Non-targeted Approach Research Articles

Non-targeted detection of food adulteration using an ensemble machine-learning model

Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an example, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious samples; this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety.

Comparable outcomes from long and short read random sequencing of total RNA for detection of pathogens in chicken respiratory samples.

Co-infections of avian species with different RNA viruses and pathogenic bacteria are often misdiagnosed or incompletely characterized using targeted diagnostic methods, which could affect the accurate management of clinical disease. A non-targeted sequencing approach with rapid and precise characterization of pathogens should help respiratory disease management by providing a comprehensive view of the causes of disease. Long-read portable sequencers have significant potential advantages over established short-read sequencers due to portability, speed, and lower cost. The applicability of short reads random sequencing for direct detection of pathogens in clinical poultry samples has been previously demonstrated. Here we demonstrate the feasibility of long read random sequencing approaches to identify disease agents in clinical samples. Experimental oropharyngeal swab samples (n = 12) from chickens infected with infectious bronchitis virus (IBV), avian influenza virus (AIV) and Mycoplasma synoviae (MS) and field-collected clinical oropharyngeal swab samples (n = 11) from Kenyan live bird markets previously testing positive for Newcastle disease virus (NDV) were randomly sequenced on the MinION platform and results validated by comparing to real time PCR and short read random sequencing in the Illumina MiSeq platform. In the swabs from experimental infections, each of three agents in every RT-qPCR-positive sample (Ct range 19-34) was detectable within 1 h on the MinION platform, except for AIV one agent in one sample (Ct = 36.21). Nine of 12 IBV-positive samples were assigned genotypes within 1 h, as were five of 11 AIV-positive samples. MinION relative abundances of the test agent (AIV, IBV and MS) were highly correlated with RT-qPCR Ct values (R range-0.82 to-0.98). In field-collected clinical swab samples, NDV (Ct range 12-37) was detected in all eleven samples within 1 h of MinION sequencing, with 10 of 11 samples accurately genotyped within 1 h. All NDV-positive field samples were found to be co-infected with one or more additional respiratory agents. These results demonstrate that MinION sequencing can provide rapid, and sensitive non-targeted detection and genetic characterization of co-existing respiratory pathogens in clinical samples with similar performance to the Illumina MiSeq.

Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography Mass Spectrometry (Unified-HILIC/AEX/MS): A Single-Run Method for Comprehensive and Simultaneous Analysis of Polar Metabolome.

One of the technical challenges in the field of metabolomics is the development of a single-run method to detect the full complement of polar metabolites in biological samples. However, an ideal method to meet this demand has not yet been developed. Herein, we proposed a simple methodology that enables the comprehensive and simultaneous analysis of polar metabolites using unified-hydrophilic-interaction/anion-exchange liquid chromatography mass spectrometry (unified-HILIC/AEX/MS) with a polymer-based mixed amines column composed of methacrylate-based polymer particles with primary, secondary, tertiary, and quaternary amines as functional groups. The optimized unified-HILIC/AEX/MS method is composed of two consecutive chromatographic separations, HILIC-dominant separation for cationic, uncharged, and zwitterionic polar metabolites [retention times (RTs) = 0-12.8 min] and AEX-dominant separation for polar anionic metabolites (RTs = 12.8-26.5 min), by varying the ratio of acetonitrile to 40 mM ammonium bicarbonate solution (pH 9.8). A total of 400 polar metabolites were analyzed simultaneously through a combination of highly efficient separation using unified-HILIC/AEX and remarkably sensitive detection using multiple reaction monitoring-based triple quadrupole mass spectrometry (unified-HILIC/AEX/MS/MS). A nontargeted metabolomic approach using unified-HILIC/AEX high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) also provided more comprehensive information on polar metabolites (3242 metabolic features) in HeLa cell extracts than the conventional HILIC/HRMS method (2068 metabolic features). Our established unified-HILIC/AEX/MS/MS and unified-HILIC/AEX/HRMS methods have several advantages over conventional techniques, including polar metabolome coverage, throughput, and accurate quantitative performance, and represent potentially useful tools for in-depth studies on metabolism and biomarker discovery.

