Mass spectrometry-based metabolomics applied to the chemical safety of food

Integrating mass defect filtering and targeted molecular networking for foodomics research: A case study of Magnolia officinalis cortex

Mass spectrometry (MS)-based foodomics is widely used to tackle complex challenges in food science, although its effectiveness is often hampered by extensive data redundancy. To address this limitation, a novel MS-based foodomics strategy, integrating mass defect filtering and targeted molecular networking (IMDFTMN), was developed and applied to Magnolia Officinalis Cortex (MOC). By minimizing redundant information, more concise and streamlined molecular networks were produced, thereby enhancing the efficiency of compound annotation. In this study, 167 characteristic compounds, including phenylpropanoid glycosides, phenolic glycosides, lignans, and alkaloids, were identified from 44 batches of MOC. These batches, obtained from various regions, were grouped into two distinct clusters based on 25 differential markers. The practical utility of these markers was validated through a support vector machine model, which accurately classified the 44 MOC batches according to geographic origin. This process not only improved grouping accuracy in foodomics analyses but also enabled the precise identification of key differential markers. In conclusion, this innovative strategy not only deepened our comprehension of the chemical profile characteristics of MOC across various regions, facilitating further studies on quality consistency and efficacy, but also provided significant insights for addressing critical issues in food science, such as food composition analysis, adulteration detection, variety identification, and origin tracing.

Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

Application of Sorbent-Based Extraction Techniques in Food Analysis

This review presents an outline of the application of the most popular sorbent-based methods in food analysis. Solid-phase extraction (SPE) is discussed based on the analyses of lipids, mycotoxins, pesticide residues, processing contaminants and flavor compounds, whereas solid-phase microextraction (SPME) is discussed having volatile and flavor compounds but also processing contaminants in mind. Apart from these two most popular methods, other techniques, such as stir bar sorptive extraction (SBSE), molecularly imprinted polymers (MIPs), high-capacity sorbent extraction (HCSE), and needle-trap devices (NTD), are outlined. Additionally, novel forms of sorbent-based extraction methods such as thin-film solid-phase microextraction (TF-SPME) are presented. The utility and challenges related to these techniques are discussed in this review. Finally, the directions and need for future studies are addressed.

Simultaneous Determination of Multiple Contaminants in Chicken Liver Using Dispersive Liquid-Liquid Microextraction (DLLME) Detected by LC-HRMS/MS

Simultaneous determination of a mixture of food contaminants, including pesticides, sulphonamides, fluoroquinolones, anthelmintics, and aflatoxin B1, in solid biological samples (chicken liver) by dispersive liquid-liquid microextraction/liquid chromatography-high resolution mass spectrometry (DLLME/LC-HRMS) is presented. Previous work focused on the application of DLLME to single-class contaminants. In this work, the DLLME extraction method has been extended to complex multiresidues in the biological matrix. The first part of this study was the selection of an appropriate solvent that enabled the dissolution of analytes from the chicken livers. The matrix-matched calibration curves showed good linearity in the range 0.5-50.0 µg kg^-1 for aflatoxin B1 and 50-500 µg kg^-1 for pesticides, fluoroquinolones, sulphonamides, and anthelmintics, with a coefficient of determination (R²) values of 0.9916-0.9967. The mean recoveries were in the range of 80.4-96.3%, and the relative standard deviation (RSD) values were in the range of 1.53-8.98%. The limit of detection (LOD) and the limit of quantification (LOQ) values were 0.03 µg kg^-1 and 0.09 µg kg^-1, respectively, for aflatoxin B1, and for pesticides, fluoroquinolones, sulphonamides, and anthelmintics, they were in the range of 0.011-1.197 µg kg^-1 and 0.150-2.579 µg kg^-1, respectively. The developed method was compared with the standard solid phase extraction (SPE) method, and there was no significant difference between the two methods.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

Characterization and Classification of Spanish Honey by Non-Targeted LC-HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods

