Sweetness prediction of natural compounds

ChemSweet: An AI-driven computational platform for next-gen sweetener discovery

Nowadays, the overconsumption of artificial sweeteners and their related adverse health impacts have proposed an urgent need to develop safe and healthy alternatives. Herein, we introduce ChemSweet, an AI-based platform for the rapid discovery of potential sweet molecules (http://chemsweet.ddai.tech) with the consideration of their physicochemical properties, sweetness profile, and health risks at the same time. Machine learning prediction models of four important physicochemical and four toxicity properties were established and integrated with the platform to evaluate the candidate molecules' biosafety and stability during the processing processes. Then, a new sweet taste prediction system was developed which ensures the sweet evaluation of six specific kinds of sweeteners. To facilitate the practical application of ChemSweet, the SuperNatural database was integrated for the rational screening of promising new sweeteners. We successfully identified 294 potential sweeteners that simultaneously meet the multiple anticipated criteria. We believe that ChemSweet will serve as a useful tool for identifying safe and healthy sweeteners while reducing the timeframe and high experimental costs.

Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development

The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies with AI, researchers can explore and develop new flavor substances in a digital environment, saving time and resources. More and more research will use AI and big data to enhance product flavor, improve product quality, meet consumer needs, and drive the industry toward a smarter and more sustainable future. In this review, we elaborate on the mechanisms of flavor recognition and their potential impact on nutritional regulation. With the increase of data accumulation and the development of internet information technology, food flavor databases and food ingredient databases have made great progress. These databases provide detailed information on the nutritional content, flavor molecules, and chemical properties of various food compounds, providing valuable data support for the rapid evaluation of flavor components and the construction of screening technology. With the popularization of AI in various fields, the field of food flavor has also ushered in new development opportunities. This review explores the mechanisms of flavor recognition and the role of AI in enhancing food flavor analysis through high-throughput omics data and screening technologies. AI algorithms offer a pathway to scientifically improve product formulations, thereby enhancing flavor and customized meals. Furthermore, it discusses the safety challenges of integrating AI into the food flavor industry.

Effect and mechanism of green and aldehyde aroma compounds from sweet orange on sucrose sweetness perception

At present, there are relatively few studies on the influence of green aroma and aldehyde aroma compounds on the sweetness perception of sucrose. This study examined the effects of 11 aroma compounds from sweet orange, characterized by green and aldehyde flavors, on the sweetness of a 5 % sucrose solution. Using artificial sensory analysis and electronic tongue technology, it was found that most aromatic compounds can inhibit sweetness perception, and the inhibitory effect of trans-2-decenoaldehyde is the most significant. The mechanism of inhibition was explored through molecular simulation, revealing that the binding free energy of molecular docking was greater than -5.9 kcal/mol. Further molecular dynamics analysis showed that compared with the T1R2/T1R3 sucrose binary system, the addition of aroma substances reduced the number of hotspot residues involved in protein ligand binding, and did not enhance the binding ability of ligand proteins, indicating an inhibitory effect.

The insights into sour flavor and organic acids in alcoholic beverages

Alcoholic beverages have developed unique flavors over millennia, with sourness playing a vital role in their sensory perception and quality. Organic acids, as crucial flavor compounds, significantly impact flavor. This paper reviews the sensory attribute of sour flavor and key organic acids in alcoholic beverages. Regarding sour flavor, research methods include both static and dynamic sensory approaches and summarize the interaction of sour flavor with aroma, taste, and mouthfeel. In addition, this review focuses on identifying key organic acids, including sample extraction, chromatography, olfactometry/taste, and mass spectrometry. The key organic acids in alcoholic beverages, such as wine, Baijiu, beer, and Huangjiu, and their primary regulatory methods are discussed. Finally, future avenues for the exploration of sour flavor and organic acids by coupling machine learning, database, sensory interactions and electroencephalography are suggested. This systematic review aims to enhance understanding and serve as a reference for further in-depth studies on alcoholic beverages.

