Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.Scientific Contribution FlavorMiner is an advanced machine learning (ML)-based tool designed to predict molecular flavor features with high accuracy and efficiency, addressing the complexity of food metabolomics. By leveraging robust algorithmic combinations paired with mathematical representations FlavorMiner achieves high predictive performance. Applied to cocoa metabolomics, FlavorMiner demonstrated its capacity to extract meaningful insights, showcasing its versatility for flavor analysis across diverse food products. This study underscores the transformative potential of ML in accelerating flavor biochemistry research, offering a scalable solution for the food and beverage industry.

The production of fine-flavor cocoa represents a promising avenue to enhance socioeconomic development in Colombia and Latin America. Premium chocolate is obtained through a post-harvesting process, which relies on semi-standardized techniques. The change in the metabolic profile during cocoa processing considerably impacts flavor and nutraceutical properties of the final product. Understanding this impact considering both volatiles and non-volatile compounds is crucial for process and product re-engineering of cocoa post-harvesting. Consequently, this work studied the metabolic composition of cocoa liquor by untargeted metabolomics and lipidomics. This approach offered a comprehensive view of cocoa biochemistry, considering compounds associated with bioactivity and flavor in cocoa liquor. Their variations were traced back over the cocoa processing (i.e., drying, and roasting), highlighting their impact on flavor development and the nutraceutical properties. These results represent the basis for future studies aimed to re-engineer cocoa post-harvesting considering the variation of key flavor and bioactive compounds over processing.

Tomatoes show diverse phytochemical attributes that contribute to their nutritive and health values. This study comprehensively dissects primary and secondary metabolite profiles of seven tomato varieties. UHPLC-qTOF-MS assisted molecular networking was used to monitor 206 metabolites, 30 of which were first-time to be reported. Flavonoids, as valuable antioxidants, were enriched in light-colored tomatoes (golden sweet, sun gold, and yellow plum) versus high tomatoside A, an antihyperglycemic saponin, in cherry bomb and red plum varieties. UV–Vis analysis revealed similar results with a strong absorbance corresponding to rich phenolic content in light varieties. GC–MS unveiled monosaccharides as the main contributors to samples’ segregation, found abundant in San Marzano tomato accounting for its sweet flavor. Fruits also demonstrated potential antioxidant activities in correlation to their flavonoids and phospholipids. This work provides a complete map of tomatoes’ metabolome heterogeneity for future breeding programs and a comparative approach utilizing different metabolomic platforms for tomato analysis.

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

M. oleifera known as “miracle tree” is increasingly used in nutraceuticals for the reported health effects and nutritional value of its leaves. This study presents the first metabolome profiling of M. oleifera leaves of African origin using different solvent polarities via HR-UPLC/MS based molecular networking followed by multivariate data analyses for samples classification. 119 Chemicals were characterized in both positive and negative modes belonging to 8 classes viz. phenolic acids, flavonoids, peptides, fatty acids/amides, sulfolipids, glucosinolates and carotenoids. New metabolites i.e., polyphenolics, fatty acids, in addition to a new class of sulfolipids were annotated for the first time in Moringa leaves. In vitro anti-inflammatory and anti-aging bioassays of the leaf extracts were assessed and in correlation to their metabolite profile via multivariate data analyses. Kaempferol, quercetin and apigenin-O/C-glycosides, fatty acyl amides and carotenoids appeared crucial for biological activities and leaves origin discrimination.

With a favored taste and various bioactivities, coffee has been consumed as a daily beverage worldwide. The current study presented a multi-faceted comparative metabolomics approach dissecting commercially available coffee products in the Middle East region for quality assessment and functional food purposes using NMR and GC/MS platforms. NMR metabolites fingerprinting led to identification of 18 metabolites and quantification (qNMR) of six prominent markers for standardization purposes. An increase of β-ethanolamine (MEA) reported for the first time, 5-(hydroxymethyl) furfural (5-HMF), concurrent with a reduction in chlorogenic acid, kahweol, and sucrose levels post roasting as revealed using multivariate data analyses (MVA). The diterpenes kahweol and cafestol were identified in green and roasted Coffea arabica, while 16-O-methyl cafestol in roasted C. robusta. Moreover, GC/MS identified a total of 143 metabolites belonging to 15 different chemical classes, with fructose found enriched in green C. robusta versus fatty acids abundance, i.e., palmitic and stearic acids in C. arabica confirming NMR results. These potential results aided to identify novel quality control attributes, i.e., ethanolamine, for coffee in the Middle East region and have yet to be confirmed in other coffee specimens.

The impact of cocoa lipid content on chocolate quality has been extensively described. Nevertheless, few studies have elucidated the cocoa lipid composition and their bioactive properties, focusing only on specific lipids. In the present study the lipidome of fine-flavor cocoa fermentation was analyzed using LC-MS-QTOF and a Machine Learning model to assess potential bioactivity was developed. Our results revealed that the cocoa lipidome, comprised mainly of fatty acyls and glycerophospholipids, remains stable during fine-flavor cocoa fermentations. Also, several Machine Learning algorithms were trained to explore potential biological activity among the identified lipids. We found that K-Nearest Neighbors had the best performance. This model was used to classify the identified lipids as bioactive or non-bioactive, nominating 28 molecules as potential bioactive lipids. None of these compounds have been previously reported as bioactive. Our work is the first untargeted lipidomic study and systematic effort to investigate potential bioactivity in fine-flavor cocoa lipids.

Saffron is a spice revered for its unique flavor and health attributes often subjected to fraudulence. In this study, molecular networking as a visualization tool for UPLC/MS dataset of saffron and its common substitutes i.e. safflower and calendula (n = 21) was employed for determining genuineness of saffron and detecting its common substitutes i.e. safflower and calendula. Saffron was abundant in flavonol-O-glycosides and crocetin esters versus richness of flavanones/chalcones glycosides in safflower and cinnamates/terpenes in calendula. OPLS-DA identified differences in UPLC/MS profiles of different saffron accessions where oxo-hydroxy-undecenoic acid-O-hexoside was posed as saffron authentication marker and aided in discrimination between Spanish saffron of high quality from its inferior grade i.e. Iranian saffron along with crocetin di-O-gentiobiosyl ester and kaempferol-O-sophoroside. Kaempferol-O-neohesperidoside and N,N,N,-p-coumaroyl spermidine were characteristic safflower metabolites, whereas, calendulaglycoside C and di-O-caffeoyl quinic acid were unique to calendula. UV/VIS fingerprint spectral regions of picrocrocin (230–260 nm) and crocin derivatives (400–470 nm) were posed as being discriminatory of saffron authenticity and suggestive it can replace UPLC/MS in saffrom quality determination.

Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.

AbstractWe report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the “omics” age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website (https://caismd.indiayouth.info/) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.