Improving Aroma Complexity with Hanseniaspora spp.: Terpenes, Acetate Esters, and Safranal

Hanseniaspora vineae and Hanseniaspora opuntiae are apiculate yeasts normally found on the skins of ripe grapes and at the beginning of alcoholic fermentation. Several studies have reported that these species can provide interesting sensory characteristics to wine by contributing high levels of acetate esters and can increase the mouthfeel and body of wines. The present work aims to evaluate the use of these two species sequentially with Saccharomyces cerevisiae to improve the sensory profile of Albillo Mayor white wines. The fermentations were carried out in triplicate in 150 L stainless steel barrels. At the end of the alcoholic fermentation polysaccharides, colour, and an extensive study of the aromatic profiles were measured. Results showed up to 1.55 times higher content of 2-phenylethanol in H. opuntiae wines and up to three times higher concentration of fermentative esters in H. vineae wines than in the controls. Interestingly, it should be noted that the compound safranal was identified only in the H. vineae wines. These results indicated that the species studied are an interesting bio-tool to improve the aromatic profile of Albillo Mayor white wines. A novel non-targeted NMR-based metabolomics approach is proposed as a tool for optimising wine productions with standard and sequential fermentation schemes using apiculate yeast strains due to its discriminant capacity to differentiate fine features between wine samples from the identical geographical origin and grape variety but diverse fermentations or vintages.

Multiomic Data Integration in the Analysis of Drought-Responsive Mechanisms in Quercus ilex Seedlings.

The integrated analysis of different omic layers can provide new knowledge not provided by their individual analysis. This approach is also necessary to validate data and reveal post-transcriptional and post-translational mechanisms of gene expression regulation. In this work, we validated the possibility of applying this approach to non-model species such as Quercus ilex. Transcriptomics, proteomics, and metabolomics from Q. ilex seedlings subjected to drought-like conditions under the typical summer conditions in southern Spain were integrated using a non-targeted approach. Two integrative approaches, PCA and DIABLO, were used and compared. Both approaches seek to reduce dimensionality, preserving the maximum information. DIABLO also allows one to infer interconnections between the different omic layers. For easy visualization and analysis, these interconnections were analyzed using functional and statistical networks. We were able to validate results obtained by analyzing the omic layers separately. We identified the importance of protein homeostasis with numerous protease and chaperones in the networks. We also discovered new key processes, such as transcriptional control, and identified the key function of transcription factors, such as DREB2A, WRKY65, and CONSTANS, in the early response to drought.

Non-Targeted Chemical Characterization of JUUL-Menthol-Flavored Aerosols Using Liquid and Gas Chromatography

The aerosol constituents generated from JUUL Menthol pods with 3.0% and 5.0% nicotine by weight (Me3 and Me5) are characterized by a non-targeted approach, which was developed to detect aerosol constituents that are not known to be present beforehand or that may be measured with targeted methods. Three replicates from three production batches (n = 9) were aerosolized using two puffing regimens (intense and non-intense). Each of the 18 samples were analyzed by gas chromatography electron ionization mass spectrometry and by liquid chromatography electrospray ionization high-resolving power mass spectrometry. All chemical constituents determined to differ from control were identified and semi-quantified. To have a complete understanding of the aerosol constituents and chemistry, each chemical constituent was categorized into one of five groups: (1) flavorants, (2) harmful and potentially harmful constituents, (3) leachables, (4) reaction products, and (5) chemical constituents that were unable to be identified or rationalized (e.g., chemical constituents that could not be categorized in groups (1–4). Under intense puffing, 74 chemical constituents were identified in Me3 aerosols and 68 under non-intense puffing, with 53 chemical constituents common between both regimens. Eighty-three chemical constituents were identified in Me5 aerosol using an intense puffing regimen and seventy-five with a non-intense puffing regimen, with sixty-two chemical constituents in common. Excluding primary constituents, reaction products accounted for the greatest number of chemical constituents (approximately 60% in all cases, ranging from about 0.05% to 0.1% by mass), and flavorants—excluding menthol—comprised the second largest number of chemical constituents (approximately 25%, ranging consistently around 0.01% by mass). The chemical constituents detected in JUUL aerosols were then compared to known constituents from cigarette smoke to determine the relative chemical complexities and commonalities/differences between the two. This revealed (1) a substantial decrease in the chemical complexity of JUUL aerosols vs. cigarette smoke and (2) that there are between 55 (Me3) and 61 (Me5) unique chemical constituents in JUUL aerosols not reported in cigarette smoke. Understanding the chemical complexity of JUUL aerosols is important because the health effects of combustible cigarette smoke are related to the combined effect of these chemical constituents through multiple mechanisms, not just the effects of any single smoke constituent.