Health effects of synthetic additives and the substitution potential of plant-based additives

The growth of the world population and the rapid industrialization of food have led to food producers' increased reliance on food additives. While food additives offer numerous conveniences and advantages in food applications, the potential risks associated with synthetic additives remain a significant concern. This report examines the current status of safety assessment and toxicity studies of common synthetic additives, including flavorings (sweeteners and flavor enhancers), colorants, preservatives (antimicrobials and antioxidants), and emulsifiers. The report also examines recent advances in promising plant-based alternative additives in terms of active ingredients, sensory properties, potential health benefits, food application challenges, and their related technologies (edible coatings/films and nanoencapsulation technologies), providing valuable references and insights for the sustainable development of food additives.

Exploring Bitter and Sweet: The Application of Large Language Models in Molecular Taste Prediction

The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.

The Molecular Theory of Sweet Taste: Revisit, Update, and Beyond

The molecular origin of the sweet taste is still elusive. Herein, the canonical AH-B-X theory of sweet taste is extended by resurveying various sweeteners, which provides deeper insights into an analogous intramolecular connectivity pattern of both glucophores in sweeteners and their interaction counterparts in sweet taste receptor TAS1R2/TAS1R3: electrostatic complementarity and topochemical compatibility. Furthermore, their complementary interaction is elaborately illustrated, accounting for the common molecular feature of eliciting sweetness. Moreover, it highlights that multiple glucophores in a topological system synergistically mediate the elicitation and performance of sweetness. This perspective presents a meaningful framework for the structure-activity relationship-based molecular design and modification of sweeteners and sheds light on the mechanism of molecular evolution of TAS1R2s/TAS1R3s. The link between palatability of sweeteners and harmony relationships between their structural components via stereochemistry and network has significant implications to illuminate the underlying mechanisms by which nature designs chemical reactions to elicit the most important taste.

Classification of tastants: A deep learning based approach

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.

Rapid Kinetic Interactions of Sugar and Sugar Alcohol with Sweet Taste Receptors on Live Cells Using Stopped-Flow Spectroscopy

The subjective measurement of the dynamic perception of sweetness is a problem in food science. Herein, the rapid interactions of sugars and sugar alcohols with sweet taste receptors on living cells on a millisecond timescale were studied via stopped-flow fluorescence spectroscopy. According to the rapid-kinetic parameters, sweeteners were divided into two groups. Sweeteners in group I disrupted the hydrogen bond network structure of water, and the apparent rate constant (kobs) was in the range of 0.45-0.6 s-1. Sweeteners in group II promoted the hydrogen bond formation of water, and the kobs was mostly in the range of 0.6-0.75 s-1. For most sweeteners, the kobs of cell responses was negatively correlated with the apparent specific volume of sweeteners. The differences in the cellular responses may be attributed to the disturbance in the water structure. Experimental results showed that the kinetic parameters of sweet cell responses reflected the dynamic perception of sweetness. Rapid kinetics, solution thermodynamic analysis, and water structure analysis enriched the physicochemical study of the sweetness mechanism and can be used to objectively evaluate the dynamic perception of sweetness.

Classification-based machine learning approaches to predict the taste of molecules: A review

The capacity to discriminate safe from dangerous compounds has played an important role in the evolution of species, including human beings. Highly evolved senses such as taste receptors allow humans to navigate and survive in the environment through information that arrives to the brain through electrical pulses. Specifically, taste receptors provide multiple bits of information about the substances that are introduced orally. These substances could be pleasant or not according to the taste responses that they trigger. Tastes have been classified into basic (sweet, bitter, umami, sour and salty) or non-basic (astringent, chilling, cooling, heating, pungent), while some compounds are considered as multitastes, taste modifiers or tasteless. Classification-based machine learning approaches are useful tools to develop predictive mathematical relationships in such a way as to predict the taste class of new molecules based on their chemical structure. This work reviews the history of multicriteria quantitative structure-taste relationship modelling, starting from the first ligand-based (LB) classifier proposed in 1980 by Lemont B. Kier and concluding with the most recent studies published in 2022.

