Feasibility of Applying Untargeted Metabolomics with GC-Orbitrap-HRMS and Chemometrics for Authentication of Black Pepper (Piper nigrum L.) and Identification of Geographical and Processing Markers

Metabolomics applications for plant-based foods origin tracing, cultivars identification and processing: Feasibility and future aspects

Metabolomics, the systematic study of metabolites, is dedicated to a comprehensive analysis of all aspects of plant-based food research and plays a pivotal role in the nutritional composition and quality control of plant-based foods. The diverse chemical compositions of plant-based foods lead to variations in sensory characteristics and nutritional value. This review explores the application of the metabolomics method to plant-based food origin tracing, cultivar identification, and processing methods. It also addresses the challenges encountered and outlines future directions. Typically, when combined with other omics or techniques, synergistic and complementary information is uncovered, enhancing the classification and prediction capabilities of models. Future research should aim to evaluate all factors affecting food quality comprehensively, and this necessitates advanced research into influence mechanisms, metabolic pathways, and gene expression.

Comparison of data processing strategies using commercial vs. open-source software in GC-Orbitrap-HRMS untargeted metabolomics analysis for food authentication: thyme geographical differentiation and marker identification as a case study

Untargeted analysis of gas chromatography-high-resolution mass spectrometry (GC-HRMS) data is a key and time-consuming challenge for identifying metabolite markers in food authentication applications. Few studies have been performed to evaluate the capability of untargeted data processing tools for feature extraction, metabolite annotation, and marker selection from untargeted GC-HRMS data since most of them are focused on liquid chromatography (LC) analysis. In this framework, this study provides a comprehensive evaluation of data analysis tools for GC-Orbitrap-HRMS plant metabolomics data, including the open-source MS-DIAL software and commercial Compound Discoverer™ software (designed for Orbitrap data processing), applied for the geographical discrimination and search for thyme markers (Spanish vs. Polish differentiation) as the case study. Both approaches showed that the feature detection process is highly affected by unknown metabolites (Levels 4-5 of identification confidence), background signals, and duplicate features that must be carefully assessed before further multivariate data analysis for reliable putative identification of markers. As a result, Compound Discoverer™ and MS-DIAL putatively annotated 52 and 115 compounds at Level 2, respectively. Further multivariate data analysis allowed the identification of differential compounds, showing that the putative identification of markers, especially in challenging untargeted analysis, heavily depends on the data processing parameters, including available databases used during compound annotation. Overall, this method comparison pointed out both approaches as good options for untargeted analysis of GC-Orbitrap-HRMS data, and it is presented as a useful guide for users to implement these data processing approaches in food authenticity applications depending on their availability.

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking

The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5-117.1% accuracy surpassed 1.2-14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

High-Performance Thin-Layer Chromatography Coupled with Single Quadrupole: Application the Identification and Differentiation of Rehmanniae Radix and Its Different Processing Products from Raw Materials to Commercial Products

The authentication of ingredients in formulas is crucial yet challenging, particularly for constituents with comparable compositions but vastly divergent efficacy. Rehmanniae Radix and its derivatives are extensively utilized in food supplements, which contain analogous compositions but very distinct effects. Rehmanniae Radix, also a difficult-to-detect herbal ingredient, was chosen as a case to explore a novel HPTLC-QDa MS technique for the identification of herbal ingredients in commercial products. Through systematic condition optimization, including thin layer and mass spectrometry, a stable and reproducible HPTLC-QDa MS method was established, which can simultaneously detect oligosaccharides and iridoids. Rehmannia Radix and its processed products were then analyzed to screen five markers that could distinguish between raw and prepared Rehmannia Radix. An HPTLC-QDa-SIM method was further established for formula detection by using the five markers and validated using homemade prescriptions and negative controls. Finally, this method was applied to detect raw and prepared Rehmannia Radix in 12 commercial functional products and supplements.

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.

Geographical discrimination of Flos Trollii by GC-MS and UHPLC-HRMS-based untargeted metabolomics combined with chemometrics

For centuries, Flos Trollii has been consumed as functional tea and a folk medicine in China's north and northwest zones. The quality of Flos Trollii highly depends on the producing zones. Unfortunately, few studies have been reported on the geographical discrimination of Flos Trollii. This work comprehensively investigated Flos Trollii compounds with an integration strategy combining gas chromatography-mass spectrometry (GC-MS) and ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) with chemometrics to explore the differences between Flos Trollii obtained from various origins of China. About 71 volatile and 22 involatile markers were identified with GC-MS and UHPLC-HRMS, respectively. Geographical discrimination models were synthetically investigated based on the identified markers. The results indicated that the UHPLC-HRMS coupled with the fisher discrimination model provided the best prediction capability (>97%). This study provides a new solution for Flos Trollii discrimination.