An UPLC-Q-TOF/MS-Based Analysis of the Differential Composition of Dendrobium officinale in Different Regions.

Dendrobium officinale (D. officinale) is a valuable traditional Chinese herbal medicine with high commercial value. In Chinese Pharmacopoeia (Ch.P., 2020 edition), the quality of D. officinale is mainly evaluated by its polysaccharide content. However, varying growth and production conditions, such as cultivation environment, origin, harvesting process, or processing methods, resulting in highly variable yields, quality, and composition. The aim of this study was to investigate whether the content of secondary metabolites in D. officinale from different origins is consistent with the polysaccharide content. The results showed that the polysaccharide content and pass rate were ranked as GX > AH > GZ > YN. Based on the nontargeted metabolomics approach, we searched for differential components in 22 different regions of D. officinale, including amides, bibenzyls, disaccharide, flavonoids, organic nitrogenous compounds, and phenolic glycosides. The overall expression was opposite to the polysaccharide, and the most expressed was YN, followed by GZ, AH, and GX. These results indicated that the current quality standard for evaluating the quality of D. officinale by polysaccharide content alone is imperfect, and small molecule compounds need to be included as quality markers.

Plasma-Based Metabolomics Profiling of High-Risk Human Papillomavirus and their Emerging Roles in the Progression of Cervical Cancer.

High-risk human papillomavirus (HR-HPV) is the main etiological factor for cervical cancer. Accumulating evidence has suggested the active role of metabolites in the initiation and progression of cancers. This study explored the plasma metabolic profiles of HPV-16 positive (HPV16 (+)), HPV-18 positive (HPV18 (+)), and HPV negative (CTL) individuals using a nontargeted metabolomics approach. C8 ceramide-1-Phosphate (d18 : 1/8 : 0) was found to inhibit cervical cancer cell proliferation and migration in vitro, evidenced by CCK8 experiments, a cell migration test, RT-qPCR, and western blotting. The underlying mechanism demonstrated that C8 inhibited proliferation and migration in cervical cancer cells via the MAPK/JNK1 signaling pathway. These findings may contribute to the clinical treatment of HR-HPV-induced cervical cancer by intervening in its initiation and progression.

Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia.

Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and non-COVID-19 pneumonia.

Studying the Fenton treatment of polycyclic aromatic compounds in a highly contaminated soil with different modifiers by high resolution mass spectrometry

Remediation of polycyclic aromatic compounds (PAHs) contaminated soil is highly challenging, because of the complex soil matrix and immense number of various pollutants. Especially higher aromatic species are not water soluble and cannot be removed by microorganisms. One way to make them accessible for biological treatment is to make them water soluble through an oxidation reaction. Here, the Fenton reaction is a method of choice. The Fenton reaction in a soil that is highly contaminated with various polyaromatic hydrocarbons is studied in a non-target approach using high resolution mass spectrometry as method for detection with addition of modifiers (methanol, ethanol and toluene) to make the contaminants accessible for the reaction. The results show that the most effective oxidation was achieved using toluene as a modifier, which leads to a significant decrease of polyaromatic hydrocarbons (PAHs) and polyaromatic nitrogen heterocycles (PANHs) in the soil. After the toluene-based Fenton reaction, the number of oxygen atoms in the products increased up to O18, which makes them more soluble in water and allows for a better removal from the soil matrix. Similar hydrocarbon distributions were detected on the molecular level in Soxhlet extracted soil phase and toluene phase, while smaller PAHs with double bond equivalents (DBE) ≤ 38 were transferred into the water phase.

Untargeted Metabolomics Reveals New Markers of Food Processing for Strawberry and Apple Purees.