A non-targeted LC-HRMS fingerprinting methodology based on a C18 reversed-phase mode under universal gradient elution using an Orbitrap mass analyzer was developed to characterize and classify Spanish honey samples. A simple sample treatment consisting of honey dissolution with water and a 1:1 dilution with methanol was proposed. A total of 136 honey samples belonging to different blossom and honeydew honeys from different botanical varieties produced in different Spanish geographical regions were analyzed. The obtained LC-HRMS fingerprints were employed as sample chemical descriptors for honey pattern recognition by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results demonstrated a superior honey classification and discrimination capability with respect to previous non-targeted HPLC-UV fingerprinting approaches, with them being able to discriminate and authenticate the honey samples according to their botanical origins. Overall, noteworthy cross-validation multiclass predictions were accomplished with sensitivity and specificity values higher than 96.2%, except for orange/lemon blossom (BL) and rosemary (RO) blossom-honeys. The proposed methodology was also able to classify and authenticate the climatic geographical production region of the analyzed honey samples, with cross-validation sensitivity and specificity values higher than 87.1% and classification errors below 10.5%.

Evaluation of different extractions for the metabolite identification of malachite green in brook trout and shrimp

Applications of mass spectrometry-based metabolomics in food science have developed fast in the last decade. Sample preparation and data processing are critical in non-target/metabolomic workflows but there is currently no standardized protocol for the development of these methods. The impact of data processing parameters or the inclusion of a different matrix is not often taken into account during the selection of an extraction. Thus, this study aimed to investigate the impact of different extractions, e.g., QuEChERS, and data processing on the determination of malachite green metabolites in two different organisms, brook trout and shrimp. The results obtained confirm the need for a harmonized approach for the validation of non-target workflows, as depending on the comparison criteria, the matrix, the mode of ionization or data processing, a different extraction could be chosen. This study also identified for the first time des-methylated leucomalachite green as another metabolite in the two organisms.

Sample preparation techniques for suspect and non-target screening of emerging contaminants

The progress in sensitivity and resolution in mass spectrometers in recent years provides the possibility to detect a broad range of organic compounds in a single procedure. For this reason, suspect and non-target screening techniques are gaining attention since they enable the detection of hundreds of known and unknown emerging contaminants in various matrices of environmental, food and human sources. Sample preparation is a critical step before analysis as it can significantly affect selectivity, sensitivity and reproducibility. The lack of generic sample preparation protocols is obvious in this fast-growing analytical field, and most studies use those of traditional targeted analysis methods. Among them, solvent extraction and solid phase extraction (SPE) are widely used to extract emerging contaminants from solid and liquid sample types, respectively. Sequential solvent extraction and a combination of different SPE sorbents can cover a broad range of chemicals in the samples. Gel permeation chromatography (GPC) and adsorption chromatography, including acidification, are typically used to remove matrix components such as lipids from complex matrices, but usually at the expense of compound losses. Ideally, the purification of samples intended for non-target analysis should be selective of matrix interferences. Recent studies have suggested quality assurance/quality control measures for suspect and non-target screening, based on expansion and extrapolation of target compound lists, but method validations remain challenging in the absence of analytical standards and harmonized sample preparation approaches.

Exploring the Biological Effect of Biosynthesized Au-Pd Core-Shell Nanoparticles through an Untargeted Metabolomics Approach

The biosynthesis of Au-Pd core-shell nanoparticles (NPs) with wild-type Escherichia coli (Au-Pd/E. coli) is an excellent newly established, environmentally friendly synthetic method for the fabrication of nanomaterials compared to traditional chemosynthesis. However, there is insufficient detailed bioinformation on the compatibility, metabolic process, and mechanism of this approach. Metabolomics approaches have provided an excellent alternative to numerous bioinformatics approaches for shedding light on the biological response of an organism exposed to external stimuli at the molecular level. In this study, two different doses (8 and 80 μg/mL) of Au-Pd/E. coli were applied to treat human umbilical vein endothelial cells (HUVECs). Gas chromatography/mass spectrometry coupled with bioinformatics was used to analyze the changes in the HUVEC metabolome after treatment. The results indicated the occurrence of nonsignificant acute cytotoxicity based on cell proliferation and apoptosis analysis, while high concentrations (80 μg/mL) of Au-Pd/E. coli induced dramatic changes in energy metabolism, revealing a notable inhibition of the tricarboxylic acid (TCA) cycle along with the enhancement of glycolysis, the pentose phosphate pathway, fatty acid biosynthesis, and lipid accumulation, which was correlated with mitochondrial dysfunction. The metabolomics results obtained for this novel Au-Pd/E. coli-cell system could broaden our knowledge of the biological effect of Au-Pd/E. coli and possibly reveal material modifications and technological innovations.

