Differentiating organically and conventionally grown oregano using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), headspace gas chromatography with flame ionization detection (headspace-GC-FID), and flow injection mass spectrum (FIMS) fingerprints combined with multivariate data analysis

Uncovering the Counterfeit: A study of whiskey authenticity through volatile organic compound fingerprinting, aroma and color sensory analysis

This study presents a method employing gas chromatography coupled with mass spectrometry and headspace solid-phase microextraction (HS-SPME-GC-MS), supplemented with chemometrics (Soft independent modelling of class analogies - SIMCA), to analyze volatile organic compound (VOCs) profiles in suspect whiskey samples. Furthermore, a sensory analysis of aroma and color was conducted with a panel of 52 non-trained volunteers to evaluate their ability to discriminate and preference for counterfeit whiskeys. The HS-SPME-GC-MS method successfully distinguished 41 seized samples from authentic beverages. Interestingly, sensory analysis revealed that panelists could differentiate between counterfeit and authentic samples with a reference standard but did not consistently show a preference for aroma. In some cases, there was even a preference for the color of counterfeit whiskeys. The findings suggest that sensorial tests alone may not effectively distinguish counterfeit from authentic whiskeys, especially for non-expert consumers, highlighting the need for analytical instrumentation methods in fraud detection.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study

Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.

Potential Mechanisms of Imidacloprid-Induced Neurotoxicity in Adult Rats with Attempts on Protection Using Origanum majorana L. Oil/Extract: In Vivo and In Silico Studies

Imidacloprid (IMI) insecticide is rapidly metabolized in mammals and contributes to neurotoxicity via the blocking of nicotinic acetylcholine receptors, as in insects. Origanum majorana retains its great antioxidant potential in both fresh and dry forms. No data is available on the neuroprotective effect of this plant in laboratory animals. In this context, aerial parts of O. majorana were used to prepare the essential oil (OMO) and methanol extract (OME). The potential neuroprotective impact of both OMO and OME against IMI-induced neurotoxicity in rats was explored. Forty-two rats were divided into 6 groups, with 7 rats in each one. Rats were daily administered the oral treatments: normal saline, OMO, OME, IMI, IMI + OMO, and IMI + OME. Our results revealed the identification of 55 components in O. majorana essential oil, most belonging to the oxygenated and hydrocarbon monoterpenoid group. Moreover, 37 constituents were identified in the methanol extract, mostly phenolics. The potent neurotoxic effect of IMI on rats was confirmed by neurobehavioral and neuropathological alterations and a reduction of both acetylcholine esterase (AchE) activity and dopamine (DA), serotonin (5HT), and γ-aminobutyric acid (GABA) levels in the brain. Exposure of rats to IMI elevates the malondialdehyde (MDA) levels and reduces the antioxidant capacity. IMI could upregulate the transcription levels of nuclear factor-κB (NF-κB), interleukin-1 β (IL-1β), and tumor necrosis factor (TNF-α) genes and express strong caspase-3 and inducible nitric oxide synthase (iNOS) immunostaining in most examined brain areas. On the other hand, rats coadministered OMO or OME with IMI showed a marked improvement in all of the studied toxicological parameters. In conclusion, cotreatment of O. majorana extracts with IMI can protect against IMI neurotoxicity via their potent antioxidant, anti-inflammatory, and anti-apoptotic effects. Thus, we recommend a daily intake of O. majorana to protect against insecticide's oxidative stress-mediated neuroinflammatory stress and apoptosis. The molecular docking study of linalool, rosmarinic acid, γ-terpene, and terpene-4-ol justify the observed normalization of the elevated iNOS and TNF-α levels induced after exposure to IMI.

Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research

Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.

A spectrum-effect based method for screening antibacterial constituents in Niuhuang Shangqing Pill using comprehensive two-dimensional liquid chromatography

To investigate and screen the active antibacterial constituents of Niuhuang Shangqing Pill (NSP), the current study developed a two-dimensional liquid chromatography (2DLC) method combining microcalorimetry technique. 60% ethanol extracts from 10 batches of different commercial NSP samples were analyzed and their chemical fingerprint were developed by the comprehensive 2DLC system of Shimadzu Nexera X2. Anti-streptococcus pneumoniae (SP) constituents were determined by microcalorimetry. Thermal kinetic parameters of the SP thermogram affected by 60% ethanol extracts from 10 NSP samples were analyzed by principal component analysis. Spectrum-effect correlation between comprehensive 2DLC fingerprint and the antibacterial activity were analyzed by orthogonal partial least squares (OPLS) and orthogonal partial least squares discriminant analysis (OPLS-DA). Findings showed that peak X1 (unknown), X9 (aloe-emodin), X10 (baicalein), X11 (unknown), X14 (wogonin), X15 (glycyrrhizic acid) and X17 (unknown) are the relevant components that are in positive correlation with inhibitory rate. Regarding inhibitory rate, X17 is the most powerful one, followed by X14, X15, X10, X11, X1 and X9, suggesting that compound X17, wogonin, glycyrrhizic acid and baicalein are the major active antibacterial components of NSP. The current method employing 2DLC with microcalorimetry technique proposes a new insight for screening and identifying antibacterial components in complex herbal formula.