Insights into Chemical Structure-Based Modeling for New Sweetener Discovery

The search for novel, natural, high-sweetness, low-calorie sweeteners remains open and challenging. In the present study, the structure-based machine learning modeling and sweetness recognition mechanism were investigated to assist this process. It was found that whether or not a compound was sweet was closely related to molecular connectivity and composition (the number of hydrogen bond acceptors and donors), tpsaEfficiency, structural complexity, and shape (nAtomP and Fsp3). While the relative sweetness of sweet compounds was more determined by the molecular properties (tpsaEfficiency and Log P), structural complexity and composition (nAtomP and ATSm 1). The built machine learning models exhibited very good performance for classifying the sweet/non-sweet compounds and predicting the relative sweetness of the compounds. Moreover, a specific binding pocket was found for sweet compounds, and the sweet compounds mainly interacted with the VFT domain of the T1R2-T1R3 through hydrogen bonds. In addition, the results indicated that among the sweet compounds, those that were sweeter bound to the VFT domain stronger than those that had low sweetness. This study provides very useful information for developing new sweeteners.

Data-Driven Elucidation of Flavor Chemistry

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

3D-QSAR studies on the structure-bitterness analysis of citrus flavonoids

Despite their important bioactivities, the unpleasant bitter taste of citrus derived flavonoids limits their applications in the food industry, and the structure-bitterness relationship of flavonoids is still far from clear. In this study, 26 flavonoids were characterized by their bitterness threshold and their common skeleton using sensory evaluation and molecular superposition, respectively. The quantitative conformational relationship of the structure-bitterness of flavonoids was explored using 3D-QSAR based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). The results showed that increases of a hydrogen bond donor at A-5 or B-3', a bulky group at A-8, or an electron-withdrawing group at B-4' would enhance the bitterness of flavonoids. The bitterness of some flavonoids was predicted and evaluated, and the results were similar to the bitter intensity of the counterparts from the 3D-QSAR and contour plots, confirming the validation of 3D-QSAR. This study explains the theory of the structure-bitterness relationship of flavonoids, by showing potential information for understanding the bitterness in citrus flavonoids and developing a debittering process.

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

Taste GPCRs and their ligands

Taste GPCRs are expressed in taste buds on the tongue and play a key role in food choice and consumption. They are also expressed extra-orally, with various physiological roles that are currently under study. Unraveling the roles of these receptors relies on the knowledge of their ligands. Combining sensory, cell-based and computational approaches enabled the discovery of numerous agonists and several antagonists. Here we provide a short overview of taste receptor families, main recent methods for ligands discovery, and current sources of information about known ligands. The future directions that are likely to impact the taste GPCR field include focus on ligand interactions with naturally occurring polymorphisms, as well as harnessing the power of CryoEM and of multiple signaling readout techniques.

A survey on computational taste predictors

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00217-022-04044-5.

A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling

Nowadays, computational approaches have drawn more and more attention when exploring the relationship between sweetness and chemical structure instead of traditional experimental tests. In this work, we proposed a novel multi-layer sweetness evaluation system based on machine learning methods. It can be used to evaluate sweet properties of compounds with different chemical spaces and categories, including natural, artificial, carbohydrate, non-carbohydrate, nutritive and non-nutritive ones, suitable for different application scenarios. Furthermore, it provided quantitative predictions of sweetness. In addition, sweetness-related chemical basis and structure transforming rules were obtained by using molecular cloud and matched molecular pair analysis (MMPA) methods. This work systematically improved the data quality, explored the best machine learning algorithm and molecular characterizing strategy, and finally obtained robust models to establish a multi-layer prediction system (available at: https://github.com/ifyoungnet/ChemSweet). We hope that this study could facilitate food scientists with efficient screening and precise development of high-quality sweeteners.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach

In this study, we present a framework comprises of several independent modules which are built upon data based (structure activity relationship and classification model) and structure (molecular docking) based for identifying possible sweeteners from a vast database of natural molecules. A large database, Universal Natural Products Database (UNPD) consisting of 213,210 compounds was screened using the developed framework. At first, 10,184 molecules structurally similar to the known sweeteners were identified in the database. Further, 1924 molecules from these screened molecules were classified as sweet molecules. The shortlisted 1354 molecules were subjected to ADMET analysis. Finally, 60 molecules were arrived at with no toxicity and acceptable oral bioavailability as potential sweetener candidates. Further, molecular docking of these molecules on sweet taste receptor performed to obtain their binding energy, binding sites and correlation with sweetness index. The developed framework offers a convenient route for fast screening of molecules prior to synthesis and testing.