Liquid chromatographic methods coupled to chemometrics: a short review to present the key workflow for the investigation of wine phenolic composition as it is affected by environmental factors

The guarantee of wine authenticity arises great concern because of its nutritional and economic importance. Phenolic fingerprints have been used as a source of chemical information for various authentication issues, including botanical and geographical origin, as well as vintage age. The local environment affects wine production and especially its phenolic metabolites. Integrated analytical methodologies combined with chemometrics can be applied in wine fingerprinting studies for the determination and establishment of phenolic markers that contain comprehensive and standardized information about the wine profile and how it can be affected by various environmental factors. This review summarizes all the recent trends in the generation of chemometric models that have been developed for treating chromatographic data and have been used for the investigation of critical wine authenticity issues, revealing phenolic markers responsible for the botanical, geographical, and vintage age classification of wines. Overall, the current review suggests that chromatographic methodologies are promising and powerful techniques that can be used for the accurate determination of phenolic compounds in difficult matrices like wine, highlighting the advantages of the applications of supervised chemometric tools over unsupervised for the construction of prediction models that have been successfully used for the classification based on their territorial and botanical origin.

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Investigation of Obesity-Alleviation Effect of Eurycoma longifolia on Mice Fed with a High-Fat Diet through Metabolomics Revealed Enhanced Decomposition and Inhibition of Accumulation of Lipids

The metabolic and bioactivity effects of Eurycoma longifolia (Eucalyptus longifolia) in obesity treatment were studied in mice fed with a high-fat diet using a metabolomics approach. Aqueous extracts of E. longifolia were obtained via grinding, dissolving, and freeze-drying. The hepatic steatosis effect of E. longifolia was characterized by hematoxylin and eosin histological staining. External performance of the obesity-alleviation effect was monitored by measuring body and food weight. In addition, the metabolomics analysis of the E. longifolia-mice interaction system was performed using the established platform combining liquid chromatography-tandem mass spectrometry with statistical analysis. The presence and spatial distribution patterns of differential molecules were further evaluated through desorption electrospray ionization-mass spectrometry imaging. The results showed that E. longifolia played a vital role in downregulating lipid accumulation (especially triacylglycerols) and fatty acids biosynthesis together with enhanced lipid decomposition and healing in Bagg albino mice. During such a process, E. longifolia mainly induced metabolomic alterations of amino acids, organic acids, phospholipids, and glycerolipids. Moreover, under the experimental concentrations, E. longifolia induced more fluctuations of aqueous-soluble metabolites in the plasma and lipids in the liver than in the kidneys. This study provides an advanced alternative to traditional E. longifolia-based studies for evaluating the metabolic effects and bioactivity of E. longifolia through metabolomics technology, revealing potential technological improvement and clinical application.

Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review

Metabolomics is a high precision analytical approach to obtaining detailed information of varieties of metabolites produced in biological systems, including foods. This study reviews the use of metabolomic approaches such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GC-MS), matrix assisted laser desorption /ionization tandem time of flight mass spectrometry (MALDI-TOF-MS) and nuclear magnetic resonance (NMR) for investigating the presence of foodborne pathogens and their metabolites. Pathogenic fungi and their notable metabolites (mycotoxins) have been studied more extensively using metabolomics as compared to bacteria, necessitating further studies in this regard. Nevertheless, such identified fungal and bacteria metabolites could be used as biomarkers for a more rapid detection of these pathogens in food. Other important compounds detected through metabolomics could also be correlated to functionality of these pathogenic strains, determined by the composition of the foods in which they exist, thereby providing insights into their metabolism. Considering the prevalence of these food pathogens, metabolomics still has potentials in the determination of food-borne pathogenic microorganisms especially for the determination of pathogenic bacteria toxins and is expected to generate research interests for further studies and applications.

Targeted and non-targeted unexpected food contaminants analysis by LC/HRMS: Feasibility study on rice

A widely applicable analytical LC/HRMS method based on ion source optimization, data treatment optimization on rice matrix was developed. The effects of key parameters of ion source, and their interactions on ESI response were studied on HPLC-QTOF. Compared with center points, 40% and 20% increase of response factors in the positive and negative mode can be achieved by ion source optimization, respectively. Data processing strategies inspired from metabolomics and multi-targeted analysis were compared and developed using case and control rice samples. Highly automated workflow using XCMS achieved highest mass accuracy, highest detection rate of 96% for 5 μg/kg in a non-targeted way. A clear distinction between the control and contaminated samples by PCA and PLS-DA was also achieved by this workflow using XCMS, even for the concentration of 5 μg/kg.