In general, food processing and its conditions affect nutrients, bioactive compounds, and sensory characteristics of food products. This research aims to use a non-targeted metabolomics approach based on UPLC-ESI-QTOF-MS to determine how fruit processing can affect the metabolic profile of fruits and, through a comprehensive metabolic analysis, identify possible markers to assess their degree of processing. The present study uses a real case from the food industry to evaluate markers of the processing of strawberry and apple purees industrially elaborated with different processing techniques and conditions. The results from the multivariate analysis revealed that samples were grouped according to the type of processing, evidencing changes in their metabolic profiles and an apparent temperature-dependent effect. These metabolic profiles showed changes according to the relevance of thermal conditions but also according to the exclusively cold treatment, in the case of strawberry puree, and the pressure treatment, in the case of apple puree. After data analysis, seven metabolites were identified and proposed as processing markers: pyroglutamic acid, pteroyl-D-glutamic acid, 2-hydroxy-5-methoxy benzoic acid, and 2-hydroxybenzoic acid β-d-glucoside in strawberry and di-hydroxycinnamic acid glucuronide, caffeic acid and lysoPE(18:3(9Z,12Z,15Z)/0:0) in apple purees. The use of these markers may potentially help to objectively measure the degree of food processing and help to clarify the controversial narrative on ultra-processed foods.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines.

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Metabolomic analysis of cooperative adaptation between Co-cultured Lacticaseibacillus casei Zhang and Lactiplantibacillus plantarum P8

Microbial secondary metabolites have long been considered as potential sources of natural active molecules due to their rich chemical diversity and extensive biological activities. Increasing evidence shows that coculture from probiotic strains had synergistic effect promoting the production of bioactive molecules, and secondary metabolites that had higher stability and/or functional performance. The purpose of this study was to explore the interaction between Lacticaseibacillus casei Zhang (LcZ) and Lactiplantibacillus plantarum P8 (P8) during co-culture using non-targeted metabonomics approach. Results showed stronger growth in the LcZ and P8 co-culture (LcZ + P8) compared with either bacterium alone. Multivariate analysis revealed that significant differences in metabolic profiles between strains, and by and within the same strain at different growth stages. In addition, the alteration of cell and media metabolites during cell growth was strongly associated with energy production. Moreover, some new bioactive substances and substances with higher expression levels that might have beneficial effect on human health were identified in LcZ + P8.This is the first study to systematically identify a symbiotic interaction of metabolites in an ecosystem established by P8 and LcZ. The results provide novel insight into the system-level cooperative adaptation between strains and have implications for the further development of microbial natural products.

Impact of Weissella cibaria BYL4.2 and its supernatants on Penicillium chrysogenum metabolism.

Lactic acid bacteria (LAB) can produce a vast spectrum of antifungal metabolites to inhibit fungal growth. The purpose of this study was to elucidate the antifungal effect of isolated Weissella cibaria BYL4.2 on Penicillium chrysogenum, the antifungal activity of W. cibaria BYL4.2 against P. chrysogenum was evaluated by the superposition method, results showed that it had obviously antifungal activity against P. chrysogenum. Studying the probiotic properties of BYL4.2 and determining it as beneficial bacteria. Furtherly, different treatments were carried out to characterize the antifungal activity of cell-free supernatant (CFS) produced by W. cibaria BYL4.2, and it was shown that the CFS was pH-dependent, partly heat-sensitive, and was not influenced by proteinaceous treatment. The CFS of W. cibaria BYL4.2 was analyzed by high-performance liquid chromatography (HPLC) and found the highest content of lactic acid. Screening of metabolic markers by a non-targeted metabolomics approach based liquid chromatography-mass spectrometry (LC-MS). The results speculated that organic acid especially detected D-tartaric acid was the main antifungal substance of CFS, which could cause the down-regulation of metabolites in the ABC transporters pathway, thereby inhibiting the growth of P. chrysogenum. Therefore, this study may provide important information for the inhibitory mechanism of W. cibaria BYL4.2 on P. chrysogenum, and provide a basis for further research on the antifungal effect of Weissella.