Sugar Reduction in Dairy Food: An Overview with Flavoured Milk as an Example

Owing to the public health concern associated with the consumption of added sugar, the World Health Organization recommends cutting down sugar in processed foods. Furthermore, due to the growing concern of increased calorie intake from added sugar in sweetened dairy foods, the present review provides an overview of different types and functions of sugar, various sugar reduction strategies, and current trends in the use of sweeteners for sugar reduction in dairy food, taking flavoured milk as a central theme where possible to explore the aforementioned aspects. The strength and uniqueness of this review are that it brings together all the information on the available types of sugar and sugar reduction strategies and explores the current trends that could be applied for reducing sugar in dairy foods without much impact on consumer acceptance. Among different strategies for sugar reduction, the use of natural non-nutritive sweeteners (NNSs), has received much attention due to consumer demand for natural ingredients. Sweetness imparted by sugar can be replaced by natural NNSs, however, sugar provides more than just sweetness to flavoured milk. Sugar reduction involves multiple technical challenges to maintain the sensory properties of the product, as well as to maintain consumer acceptance. Because no single sugar has a sensory profile that matches sucrose, the use of two or more natural NNSs could be an option for food industries to reduce sugar using a holistic approach rather than a single sugar reduction strategy. Therefore, achieving even a small sugar reduction can significantly improve the diet and health of an individual.

Structure-dependent effects of sweet and sweet taste affecting compounds on their sensorial properties

A reduction in sugar consumption is desirable from a health point of view. However, the sensory profiles of alternative sweet tasting compounds differ from sucrose regarding their temporal profile and undesired side tastes, reducing consumers' acceptance. The present study describes a sensory characterization of a variety of sweet and sweet taste affecting compounds followed by a comparison of similarity to sucrose and a multivariate regression analysis to investigate structural determinants and possible interactions for the temporal profile of the sweetness and side-tastes. The results of the present study suggest a pivotal role for the number of ketones, aromatic rings, double bonds and the M LogP in the temporal profile of sweet and sweet taste affecting compounds. Furthermore, interactions between aggregated physicochemical descriptors demonstrate the complexity of the sensory response, which should be considered in future models to predict a comprehensive sensory profile of sweet and sweet taste affecting compounds.

Novel scaffold of natural compound eliciting sweet taste revealed by machine learning

Sugar replacement is still an active issue in the food industry. The use of structure-taste relationships remains one of the most rational strategy to expand the chemical space associated to sweet taste. A new machine learning model has been setup based on an update of the SweetenersDB and on open-source molecular features. It has been implemented on a freely accessible webserver. Cellular functional assays show that the sweet taste receptor is activated in vitro by a new scaffold of natural compounds identified by the in silico protocol. The newly identified sweetener belongs to the lignan chemical family and opens a new chemical space to explore.

An in silico structural approach to characterize human and rainbow trout estrogenicity of mycotoxins: Proof of concept study using zearalenone and alternariol

The mycotoxins zearalenone and alternariol may contaminate food and feed raising toxicological concerns due to their estrogenicity. Inter-species differences in their toxicokinetics and toxicodynamics may occur depending on evolution of taxa-specific traits. As a proof of principle, this manuscript investigates the comparative toxicodynamics of zearalenone, its metabolites (alpha-zearalenol and beta-zearalenol), and alternariol with regards to estrogenicity in humans and rainbow trout. An in silico structural approach based on docking simulations, pharmacophore modeling and molecular dynamics was applied and computational results were analyzed in comparison with available experimental data. The differences of estrogenicity among species of zearalenone and its metabolites have been structurally explained. Also, the low estrogenicity of alternariol in trout has been characterized here for the first time. This approach can provide a powerful tool for the characterization of interspecies differences in mycotoxin toxicity for a range of protein targets and relevant compounds for the food- and feed-safety area.