A comparison of high- and low-resolution gas chromatography-mass spectrometry for herbal product classification: A case study with Ocimum essential oils

INTRODUCTION

Selection of marker compounds for targeted chemical analysis is complicated when considering varying instrumentation and closely related plant species. High-resolution gas chromatography-mass spectrometry (GC-MS), via orbitrap detection, has yet to be evaluated for improved marker compound selection.

OBJECTIVE

This study directly compares high- and low-resolution GC-MS for botanical maker compound selection using Ocimum tenuiflorum L. (OT) and Ocimum gratissimum L. (OG) for botanical ingredient authentication.

METHODS

The essential oils of OT and OG were collected via hydrodistillation before untargeted chemical analysis with gas chromatography coupled to single-quadrupole (GC-SQ) and orbitrap (GC-Orbitrap) detectors. The Global Natural Products Social Molecular Networking (GNPS) software was used for compound annotation, and a manual search was used to find the 41 most common Ocimum essential oil metabolites.

RESULTS

The GC-Orbitrap resulted in 1.7-fold more metabolite detection and increased dynamic range compared to the GC-SQ. Spectral matching and manual searching were improved with GC-Orbitrap data. Each instrument had differing known compound concentrations; however, there was an overlap of six compounds with higher abundance in OG than OT and three compounds with a higher abundance in OT than OG, suggesting consistent detection of the most variable compounds. An unsupervised principal component analysis (PCA) could not discern the two species with either dataset.

CONCLUSION

GC-Orbitrap instrumentation improves compound detection, dynamic range, and feature annotation in essential oil analysis. However, considering both high- and low-resolution data may improve reliable marker compound selection, as GC-Orbitrap analysis alone did not improve unsupervised separation of two Ocimum species compared to GC-SQ data.

UHPLC-QTOF-HRMS metabolomics insight on the origin and processing authentication of thyme by comprehensive fingerprinting and chemometrics

The metabolic composition of thyme, one of the most used aromatic herbs, is influenced by environmental and post-harvest processing factors, presenting the possibility of exploiting thyme fingerprint to assess its authenticity. In this study, a comprehensive UHPLC-QTOF-HRMS fingerprinting approach was applied with a dual objective: (1) tracing thyme from three regions of production (Spain, Morocco, and Poland) and (2) evaluating the metabolic differences in response to processing, considering sterilized thyme samples. Multivariate statistics reveal 37 and 33 key origin and processing differentiation compounds, respectively. The findings highlighted the remarkable "terroir" influence on thyme fingerprint, noticing flavonoids, amino acids, and peptides among the most discriminant chemical classes. Thyme sterilization led to an overall metabolite enrichment, most likely due to the facilitated compound accessibility as a result of processing. The findings provide a comprehensive metabolomics insight into the origin and processing effect on thyme composition for product traceability and quality assessment.

Fingerprinting based on gas chromatography-Orbitrap high-resolution mass spectrometry and chemometrics to reveal geographical origin, processing, and volatile markers for thyme authentication

Thyme is an aromatic herb traditionally used for food purposes due to its organoleptic characteristics and medicinal properties, which is highly susceptible to food fraud. In this study, GC-HRMS-based fingerprinting was applied for the first time to determine the geographical traceability of thyme based on different origins (Spain, Poland, and Morocco), as well as to assess its processing by comparing sterilized vs. non-sterilized thyme. Unsupervised chemometric methods (PCA and HCA) revealed a predominant influence of the geographical origin on thyme fingerprints rather than processing effects. Supervised PLS-DA and OPLS-DA were used for discrimination purposes, revealing high predictive ability for further samples (100%), and allowing the identification of differential compounds (markers). A total of 24 markers were putatively identified (13 metabolites were confirmed) belonging to different classes: monoterpenoids, diterpenoids, sesquiterpenoids, alkenylbenzenes, and other miscellaneous compounds. This study outlines the potential of combining untargeted analysis by GC-HRMS with chemometrics for thyme authenticity and traceability.

