective effects due to its high acetic acid, dopamine, and serine content, while P. notoginseng from Sichuan was more beneficial for diseases of the nervous system because of its high content of fumarate. P. notoginseng from Guizhou and Tibet had high contents of malic acid, notoginsenoside R1, and amino acids. Our results can help to distinguish the geographical origin of P. notoginseng and are readily available for nutritional recommendations in human consumption.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS¹ and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS¹ spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS¹ and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

Application of Mass Spectrometry for Determining the Geographic Production Area of Wagyu Beef

Japanese Black cattle (Japanese Wagyu) beef is attracting attention for its aroma and marbling, and its handling is increasing worldwide. Here, we focused on the origin discrimination of Wagyu beef and analyzed the nutritional components of Japanese Wagyu (produced in multiple prefectures of Japan), Hybrid Wagyu (a cross between Angus and Wagyu cattle born in Australia and transported to Japan), and Australian Wagyu beef using mass spectrometry (MS). Triple-quadrupole liquid chromatography-MS was used to clarify the molecular species of lipids in Wagyu beef. Fourteen classes of lipids were separated, and 128 different triacylglycerides (TGs) were detected. A simple comparative analysis of these TGs using high-performance liquid chromatography revealed significantly higher levels of triolein (C18:1/C18:1/C18:1; abbreviated OOO) and C18:1/C18:1/C16:1 (OOPo) in Japanese Wagyu. Wagyu elements beef were comprehensively analyzed using inductively coupled plasma (ICP)-MS and ICP-optical emission spectrometry. We found significant differences in the rubidium, cesium, and lithium levels of Japanese and Australian Wagyu beef. On comparing metabolites using gas chromatography-MS, we identified significant differences in the levels of amino acids and other components of the Japanese and Australian Wagyu beef. These results suggest the possibility of determining the origin of Wagyu cattle breeds using MS and genetic discrimination.

A novel approach for authentication of shellac resin in the shellac-based edible coatings: Contain shellac or not in the fruit wax preservative coating

•

A novel approach based on targeted metabolomics for the authentication of shellac resin in shellac-based coating solution was established for the first time.

•

The authentication of shellac resin was skillfully transformed by means of taking monomer compounds constituting shellac resin (fatty acids and terpenic acids) as targeted metabolites.

•

The feasibility of the authenticated approach of shellac resin in commercial coating solution products for fruit preservation was verified by taking common metabolites as the biomarkers.

As an edible coating substrate, the detection of shellac resin has always been an intractable problem. In this paper, an authentication method of shellac resin in shellac-based edible coatings was established. Results showed that the authentication of shellac resin could be skillfully transformed as the identification of 13 targeted metabolites which were monomer compounds of shellac resin. The 13 targeted metabolites were further divided into 6 differential metabolites and 7 common metabolites with the metabonomic method and difference analysis of targeted metabolite contents. Then, four commercial soi-disant shellac-based coating solutions were selected to verify the feasibility of this method, and 7 common metabolites were detected in only one commercial sample, highly consistent with the results of shellac resin. All the above results indicated that the targeted metabolomics approach established in this study could provide a scientific basis for the qualitative authentication of shellac resin in the preservation coating.

Corylus avellana L. Natural Signature: Chiral Recognition of Selected Informative Components in the Volatilome of High-Quality Hazelnuts

The volatile fraction of plant-based foods provides useful functional information concerning sample-related variables such as plant genotype and phenotype expression, pedoclimatic and harvest conditions, transformation/processing technologies, and can be informative about the sensory quality. In this respect, the enantiomeric recognition of the chiral compounds increases the level of information in profiling studies, being the biosynthesis of native compounds often stereo-guided. Chiral native volatiles mostly show an enantiomeric excess that enables origin authentication or support correlation studies between chemical patterns and sensory profiles. This study focuses, for the first time, on the enantiomeric composition of a large set of chiral compounds within the complex volatilome of Corylus avellana L. belonging to different cultivars (Tonda Gentile Romana, Tonda Gentile Trilobata, Anakliuri) and harvested in different geographical areas (Italian and Georgian). Besides native components profiled in raw kernels, volatiles formed after technological treatment (i.e., roasting) are also considered. Headspace solid-phase microextraction combined with enantioselective gas chromatography-mass spectrometry enables the accurate tracking and annotation of about 150 compounds across many samples. The results show that chiral compounds have diagnostic distribution patterns within hazelnut volatilome with cultivar and harvest region playing the major role. Moreover, being some of these chiral molecules also key-aromas, their distribution has a decisive impact on the sensory properties of the product. In particular, the enantiomeric composition of (E)-5-methyl-2-hepten-4-one (filbertone) resulted to be discriminant for origin authentication. The enantiomeric distribution showed, according to literature, an excess of the (S)-enantiomer in both raw and roasted samples volatilome with larger differences in raw samples. The amount of both (R) and (S)-filbertone increases during roasting; the most marked increase for (R)-enantiomer is observed in Italian samples, thus supporting evidence of better hedonic properties and more pleasant odor and aroma.

