AdditiveChem: A comprehensive bioinformatics knowledge-base for food additive chemicals

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.

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.

Rapid screening of illegal additives in functional food using desorption electrospray ionization mass spectrometry imaging

Compounds such as Sildenafil, which bring potential health risks to consumers, have been illegally added to functional food. The public security department hopes to quickly screen for illegal additives. The quantity of seized samples is often large and their compositions are unknown; it is necessary to screen the unknown samples qualitatively and sometimes quantitatively. In this paper, a new method for rapid screening of 39 common illegal additives in six categories of functional food based on DESI-MSI technology is proposed, and the DESI-MSI library is established, which can be used for exclusive and sensitive qualitative confirmation of suspicious samples. A new carrier material that can be used for rapid qualitative detection of solid sample is discovered. The samples require simple or even no pretreatment to carry out high-resolution imaging through the imaging function of DESI-MSI. The imaging results are clear and intuitive, and can achieve fast and high-throughput qualitative identification of illegally added compounds. This method has good linearity, accuracy, precision, and little effect of matrix, so it can roughly quantify the illegal additives in functional products. Twenty-one batches of unknown samples were detected by DESI-MSI, and the positive results were confirmed by LC-MS/MS (QQQ). The results showed that the DESI-MSI method was effective and reliable. DESI-MSI with self-made database is a promising method for rapid screening of illegal additives in functional food, which can be widely used in grass-roots police stations.

Analysis of public opinion on food safety in Greater China with big data and machine learning

The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. To systematically collect and analyze public opinion on food safety in Greater China, we developed IFoodCloud, which automatically collects data from more than 3,100 public sources. Meanwhile, we constructed sentiment classification models using multiple lexicon-based and machine learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1 score achieved 0.9737, demonstrating its great predictive ability and robustness. Using IFoodCloud, we analyzed public sentiment on food safety in Greater China and the changing trend of public opinion at the early stage of the 2019 Coronavirus Disease pandemic, demonstrating the potential of big data and machine learning for promoting risk communication and decision-making.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

The role of additives on acrylamide formation in food products: a systematic review

Acrylamide (AA) is a toxic substance formed in many carbohydrate-rich food products, whose formation can be reduced by adding some additives. Furthermore, the type of food consumed determines the AA intake. According to the compiled information, the first route causing AA formation is the Maillard reaction. Some interventions, such as reducing AA precursors in raw materials, (i.e., asparagine), reducing sugars, or decreasing temperature and processing time can be applied to limit AA formation in food products. The L-asparaginase is more widely used in potato products. Also, coatings loaded with proteins, enzymes, and phenolic compounds are new techniques for reducing AA content. Enzymes have a reducing effect on AA formation by acting on asparagine; proteins by competing with amino acids to participate in Maillard, and phenolic compounds through their radical scavenging activity. On the other hand, some synthetic and natural additives increase the formation of AA. Due to the high exposure to AA and its toxic effects, it is essential to recognize suitable food additives to reduce the health risks for consumers. In this sense, this study focuses on different additives that are proven to be effective in the reduction or formation of AA in food products.

Rapid screening of illegal additives in functional food using atmospheric pressure solids analysis probe coupled to a portable mass spectrometer

Pharmaceutical drugs like Sildenafil are illegally added to functional food such as nutritional supplements and herbal remedies to deliver drugs without a regular prescription to consumers. Rapid screening of illegal additives is desirable for the public security department. The seized samples are often large in number and unknown in composition; methods are needed for qualitative screening of unknown samples. Here, a new approach is presented based on atmospheric pressure solids analysis probe (ASAP) coupled with single-quadrupole mass spectrometer to rapidly screen 42 common illegal additives in six categories of functional food. The ASAP-MS method could be applied to solid or liquid sample analysis with a very simple pre-treatment and no LC chromatographic separation, using a home-built library; the identification of suspicious additives could be obtained rapidly. More importantly, the approach is sensitive enough for complex matrix samples like coffee samples. 21 batches of seized unknown samples were tested by the ASAP-MS, and the positive results were confirmed by LC-MS/MS(QQQ), indicating that the ASAP-MS method is effective and reliable. The ASAP-MS with home-built library is a promising method for rapid screening of illegal additives in functional food, which could be widely used in the grassroots police station that lack professional laboratory environment.