Application of a multivariate analysis method for non-target screening detection of persistent transformation products during the cork boiling wastewater treatment

Cork boiling wastewater is a very complex mixture of naturally occurring compounds leached and partially oxidized during the boiling cycles. The effluent generated is recalcitrant and could cause a significant environmental impact. Moreover, if this untreated industrial wastewater enters a municipal wastewater treatment plant it could hamper or reduce the efficiency of most activated sludge degradation processes. Despite the efforts to treat the cork boiling wastewater for reusing purposes, is still not well-known how safe these compounds (original compounds and oxidation by-products) will be. The purpose of this work was to apply an HPLC-high resolution mass spectrometry method and subsequent non-target screening using a multivariate analysis method (PCA), to explore relationships between samples (treatments) and spectral features (masses or compounds) that could indicate changes in formation, degradation or polarity, during coagulation/flocculation (C/F) and photo-Fenton (PhF). Although, most of the signal intensities were reduced after the treatment line, 16 and 4 new peaks were detected to be formed after C/F and PhF processes respectively. The use of this non-target approach showed to be an effective strategy to explore, classify and detect transformation products during the treatment of an unknown complex mixture.

Chemometric Assessment of Chromatographic Methods for Herbal Medicines Authentication and Fingerprinting

Nowadays, increasingly more individuals turn to supplementation of the diet with herbal medicines and many such products are marketed lately. Thus the problem that this article focuses on is that these products are not subjected to rigorous quality control like synthetic drugs are, which rises a constant debate whether the supplements actually contain the herb or mixture of herbs that the manufacturer claims they do. As a solution, micellar electrokinetic chromatography and high performance liquid chromatography were investigated in order to fingerprint and authenticate herbal medicines. For this purpose, minimal sample pre-treatment was applied to several fruit based herbal medicines, which were compared with the ethanolic extract of the respective fruit. The holistic evaluation of the electropherograms and chromatograms was made by using appropriate chemometric tools, such as principal component analysis (PCA), cluster analysis and a combination of PCA and linear discriminant analysis (PCA-LDA). The results suggest that the developed method was able to successfully discriminate between different herbal medicines, based on their raw material content. Moreover, this simple and efficient methodology might also be used for routine screening and authenticity control of different products and could be implemented in any quality control laboratory.

Correlation between experimental data of protonation of aromatic compounds at (+) atmospheric pressure photoionization and theoretically calculated enthalpies

RATIONALE

The theoretical enthalpy calculated from the overall protonation reaction (electron transfer plus hydrogen transfer) in positive-mode (+) atmospheric-pressure photoionization (APPI) was compared with experimental results for 49 aromatic compounds. A linear relationship was observed between the calculated ΔH and the relative abundance of the protonated peak. The parameter gives reasonable predictions for all the aromatic hydrocarbon compounds used in this study.

METHODS

A parameter is devised by combining experimental MS data and high-level theoretical calculations. A (+) APPI Q Exactive Orbitrap mass spectrometer was used to obtain MS data for each solution. B3LYP exchange-correlation functions with the standard 6-311+G(df,2p) basis set was used to perform density functional theory (DFT) calculations.

RESULTS

All the molecules with ΔH <0 kcal/mol for the overall protonation reaction with toluene clusters produced protonated ions, regardless of the desolvation temperature. For molecules with ΔH >0, molecular ions were more abundant at typical APPI desolvation temperatures (300°C), while the protonated ions became comparable or dominant at higher temperatures (400°C). The toluene cluster size was an important factor when predicting the ionization behavior of aromatic hydrocarbon ions in (+) APPI.

CONCLUSIONS

The data used in this study clearly show that the theoretically calculated reaction enthalpy (ΔH) of protonation with toluene dimers can be used to predict the protonation behavior of aromatic compounds. When compounds have a negative ΔH value, the types of ions generated for aromatic compounds could be very well predicted based on the ΔH value. The ΔH can explain overall protonation behavior of compounds with ΔH values >0. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.