NMR Metabolome-Based Classification of Cymbopogon Species: a Prospect for Phyto-equivalency of its Different Accessions Using Chemometric Tools

Cymbopogon species are widely distributed worldwide and known for their high essential oil content with potential commercial and medicinal benefits justifying for their inclusion in food and cosmetics. Most species received scant characterization regarding their full complement of bioactive constituents necessary to explain their medicinal activities. In this study, the metabolite profiles of 5 Cymbopogon species, C. citratus, C. flexuosus, C. procerus, C. martini, and C. nardus, were characterized via NMR-based metabolomics. The results of 13 shoot accessions revealed the identification and quantification of 23 primary and secondary metabolites belonging to various compound classes. Multivariate analyses were used for species classification, though found not successful in discrimination based on geographical origin. Nevertheless, C. citratus was found particularly enriched in neral, geranial, (E)-aconitic acid, isoorientin, and caffeic acid as the major characterizing metabolites compared to other species, while an unknown apigenin derivative appeared to discriminate C. martini. The high essential oil and phenolic content in C. citratus emphasizes its strong antioxidant activity, whereas (E)-aconitic acid accounts for its traditional use as insecticide. This study affords the first insight into metabolite compositional differences among Cymbopogon species. Moreover, antimicrobial, insecticidal, antidiabetic, and antioxidant compounds were identified that can be utilized as biomarkers for species authentication.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Detection of Peanut Adulteration in Food Samples by Nuclear Magnetic Resonance Spectroscopy

The addition of cheap and also readily available raw materials, such as peanut powder, to visually and chemically similar matrices is a common problem in the food industry. When peanuts are used as an adulterant, there is an additional risk of potential health hazard to consumers as a result of allergy-induced anaphylaxis. In this study, different series of peanut admixtures to visually similar food products, such as powdered hazelnuts, almonds, and walnuts, were prepared and analyzed by ¹H nuclear magnetic resonance (NMR) spectroscopy. For identification, an isolated signal at 3.05 ppm in the ¹H NMR spectrum of polar peanut extract was used as an indicator of peanut adulteration. The chemical marker was identified as (2S,4R)-N-methyl-4-hydroxy-l-proline by resynthesis of the compound and used as an internal standard. The signal-to-noise ratio and the integral of the signal of the marker can both be used to detect peanut impurities. Overall, an approximate limit of detection of 4% admixtures of peanut in various food products was determined using a 400 MHz spectrometer. With regard to food fraud, we present a viable screening method for detection of economic-relevant peanut adulteration.

Mass-Spectrometry-Based Food Metabolomics in Routine Applications: A Basic Standardization Approach Using Housekeeping Metabolites for the Authentication of Asparagus

The low reproducibility of non-targeted liquid chromatography-mass spectrometry-based metabolomics approaches represents a major challenge for their implementation in routine analyses, because it is impossible to compare individual measurements directly with each other, if they were not analyzed in the same batch. This study describes a normalization process based on housekeeping metabolites in plant-based raw materials, which are present in comparatively constant concentrations and are subject to no or only minor deviations as a result of exogenous influences. As a model, an authenticity study was selected to determine the origin of white asparagus (Asparagus officinalis). Using three model data sets and one test data set, we were able to show that samples that have been measured independently of one another can be correctly assigned in terms of origin after the normalization with housekeeping metabolites. The procedure does not require internal standards or the measurements of further reference samples and can also be applied to other matrices and scientific issues.