Mechanistic role of plant-based bitter principles and bitterness prediction for natural product studies II: prediction tools and case studies

The first part of this chapter provides an overview of computer-based tools (algorithms, web servers, and software) for the prediction of bitterness in compounds. These tools all implement machine learning (ML) methods and are all freely accessible. For each tool, a brief description of the implemented method is provided, along with the training sets and the benchmarking results. In the second part, an attempt has been made to explain at the mechanistic level why some medicinal plants are bitter and how plants use bitter natural compounds, obtained through the biosynthetic process as important ingredients for adapting to the environment. A further exploration is made on the role of bitter natural products in the defense mechanism of plants against insect pest, herbivores, and other invaders. Case studies have focused on alkaloids, terpenoids, cyanogenic glucosides and phenolic derivatives.

RQ3, A Natural Rebaudioside D Isomer, Was Obtained from Glucosylation of Rebaudioside A Catalyzed by the CGTase Toruzyme 3.0 L

In this study, a monoglucosyl rebaudioside A product was isolated from the mixture of glucosylated rebaudioside A obtained from the most reported and industrial used cyclodextrin glycosyl transferase, Toruzyme 3.0 L (CGTase, Toruzyme 3.0 L). The molecular structure of the monoglucosyl rebaudioside A was characterized using LC-MS/MS and methylation analysis combined with 1D and 2D NMR, indicating that it is 13-[(2-O-(3-α-O-D-glucopyranosyl)-β-D-glucopyranosyl-3-O-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid β-D-glucopyranosyl ester (also known as RQ3, which naturally exists in Stevia extract as an isomer of rebaudioside D). This study may help to further understand the reaction mechanism of glucosylation of steviol glycoside assisted by Toruzyme 3.0 L in the aspect of molecule linkage pattern, and also benefit the application of the glucosylated rebaudiosides.

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules

The dichotomy of sweet and bitter tastes is a salient evolutionary feature of human gustatory system with an innate attraction to sweet taste and aversion to bitterness. A better understanding of molecular correlates of bitter-sweet taste gradient is crucial for identification of natural as well as synthetic compounds of desirable taste on this axis. While previous studies have advanced our understanding of the molecular basis of bitter-sweet taste and contributed models for their identification, there is ample scope to enhance these models by meticulous compilation of bitter-sweet molecules and utilization of a wide spectrum of molecular descriptors. Towards these goals, our study provides a structured compilation of bitter, sweet and tasteless molecules and state-of-the-art machine learning models for bitter-sweet taste prediction (BitterSweet). We compare different sets of molecular descriptors for their predictive performance and further identify important features as well as feature blocks. The utility of BitterSweet models is demonstrated by taste prediction on large specialized chemical sets such as FlavorDB, FooDB, SuperSweet, Super Natural II, DSSTox, and DrugBank. To facilitate future research in this direction, we make all datasets and BitterSweet models publicly available, and present an end-to-end software for bitter-sweet taste prediction based on freely available chemical descriptors.

Triterpenoids from Quercus petraea: Identification in Wines and Spirits and Sensory Assessment

Eight new triterpenoids (1-8), the known genin (9), and two known functionalized triterpenoids (10 and 11) were isolated from a Quercus petraea heartwood extract. The structures of the new compounds were unequivocally elucidated using HRESIMS and 1D/2D NMR experiments. Sensory analyses were performed in a non-oaked wine on the pure compounds 1-11. Except compounds 1 and 11, all molecules exhibited a sweet taste at 5 mg/L that was particularly intense for compounds 3 and 9. Using LC-HRMS, compounds 1-11 were observed in an oak wood extract and in oaked red wine and cognac. They were also semiquantified in several samples of sessile ( Q. petraea) and pedunculate ( Q. robur) oak wood extract. All compounds were found in quantities significantly higher in sessile than in pedunculate oak wood. These results support the hypothesis of their contribution to the increase in sweetness during oak aging and show that they can be used as chemical markers to identify the species of oak used for cooperage.

e-Sweet: A Machine-Learning Based Platform for the Prediction of Sweetener and Its Relative Sweetness