Associations of perfluoroalkyl substances (PFAS) with non-targeted metabolomics across two independent cohorts of elderly individuals

BACKGROUND AND AIM: Perfluoroalkyl substances (PFAS) have been linked to adverse health effects including increased blood cholesterol levels and hepatic toxicity. Alterations in metabolism and oxidative stress are among the hypothesized mechanisms by which PFAS exposure may exert effects; however, studies of associations between PFAS and metabolome are scarce. The main aim of this study was to assess associations of PFAS exposure and metabolome by using non-targeted metabolomics in two independent cohorts of elderly individuals. METHODS: In two population-based cohorts of elderly individuals from Sweden and California (the Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS, n = 901, median age 70 years]; and the California Teacher Study [CTS, n = 325, median age 69 years]), plasma/serum PFAS concentrations were determined using isotope-dilution ultra-pressure liquid chromatography (UHPLC) coupled to tandem mass spectrometry. Non-targeted metabolomics was performed using UHPLC high-resolution mass spectrometry (QTOFMS). Associations of PFAS and metabolomics signatures was assessed using a discovery-validation approach with multivariable linear regression. RESULTS: In the discovery cohort 89 annotated metabolites out of 204 could be associated with levels of five PFAS following adjustment for age, sex, and correction for multiple testing. Eleven associations could then be replicated in the CTS. The metabolites being replicated in the CTS were primarily lipid related metabolites such as lysophosphatidylcholine, phosphatidylcholine, sphingomyelin as well as amino acids such as betaine. CONCLUSIONS: Using a non-targeted metabolomics discovery-validation approach, we the previously reported associations between PFAS and lipid related metabolites and identified and replicated novel associations. Our results encourage additional studies investigating the underlying mechanisms of our findings. KEYWORDS: PFAS, PFOS, PFOA, Metabolomics, Lipid metabolism

Untargeted HPLC-MS-based metabolomics approach to reveal cocoa powder adulterations

Cocoa powder is a highly consumed product all over the world which could be substituted by cheaper raw materials resulting in food fraud. In this work, a non-targeted metabolomics approach based on the use of reversed-phase liquid chromatography coupled to high-resolution mass spectrometry was developed to carry out the characterization of cocoa powder samples adulterated, at two different levels, with carob flour, soy flour, and chicory. The sample preparation protocol and the chromatographic parameters were optimized to extract and detect the highest number of molecular features. Both non-supervised and supervised statistical methods were employed to analyze the most significant variables that gave rise to group discrimination. From the 21 and 37 significant variables analyzed in positive and negative ionization modes, respectively, a total of 20 were tentatively identified. Different families of compounds including flavonoids, fatty acids, terpenoids, lysophospholipids, and a galactolipid could be pointed out as cocoa adulteration markers.

Birth Weight and Early Postnatal Outcomes: Association with the Cord Blood Lipidome.

Being born small or large for gestational age (SGA and LGA, respectively), combined with suboptimal early postnatal outcomes, can entail future metabolic alterations. The exact mechanisms underlying such risks are not fully understood. Lipids are a highly diverse class of molecules that perform multiple structural and metabolic functions. Dysregulation of lipid metabolism underlies the onset and progression of many disorders leading to pathological states. The aim of this pilot study was to investigate the relationships between birth weight, early postnatal outcomes, and cord blood serum lipidomes. We performed a non-targeted lipidomics-based approach to ascertain differences in cord blood lipid species among SGA, LGA, and appropriate-for-GA (AGA) newborns. Moreover, we longitudinally assessed (at birth and at ages of 4 and 12 months) weight and length, body composition (DXA), and clinical parameters. We disclosed distinct cord blood lipidome patterns in SGA, LGA, and AGA newborns; target lipid species distinctly modulated in each SGA, AGA, and LGA individual were associated with parameters related to growth and glucose homeostasis. The distinct lipidome patterns observed in SGA, AGA, and LGA newborns may play a role in adipose tissue remodeling and future metabolic risks. Maternal dietary interventions may potentially provide long-term benefits for the metabolic health of the offspring.