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.

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.

Metabolomics and hormonomics to crack the code of filbert growth

INTRODUCTION

Plants respond to changes in their environments through hormonal activation of a physiological cascade that redirects metabolic resources and growth. In filberts (Corylus sp.), chelated iron promotes the growth of new shoots but the mechanism(s) are not understood.

OBJECTIVES

To use untargeted metabolomics and hormonomics approaches to generate novel hypotheses for the morphoregulatory role of ferric ethylenediamine-N,N'-di-(ortho-hydroxyphenyl) acetic acid (Fe-EDDHA) in filbert shoot organogenesis in vitro.

METHODS

Data were generated using previously optimized standardized untargeted metabolomics protocols with time of flight mass spectrometry. Multivariate statistical tools (principal component and partial least squares discriminant analysis) did not detect significant differences. Discovery tools Significance Analysis of Microarrays (SAM), multiple linear regression analysis, Bayesian analysis, logical algorithms, machine learning, synthetic biotransformations, targeted hormonomics, and online resources including MetaboAnalyst were used.

RESULTS

Starch/sucrose metabolism and shikimate pathway metabolites were increased. Dose dependent decreases were found in polyphenol metabolism, specifically ellagic acid and its methylated derivative 3,4,3'-tri-O-methylellagic acid. Hormonomics analysis revealed significant differences in phytohormones and their conjugates. FeEDDHA treatment reduced indole-3-acetic acid, abscisic acid, salicylic acid, jasmonic acid conjugates (JA-Trp, JA-Ile, OH-JA) and dihydrozeatinglucoside in regenerating explants. Serotonin (5HT) was decreased in FeEDDHA-treated regenerating tissues while the related metabolite melatonin was increased. Eight phenolic conjugates of 5HT and eight catabolites were affected by FeEDDHA indicating that metabolism to sequester, deactivate and metabolize 5HT was induced by Fe(III). Tryptophan was metabolized through kynurenine but not anthranilate.

CONCLUSION

Seven novel hypotheses were generated to guide future studies to understand the regulatory control(s) of shoot organogenesis.

Origin identification of Panax notoginseng by multi-sensor information fusion strategy of infrared spectra combined with random forest

Traditional Chinese medicine Panax notoginseng is a valuable geo-authentic herbal material. The difference of growth environment in different producing areas has significant influence on the quality of traditional Chinese medicine, and origin identification is an important part of the quality assessment of P. notoginseng. In this study, Fourier transform mid-infrared (FT-MIR) and near infrared (NIR) sensor technologies combined with single spectra analysis and multi-sensor information fusion strategy (low-, mid- and high-level) for the origin identification of 210 P. notoginseng samples from five cities in Yunnan Province, China. FT-MIR spectra were considered to play a greater role in data analysis than NIR spectra. Random forest (RF) was used to establish classification models. The result of the random forest Boruta (RF-Bo) model and the random forest variable selection (RF-Vs) model based on high-level multi-sensor information fusion strategy was satisfactory. In addition, the RF-Bo model based on high-level multi-sensor information fusion strategy was faster and simpler in data analysis and the accuracy was 95.6%.

Untargeted metabolomics with multivariate analysis to discriminate hazelnut (Corylus avellana L.) cultivars and their geographical origin

BACKGROUND

In the present study a metabolomics-based approach was used to discriminate among different hazelnut cultivars and to trace their geographical origins. Ultra-high-pressure liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UHPLC-ESI/QTOF-MS) was used to profile phenolic and sterolic compounds.

RESULTS

Compounds were identified against an in-house database using accurate monoisotopic mass and isotopic patterns. The screening approach was designed to discern 15 hazelnut cultivars and to discriminate among the geographical origins of six cultivars from the four main growing regions (Chile, Georgia, Italy, and Turkey). This approach allowed more than 1000 polyphenols and sterols to be annotated. The metabolomics data were elaborated with both unsupervised (hierarchical clustering) and supervised (orthogonal projections to latent structures discriminant analysis, OPLS-DA) statistics. These multivariate statistical tools allowed hazelnut samples to be discriminated, considering both 'cultivar type' and 'geographical origin'. Flavonoids (anthocyanins, flavanols and flavonols - VIP scores 1.34-1.49), phenolic acids (mainly hydroxycinnamics - VIP scores 1.35-1.55) together with cholesterol, ergosterol, and stigmasterol derivatives (VIP scores 1.34-1.49) were the best markers to discriminate samples according to geographical origin.