Artificial sweeteners (AS) can elicit the strong sweet sensation with the low or zero calorie, and are widely used to replace the nutritive sugar in the food and beverage industry. However, the safety issue of current AS is still controversial. Thus, it is imperative to develop more safe and potent AS. Due to the costly and laborious experimental-screening of AS, in-silico sweetener/sweetness prediction could provide a good avenue to identify the potential sweetener candidates before experiment. In this work, we curate the largest dataset of 530 sweeteners and 850 non-sweeteners, and collect the second largest dataset of 352 sweeteners with the relative sweetness (RS) from the literature. In light of these experimental datasets, we adopt five machine-learning methods and conformational-independent molecular fingerprints to derive the classification and regression models for the prediction of sweetener and its RS, respectively via the consensus strategy. Our best classification model achieves the 95% confidence intervals for the accuracy (0.91 ± 0.01), precision (0.90 ± 0.01), specificity (0.94 ± 0.01), sensitivity (0.86 ± 0.01), F1-score (0.88 ± 0.01), and NER (Non-error Rate: 0.90 ± 0.01) on the test set, which outperforms the model (NER = 0.85) of Rojas et al. in terms of NER, and our best regression model gives the 95% confidence intervals for the R²(test set) and ΔR² [referring to |R²(test set)- R²(cross-validation)|] of 0.77 ± 0.01 and 0.03 ± 0.01, respectively, which is also better than the other works based on the conformation-independent 2D descriptors (e.g., 2D Dragon) according to R²(test set) and ΔR². Our models are obtained by averaging over nineteen data-splitting schemes, and fully comply with the guidelines of Organization for Economic Cooperation and Development (OECD), which are not completely followed by the previous relevant works that are all on the basis of only one random data-splitting scheme for the cross-validation set and test set. Finally, we develop a user-friendly platform “e-Sweet” for the automatic prediction of sweetener and its corresponding RS. To our best knowledge, it is a first and free platform that can enable the experimental food scientists to exploit the current machine-learning methods to boost the discovery of more AS with the low or zero calorie content.

PhytoMolecularTasteDB: An integrative database on the "molecular taste" of Indian medicinal plants

PhytoMolecularTaste database (PhytoMolecularTasteDB) described in the present work is related to the article “Main phytocompunds׳ tastes: a better predictor for the ethnopharmacological activities of medicinal plant than the phytochemical class?” (Dragos and Gilca, 2018) [1]. It includes a comprehensive list of plant derived tastants, as well as details on the “phyto-molecular taste” (PMT) (the combination of tastes resulted from the main tastants found in a medicinal plant). To collect the data, we searched publications in various databases and journals by using relevant keywords. Wherever necessary, manual search of lacking information was also performed in several books. We then extracted the reported phytoconstituents and PMT of all the ayurvedic medicinal plants included in DB. Data were compiled in Excel. In total, PhytoMolecularTasteDB includes 431 ayurvedic medicinal plants, 94 EPAs, 223 phytochemical classes, and 438 plant-derived tastants.

Bitter and sweet tasting molecules: It's complicated

"Bitter" and "sweet" are frequently framed in opposition, both functionally and metaphorically, in regard to affective responses, emotion, and nutrition. This oppositional relationship is complicated by the fact that some molecules are simultaneously bitter and sweet. In some cases, a small chemical modification, or a chirality switch, flips the taste from sweet to bitter. Molecules humans describe as bitter are recognized by a 25-member subfamily of class A G-protein coupled receptors (GPCRs) known as TAS2Rs. Molecules humans describe as sweet are recognized by a TAS1R2/TAS1R3 heterodimer of class C GPCRs. Here we characterize the chemical space of bitter and sweet molecules: the majority of bitter compounds show higher hydrophobicity compared to sweet compounds, while sweet molecules have a wider range of sizes. Importantly, recent evidence indicates that TAS1Rs and TAS2Rs are not limited to the oral cavity; moreover, some bitterants are pharmacologically promiscuous, with the hERG potassium channel, cytochrome P450 enzymes, and carbonic anhydrases as common off-targets. Further focus on polypharmacology may unravel new physiological roles for tastant molecules.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

A QSTR-Based Expert System to Predict Sweetness of Molecules

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.