A Nontargeted UHPLC-HRMS Metabolomics Pipeline for Metabolite Identification: Application to Cardiac Remote Ischemic Preconditioning

In recent years, the number of investigations based on nontargeted metabolomics has increased, although often without a thorough assessment of analytical strategies applied to acquire data. Following published guidelines for metabolomics experiments, we report a validated nontargeted metabolomics strategy with pipeline for unequivocal identification of metabolites using the MSMLS molecule library. We achieved an in-house database containing accurate m/z values, retention times, isotopic patterns, full MS, and MS/MS spectra. A UHPLC-HRMS Q-Exactive method was developed, and experimental variations were determined within and between 3 experimental days. The extraction efficiency as well as the accuracy, precision, repeatability, and linearity of the method were assessed, the method demonstrating good performances. The methodology was further blindly applied to plasma from remote ischemic pre-conditioning (RIPC) rats. Samples, previously analyzed by targeted metabolomics using completely different protocol, analytical strategy, and platform, were submitted to our analytical pipeline. A combination of multivariate and univariate statistical analyses was employed. Selection of putative biomarkers from OPLS-DA model and S-plot was combined to jack-knife confidence intervals, metabolites' VIP values, and univariate statistics. Only variables with strong model contribution and highly statistical reliability were selected as discriminated metabolites. Three biomarkers identified by the previous targeted metabolomics study were found in the current work, in addition to three novel metabolites, emphasizing the efficiency of the current methodology and its ability to identify new biomarkers of clinical interest, in a single sequence. The biomarkers were identified to level 1 according to the metabolomics standard initiative and confirmed by both RPLC and HILIC-HRMS.

Metabolic profile modifications in milk after enrofloxacin administration studied by liquid chromatography coupled with high resolution mass spectrometry

High resolution accurate mass spectrometry (HRMS) operating in full scan MS mode was used in the search and identification of metabolites in raw milk from cows medicated with enrofloxacin. Data consisting of m/z features were taken throughout the entire chromatogram of milk samples from medicated animals and were compared with blank samples. Twenty six different compounds were identified. Some of them were attributed to structures related to enrofloxacin while others were dipeptides or tripeptides. Additionally, enrofloxacin was administered in a controlled treatment for three days. Milk was collected daily from the first day of treatment and until four days after in the search for the identified compounds. The obtained data were chemometrically treated by Principal Component Analysis. Samples were classified by this method into three different groups corresponding to days 1-2, day 3 and days 4-7 considering the different concentration profile evolution of metabolites during the days studied. Tentative metabolic pathways were designed to rationalize the presence of the newly identified compounds.

Liquid chromatography-quadrupole time of flight tandem mass spectrometry-based targeted metabolomic study for varietal discrimination of grapes according to plant sterols content

Grapevine and derived products are rich in a wide range of compounds and its quality mainly depends on its metabolites, as a result of viticulture practices. Plant sterols, also called phytosterols (PS), are secondary metabolites regarded as bioactive substance present in grape berries and other plant-based food. The present study deals with a metabolomic approach focusing on phytosterols family in six varieties of Rioja grapes (Cabernet Sauvignon, Tempranillo, Graciano, Garnacha, White Garnacha and Viura), in order to find significant differences among them. Liquid chromatography- mass spectrometry with a quadrupole-time of flight mass analyzer (LC-QTOF) was used to find as many metabolites as possible in the different grape berry fractions, and using statistics to help finding significant clustering of the metabolic profile of pulp, peel and seeds in relation to the variety. The best chromatographic and detection conditions were achieved by gas phase ionization via atmospheric pressure chemical ionization (APCI) in positive mode. Furthermore, analysis with electrospray (ESI) is also needed for phytosterol derivatives confirmation. Putative compounds of interest in the analyzed samples were found by an automated compound extraction algorithm (Molecular Feature Extraction, MFE) and an initial differential expression from the data was created with the aid of commercial software. Once the data were collected, the results were filtered, aligned and normalized, and evaluating applying one-way analysis of variance (ANOVA) with a 95% significance level. For sample class prediction, partial least square-discriminant analysis (PLS-DA) is used as a supervised pattern recognition method and excellent separation among the grape varieties is shown. An overall accuracy of 93.3% (pulp samples), 100.0% (peel) or 96.7% (seeds) in discriminating between grape varieties was achieved when comparing the different fractions. In general, 7 PS derivatives were identified with ID scores higher than 84%.