CONCLUSIONS

This work illustrates the potential of untargeted profiling of phenolics and sterols based on UHPLC-ESI/QTOF mass spectrometry to discriminate hazelnut and support authenticity and origin. © 2019 Society of Chemical Industry.

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.

Modeling growth and fermentation inhibition during bioethanol production using component profiles obtained by performing comprehensive targeted and non-targeted analyses

Corn cob and corn stover hydrolysates are forms of lignocellulosic biomass that can be used in second generation bioethanol production and biorefinery processes. Growth and fermentation inhibitors generated during physicochemical and enzymatic hydrolysis decrease ethanol and biomaterial production during the subsequent biological processes. Here, estimates of growth and fermentation inhibition during bioethanol fermentation were made using component profiles of corn cobs and corn stover at different degrees of hydrolysis. The component profiles were acquired by non-targeted gas chromatography mass spectrometry and targeted high-performance liquid chromatography. Correlations between the comprehensive analysis results and yeast growth and ethanol production were modeled very accurately by partial-least-squares regression analysis. Acetate, apocynin, butyrovanillone, furfural, furyl hydroxymethyl ketone, m-methoxyacetophenone, palmitic acid, syringaldehyde, and xylose, were compounds with very variable importance in projection values and had negative correlation coefficients in the model. In fact, methoxyacetophenone, apocynin, and syringaldehyde inhibited fermentation more than furfural in equivalent concentration.

Exploiting High-Resolution Mass Spectrometry for Targeted Metabolite Quantification and 13C-Labeling Metabolism Analysis

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. In this chapter, we summarize high-resolution mass spectrometry-based approaches mainly used for targeted metabolite and metabolomics analysis, and then introduce an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids, and shikimate pathway-related metabolites. Major steps in the workflow, including cell culture, metabolite extraction, LC-MS analysis and data processing, are described. Furthermore, we adapt this new approach to a dynamic ¹³C-labeling experiment and demonstrate its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary ¹³C-labeling metabolism analysis.

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.

NMR-based global metabolomics approach to decipher the metabolic effects of three plant growth regulators on strawberry maturation

Plant growth regulators (PGRs) are commonly used to regulate maturation in strawberry. Despite this, comprehensive assessments of the metabolomic effects of PGRs on strawberry maturation are lacking. In this study, a nuclear magnetic resonance-based approach, combined with multivariate and pathway analysis, was used to evaluate the regulatory effects of gibberellin, forchlorfenuron, and brassinolide, applied at two different maturation stages, on the expression of metabolites in strawberry. The results demonstrated that the PGRs differentially influenced metabolism, whether applied at the same or different maturation stages. Additionally, we also discovered that these different PGRs exhibited some similar metabolic trends when applied at the same growth period. Our findings validate the use of NMR-based metabolomics for identifying subtle changes in the expression of metabolites associated with PGR application.

Current and future perspectives of functional metabolomics in disease studies-A review

Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.

Metabolomic analysis for the protective effects of mangiferin on sepsis-induced lung injury in mice

This study aimed to investigate the efficacy of mangiferin, including its known antioxidant and anti-inflammatory effects on sepsis-induced lung injury induced by a classical cecal ligation and puncture (CLP) models in mouse using a metabolomics approach. A total of 24 mice were randomly divided into four groups: the sham group was given saline before sham operation. The CLP group received the CLP operation only. HMF and LMF groups were given mangiferin treatment of high dose and low dose of mangiferin, respectively, before the CLP operation. One week after treatment, the mice were sacrificed and their lungs were collected for metabolomics analysis. We developed ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry to perform lung metabolic profiling analysis. With the methods of principal component analysis and partial least squares discriminant analysis, 58 potential metabolites associated with amino acid metabolism, purine metabolism, lipid metabolism and energy regulation were observed to be increased or reduced in HMF and LMF groups compared with the CLP group. Conclusively, our results suggest that mangiferin plays a protective role in the moderation of sepsis-induced lung injury through reducing oxidative stress, regulating lipid metabolism and energy biosynthesis.