Odorants in the organophosphorus pesticide contaminated soils：identification, prioritization, and quantification using GC-HRMS based on target and non-target screening

Odorants from the pesticide site remediation were complex and changeable, and the screening of key odorous substances was the prerequisite for the process of odor control. In the study, a novel odorants identification method based on the targeted and non-targeted approach by gas chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometer (GC Orbitrap/MS) was established to investigate the low-concentration odor pollution in three organophosphorus pesticide sites. The comprehensive odor characterization method was developed and applied in the light of olfactory threshold, flavor characteristics, molecular structure, response value, and detection rate. 98 pollutants were screened out using target analysis, and 149 substances were identified with confidence levels of at least 2 based on non-target analysis. Based on the analysis results, 42 odorants were screened out by the synthetic evaluation method for priority control, including phenols, aldehydes, ketones, esters, organic sulfide, amines, nitriles, amide, acylchlorides, ethers, heterocyclic, halogenated benzene and polycyclic aromatic hydrocarbons. The limit of detection (LOD) of 42 odorants was from 0.010 to 0.100 ng L−1, which was lower than other detection methods. This research provided a new technology for identification of odorants in pesticide sites.

Instrument comparability of non-targeted UHPLC-HRMS for wine authentication

Instrument comparability is a major prerequisite for the harmonization of non-targeted analytical approaches for food authentication. While non-targeted liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful and widely used technique in authenticity studies, inter-laboratory or instrument comparison exercises are rare. For the first time, in the present study a comparison of two LC-MS platforms using identical setups and conditions except for the mass spectrometer (Orbitrap Q Exactive™ Focus vs. Plus) was carried out in order to assess the impact of instrumental differences on the classification of grape varieties in wine. A set of 201 red and white wine samples comprising 10 grape varieties was analyzed with both platforms and subjected to multivariate statistical analysis. Using unsupervised and supervised methods, the same correlation pattern between samples was observed with both platforms. Partial Least Squares – Discriminant Analysis (PLS-DA) classification models achieved comparable accuracies in the internal validation (90–95%). The percentage of common discriminating features was about 46–69% between both platforms. The common features were used to merge data from both instruments and joint PLS-DA models produced excellent results. The prediction of data from one platform with the model from the other platform, as well as a model using data from both platforms, resulted in correct classification rates of 92–98%. Putative identification of common features was performed to determine potential marker substances for grape varieties. With this study, a first step towards harmonization of non-targeted LC-MS approaches for wine authentication is taken, advancing their potential application in official food control.

Non targeted screening of nitrogen containing disinfection by-products in formation potential tests of river water and subsequent monitoring in tap water samples.

The generation of disinfection by-products during water chlorination is a major concern in water treatment, given the potential health risks that these substances may pose. In particular, nitrogen-containing DBPs are believed to have greater toxicological significance than carbon-based DBPs. Hence, high performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS) in positive mode was employed to identify new non-volatile nitrogen containing disinfection by-products (DBPs) and to assess their presence in potable water. Nine water samples were taken in the Llobregat river, in the context of a water reuse trial, near the catchment of a drinking water treatment plant (DWTP) in 2019. River samples were disinfected with chlorine under controlled formation potential tests conditions and analysed with a non-target approach. The peak lists of raw and chlorinated samples were compared exhaustively, resulting in an extensive list of 495 DBPs that include bromine and/or chlorine atoms. 172 of these species were found frequently, in three or more chlorinated samples. The empirical formulae of these DBPs were unambiguously annotated on the basis of accurate m/z measurements, isotopic patterns and common heuristic rules. Most of the annotated species (310) contained bromide, which is consistent with the relatively high bromide content of the Llobregat basin (>0.3mg/l). Drinking water samples were taken at the outlet of the DWTP during the same sampling period. According to their analysis, a large portion of the DBPs detected after the formation potential tests do not reach real-life drinking water, which suggests that the treatment train successfully removes a significant fraction of DBP precursors. However, 131 DBPs could still be detected in the final product water. A larger sampling was carried in the Barcelona water distribution network, during six consecutive weeks, and it revealed the presence of 78 halogenated DBPs in end-consumer water, most of which were nitrogen-containing. MS/MS fragmentation and retention times were employed to tentatively suggest molecular structure for these recalcitrant DBPs.