FIA-HRMS fingerprinting subjected to chemometrics as a valuable tool to address food classification and authentication: Application to red wine, paprika, and vegetable oil samples

The rise of food fraud practices, affecting a wide variety of goods and their specific characteristics (e.g., quality or geographical origin), demands rapid high-throughput analytical approaches to ensure consumers protection. In this context, this study assesses flow injection analysis coupled to high-resolution mass spectrometry (FIA-HRMS), using a fingerprinting approach and combined with chemometrics, to address four food authentication issues: (i) the geographical origin of three Spanish red wines, (ii) the geographical origin of three European paprikas, (iii) the distinction of olive oil from other vegetable oils and (iv) the assessment of its quality category. In each case, negative and positive ionisation FIA-HRMS fingerprints, and two different data fusion strategies, were evaluated. After external validation, excellent classification accuracies were reached. Moreover, high-resolution mass spectrometry (HRMS) allowed sample matrices characterisation by the putative identification of the most common ions.

Building a Resilient, Sustainable, and Healthier Food Supply Through Innovation and Technology

The modern food supply faces many challenges. The global population continues to grow and people are becoming wealthier, so the food production system must respond by creating enough high-quality food to feed everyone with minimal damage to our environment. The number of people suffering or dying from diet-related chronic diseases, such as obesity, diabetes, heart disease, stroke, and cancer, continues to rise, which is partly linked to overconsumption of highly processed foods, especially high-calorie or rapidly digestible foods. After falling for many years, the number of people suffering from starvation or malnutrition is rising, and thishas been exacerbated by the global COVID-19 pandemic. The highly integrated food supply chains that spread around the world are susceptible to disruptions due to policy changes, economic stresses, and natural disasters, as highlighted by the recent pandemic. In this perspective article, written by members of the Editorial Committee of the Annual Review of Food Science and Technology, we highlight some of the major challenges confronting the modern food supply chain as well as how innovations in policy and technology can be used to address them. Pertinent technological innovations include robotics, machine learning, artificial intelligence, advanced diagnostics, nanotechnology, biotechnology, gene editing, vertical farming, and soft matter physics. Many of these technologies are already being employed across the food chain by farmers, distributors, manufacturers, and consumers to improve the quality, nutrition, safety, and sustainability of the food supply. These innovations are required to stimulate the development and implementation of new technologies to ensure a more equitable, resilient, and efficient food production system. Where appropriate, these technologies should be carefully tested before widespread implementation so that proper risk-benefit analyses can be carried out. They can then be employed without causing unforeseen adverse consequences. Finally, it is important to actively engage all stakeholders involved in the food supply chain throughout the development and testing of these new technologies to support their adoption if proven safe and effective.

Chemometric tools for food fraud detection: The role of target class in non-targeted analysis

The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000-4000 cm^-1 of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.

Random Forest Processing of Direct Analysis in Real-Time Mass Spectrometric Data Enables Species Identification of Psychoactive Plants from Their Headspace Chemical Signatures

The United Nations Office on Drugs and Crime has designated several "legal highs" as "plants of concern" because of the dangers associated with their increasing recreational abuse. Routine identification of these products is hampered by the difficulty in distinguishing them from innocuous plant materials such as foods, herbs, and spices. It is demonstrated here that several of these products have unique but consistent headspace chemical profiles and that multivariate statistical analysis processing of their chemical signatures can be used to accurately identify the species of plants from which the materials are derived. For this study, the headspace volatiles of several species were analyzed by direct analysis in real-time high-resolution mass spectrometry (DART-HRMS). These species include Althaea officinalis, Calea zacatechichi, Cannabis indica, Cannabis sativa, Echinopsis pachanoi, Lactuca virosa, Leonotis leonurus, Mimosa hositlis, Mitragyna speciosa, Ocimum basilicum, Origanum vulgare, Piper methysticum, Salvia divinorum, Turnera diffusa, and Voacanga africana. The results of the DART-HRMS analysis revealed intraspecies similarities and interspecies differences. Exploratory statistical analysis of the data using principal component analysis and global t-distributed stochastic neighbor embedding showed clustering of like species and separation of different species. This led to the use of supervised random forest (RF), which resulted in a model with 99% accuracy. A conformal predictor based on the RF classifier was created and proved to be valid for a significance level of 8% with an efficiency of 0.1, an observed fuzziness of 0, and an error rate of 0. The variables used for the statistical analysis processing were ranked in terms of the ability to enable clustering and discrimination between species using principal component analysis-variable importance of projection scores and RF variable importance indices. The variables that ranked the highest were then identified as m/z values consistent with molecules previously identified in plant material. This technique therefore shows proof-of-concept for the creation of a database for the detection and identification of plant-based legal highs through headspace analysis.