Spices Volatilomic Fingerprinting—A Comprehensive Approach to Explore Its Authentication and Bioactive Properties

Volatile organic metabolites (VOMs) present in different spices can provide distinct analytical biosignatures related to organoleptic properties and health benefits. This study aimed to establish the volatilomic fingerprint of six of the most consumed spices all over the world (saffron (Crocus sativus L.), cinnamon (Cinnamomum verum), cumin (Cuminum cyminum L.), black pepper, (Piper nigrum L.), sweet paprika (Capsicum annuum L.), and curry (a mix of different herbs and spices)). Based on headspace solid phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) analysis, this is a powerful strategy to explore and establish the spice’s volatile pattern and unravel the potential health benefits related to the most important VOMs identified in each spice. This comprehensive knowledge will help in the definition of their authenticity, while simultaneously protecting against potential frauds and adulterations. A total of 162 VOMs were identified. Semi-quantitative assessments revealed that terpenoids and sesquiterpenoids amounted to the major volatile class in the investigated spices, except for cinnamon, where carbonyl compounds are the major group. Most of the studied spices comprised key characteristics of aroma and health bioactive compounds, e.g., dihydrojuneol in saffron, cinnamaldehyde in cinnamon, cuminaldehyde in cumin and curry, and caryophyllene in black pepper. The principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) successfully discriminated the investigated spices, being α-cubebene, 3-methyl butanal, β-patchoulene and β-selinene, the most important VOMs (highest VIP’s) that contributed to its discrimination. Moreover, some VOMs have a high influence on the spice’s bioactive potential, helping to prevent certain diseases including cancer, inflammatory-related diseases, diabetes, and cardiovascular diseases.

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.

Advances in Plant Metabolomics and Its Applications in Stress and Single-Cell Biology

In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics approaches have evolved to integrate cellular processes at various levels into a systems biology knowledge base. Metabolomics plays a crucial role in molecular networking to bridge the gaps between genotypes and phenotypes. However, the greater complexity of metabolites with diverse chemical and physical properties has limited the advances in plant metabolomics. For several years, applications of liquid/gas chromatography (LC/GC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been constantly developed. Recently, ion mobility spectrometry (IMS)-MS has shown utility in resolving isomeric and isobaric metabolites. Both MS and NMR combined metabolomics significantly increased the identification and quantification of metabolites in an untargeted and targeted manner. Thus, hyphenated metabolomics tools will narrow the gap between the number of metabolite features and the identified metabolites. Metabolites change in response to environmental conditions, including biotic and abiotic stress factors. The spatial distribution of metabolites across different organs, tissues, cells and cellular compartments is a trending research area in metabolomics. Herein, we review recent technological advancements in metabolomics and their applications in understanding plant stress biology and different levels of spatial organization. In addition, we discuss the opportunities and challenges in multiple stress interactions, multi-omics, and single-cell metabolomics.

Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts

Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCS_N2 and CCS_He) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Application of an innovative metabolomics approach to discriminate geographical origin and processing of black pepper by untargeted UHPLC-Q-Orbitrap-HRMS analysis and mid-level data fusion

An untargeted metabolomics approach based on ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) fingerprinting was applied to investigate the metabolic differences of black pepper among three geographical origins (Sri Lanka, Vietnam, and Brazil) and two post-harvest processing (sterilized and non-sterilized spice). Principal component analysis (PCA) was employed to assess the overall clustering of samples, whereas supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was effectively used for discrimination purposes. OPLS-DA models were fully validated (R²Y and Q² values > 0.5) and the variable importance in projection (VIP) approach was employed to provide valuable data about differential metabolites with high discrimination potential (8 markers were putatively identified). For origin differentiation, three markers were highlighted with VIP values > 1.5 (i.e. reynosin, artabsinolide D, and tatridin B). Fatty acid derivates were the most frequent markers within the metabolites annotated for processing discrimination (e.g. 10,16-dihydroxyhexadecanoic acid and 9-hydroperoxy-10E-octadecenoic acid). Additionally, different combinations of mid-level data fusion of chromatographic-mass spectrometric techniques (UHPLC and gas chromatography coupled to HRMS) and proton nuclear magnetic resonance spectroscopy (¹H NMR) were evaluated for the first time for geographical and processing discrimination of black pepper. The NMR-UHPLC-GC mid-level fused model was preferred among the tested fusion approaches since good sample clustering and no misclassification were achieved. Enhanced correct classification rate was achieved by mid-level data fusion compared with the findings obtained for one of the individual techniques (¹H NMR fingerprinting) (from 92% to 100% of samples correctly classified). This study opens the path to new metabolomics approaches for black pepper authentication and quality control.