Mass spectrometry based chemical imaging of foods

Chemical imaging based on mass spectrometry is an emerging technology which has opened opportunities for fundamental research in food science.

Application of single-stage Orbitrap mass spectrometry and differential analysis software to nontargeted analysis of contaminants in dog food: detection, identification, and quantification of glycoalkaloids

The objective of this study was to perform a preliminary investigation of the nontargeted search and quantitative capabilities of a single-stage Exactive High-Resolution Mass Spectrometer (HRMS). To do this, the instrument and its associated software performed a non-targeted search for deleterious substances in a dog food sample suspected of causing gastrointestinal problems in dogs. A single-stage Orbitrap/high-performance liquid chromatography method and differential expression analysis software (Sieve) was used to detect and identify, and subsequently quantify, nontargeted compounds occurring only in the suspect dog food sample. When combined with an online database (ChemSpider), a preliminary identification of one of the nontargeted compounds was determined to be potato glycoalkaloids. The diagnostic product ion ratios and quantitative data accuracy generated by the single-stage Orbitrap MS were shown to be similar to results obtained using a triple quadrupole LC-MS/MS. Additionally, the ability of the single-stage Orbitrap instrument to provide precursor and product ion accurate masses and isotope patterns was also investigated.

Potential of mass spectrometry metabolomics for chemical food safety

This review aims to describe the most significant applications of mass spectrometry-based metabolomics in the field of chemical food safety. A particular discussion of all the different analytical steps involved in the metabolomics workflow (sample preparation, mass spectrometry analytical platform and data processing) will be addressed.

Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).

A new approach for plasma (xeno)metabolomics based on solid-phase extraction and nanoflow liquid chromatography-nanoelectrospray ionisation mass spectrometry

Current metabolite profiling methods based on liquid chromatography-mass spectrometry (LC-MS) platforms do not detect many of the components present at trace concentrations in extracts of plasma due to their low ionisation efficiency or to interference from highly abundant compounds. Nanoflow LC-nanospray MS platforms, which are commonly used in proteomics, could overcome these limitations and significantly increase analytical sensitivity and coverage of the plasma (xeno)metabolome (i.e., metabolites and xenobiotics), but require small injection volumes (<0.5μL). In this study, we developed sample preparation methods to remove ion suppressive phospholipids and concentrate remaining components of the plasma (xeno)metabolome in order to analyse sub-microliter volumes of plasma extracts for nanoflow ultra-high-performance liquid chromatography-nanoelectrospray ionisation-time-of-flight mass spectrometry (nUHPLC-nESI-TOFMS). These methods use phospholipid filtration plates in combination with polymeric or mixed mode exchange solid-phase extraction (SPE). The phospholipid filtration plates removed >94% of the predominant phospholipid/lysophospholipid species from plasma, whilst absolute recoveries of 63 selected (xeno)metabolites from spiked plasma were generally between 60 and 104%. After a further SPE step, recoveries of test compounds were between 50 and 81%. Studies revealed that both the sample preparation methodology and nUHPLC-nESI-TOFMS analyses gave acceptable repeatability. A qualitative comparison of SPE methods revealed that sample concentration by either polymer or mixed mode ion-exchange SPE gave comprehensive metabolite coverage of plasma extracts, but the use of cation exchange SPE significantly increased detection of many cationic compounds in the sample extracts. Method detection limits for steroid, eicosanoid and bile metabolites were <1.0ng/mL plasma and for pharmaceutical contaminants were between 0.01 and 30ng/mL plasma. Comparison of the phospholipid removal/cation exchange SPE and the classical protein precipitation (PPT) sample preparation methodologies revealed that both methods detected the same range of (xeno)metabolites. However, unlike PPT extracts, the SPE preparations allowed direct injection of more concentrated plasma extracts onto the nUHPLC-nESI-TOFMS platform without blockage of the nanocolumn or nanospray, thus resulting in a wider coverage of the (xeno)metabolome. This is the first work to demonstrate the significantly enhanced sensitivity arising from the use of concentrated SPE sample preparations and direct nUHPLC-nESI-TOFMS analysis for untargeted profiling of plasma samples and constitutes a step forward for identifying mixtures of chemical stressors accumulated in blood as well as the disruption of key metabolite pathways in the same sample.