Distinguishing Smilax glabra and Smilax china rhizomes by flow-injection mass spectrometry combined with principal component analysis

Flow-injection mass spectrometry (FIMS) coupled with a chemometric method is proposed in this study to profile and distinguish between rhizomes of Smilax glabra (S. glabra) and Smilax china (S. china). The proposed method employed an electrospray-time-of-flight MS. The MS fingerprints were analyzed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with the aid of SIMCA software. Findings showed that the two kinds of samples perfectly fell into their own classes. Further predictive study showed desirable predictability and the tested samples were successfully and reliably identified. The study demonstrated that the proposed method could serve as a powerful tool for distinguishing between S. glabra and S. china.

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods

INTRODUCTION

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers.

OBJECTIVE

To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites.

METHODS

GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data.

RESULTS

GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set.

CONCLUSIONS

GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd.

Triacylglycerol compositions of sunflower, corn and soybean oils examined with supercritical CO2 ultra-performance convergence chromatography combined with quadrupole time-of-flight mass spectrometry

A supercritical CO₂ ultra-performance convergence chromatography (UPC²) system was utilized with a quadrupole time-of-flight mass spectrometry (Q-TOF MS) to examine the triacylglycerol compositions of sunflower, corn and soybean oils. UPC² provided an excellent resolution and separation for the triacylglycerols, while the high performance Q-TOF MS system was able to provide the molecular weight and fragment ions information for triacylglycerol compound characterization. A total of 33 triacylglycerols were identified based on their elementary compositions and MS² fragment ion profiles, and their levels in the three oils were estimated. The combination of UPC² and Q-TOF MS may determine triacylglycerol compositions for oils and fats, and provide sn-position information for fatty acids, which may be important for food nutritional value and stability.

A comprehensive strategy to detect the fraudulent adulteration of herbs: The oregano approach

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Two tier strategy proposed to detect oregano fraud.

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FT-IR screening and HR-LC-MS confirmatory methods developed.

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Unique biomarkers discovered in adulterants by HR-LC-MS.

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Chemometric calibration models generated.

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24% of oregano samples tested in UK/Ireland were found to be adulterated.

Fraud in the global food supply chain is becoming increasingly common due to the huge profits associated with this type of criminal activity. Food commodities and ingredients that are expensive and are part of complex supply chains are particularly vulnerable. Both herbs and spices fit these criteria perfectly and yet strategies to detect fraudulent adulteration are still far from robust. An FT-IR screening method coupled to data analysis using chemometrics and a second method using LC-HRMS were developed, with the latter detecting commonly used adulterants by biomarker identification. The two tier testing strategy was applied to 78 samples obtained from a variety of retail and on-line sources. There was 100% agreement between the two tests that over 24% of all samples tested had some form of adulterants present. The innovative strategy devised could potentially be used for testing the global supply chains for fraud in many different forms of herbs.

Chemotaxonomic Studies of Nine Gentianaceae Species from Western China Based on Liquid Chromatography Tandem Mass Spectrometry and Fourier Transform Infrared Spectroscopy

INTRODUCTION

Gentianaceae species which widely occur all over the world are used as folk medicine and raw food material with bitter properties. Although comparative analysis on metabolites in several Gentianaceae species has been reported, metabolic similarities used for chemotaxonomic studies are not yet clear.

OBJECTIVE

To systematically characterise the variations of holistic metabolome and characteristic metabolites (iridoid glycosides and phenols) in nine Gentianaceae species from western China.

METHODOLOGY

Fourier transform infrared (FT-IR) spectroscopy was applied to determine the variations of holistic metabolome. A targeted metabolic profiling using liquid chromatography with tandem mass spectrometry (LC-MS/MS) was established for determination of seven characteristic metabolites and identification of their derivatives. Both FT-IR and LC-MS/MS data were subjected to chemometrics analysis for exploring variations in iridoid glycosides and phenols within these species.

RESULTS

Holistic metabolome in genera Gentiana and Swertia was largely different. Diversity of the biosynthetic pathway of iridoid glycosides was also observed in these species. Principal component analysis (PCA) showed a clear separation according to infrageneric classifications of genus Gentiana. Some secondary metabolites, such as mangiferin, rhodenthoside A-C, isoorientin, isovitexin, amarogentin, and swertianolin would serve as potential chemotaxonomic markers to differentiate Gentianaceae species. Furthermore, the accumulation of the six major metabolites seems to depend on geographical regions in Sect. Monopodiae and Sect. Cruciata.

CONCLUSIONS

The combination of LC-MS/MS and FT-IR would provide some potential evidence on chemotaxonomic studies of Gentianaceae. Copyright © 2016 John Wiley & Sons, Ltd.