ChemHub: a knowledgebase of functional chemicals for synthetic biology studies

SUMMARY

The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications, and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90,000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600,000 published studies by more than 100 people over the past 10 years.

AVAILABILITY AND IMPLEMENTATION

Multiple algorithms were implemented to enable biosynthetic pathway design and precursor discovery, which can support investigation of the biosynthetic potential of these functional chemicals. ChemHub is freely available at: http://www.rxnfinder.org/chemhub/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A data-driven integrative platform for computational prediction of toxin biotransformation with a case study

Recently, biogenic toxins have received increasing attention owing to their high contamination levels in feed and food as well as in the environment. However, there is a lack of an integrative platform for seamless linking of data-driven computational methods with 'wet' experimental validations. To this end, we constructed a novel platform that integrates the technical aspects of toxin biotransformation methods. First, a biogenic toxin database termed ToxinDB (http://www.rxnfinder.org/toxindb/), containing multifaceted data on more than 4836 toxins, was built. Next, more than 8000 biotransformation reaction rules were extracted from over 300,000 biochemical reactions extracted from ~580,000 literature reports curated by more than 100 people over the past decade. Based on these reaction rules, a toxin biotransformation prediction model was constructed. Finally, the global chemical space of biogenic toxins was constructed, comprising ~550,000 toxins and putative toxin metabolites, of which 94.7% of the metabolites have not been previously reported. Additionally, we performed a case study to investigate citrinin metabolism in Trichoderma, and a novel metabolite was identified with the assistance of the biotransformation prediction tool of ToxinDB. This unique integrative platform will assist exploration of the 'dark matter' of a toxin's metabolome and promote the discovery of detoxification enzymes.

FOCUS-DB: An Online Comprehensive Database on Food Additive Safety

Processing and packaging food has greatly exaggerated the use of food additives in different types of food products. Safety assessment to determine the pharmacokinetic and toxicological properties of food additives is imperative and experimentally challenging. Several resources of food additives properties have been collated; however, information remains partial, scattered, and not readily accessible, particularly for food safety. To address the concern related to the potential health hazard of food additives, we have developed the Food-Additive-Consumption-Safety Database (FOCUS-DB). Presently, the database comprises 2885 food additives, distributed into 18 categories with 40,800 collected data points, 89,435 predicted data points, and 14,425 external links. The dynamic web interface of the FOCUS-DB resource enables a risk assessment of additives, their approval status in various regulatory authorities, physicochemical properties, acceptable daily intake, GHS signals, biological pathways, predicted pharmacokinetic parameters, and various toxicity endpoint values. FOCUS-DB supports the exploration of food additives; it is beneficial for both the regulatory authorities and industries to optimize the usage limits of the additives and formulations. This database is a promising tool that helps understand the relationship between food additives and toxicity, which could be used to develop a future food safety framework.

Fabrication of Eco-Friendly Betanin Hybrid Materials Based on Palygorskite and Halloysite

Eco-friendly betanin/clay minerals hybrid materials with good stability were synthesized by combining with adsorption, grinding, and heating treatment using natural betanin extracted from beetroot and natural 2:1 type palygorskite or 1:1 type halloysite. After incorporation of clay minerals, the thermal stability and solvent resistance of natural betanin were obviously enhanced. Due to the difference in the structure of palygorskite and halloysite, betanin was mainly adsorbed on the outer surface of palygorskite or halloysite through hydrogen-bond interaction, but also part of them also entered into the lumen of Hal via electrostatic interaction. Compared with palygorskite, hybrid materials prepared with halloysite exhibited the better color performance, heating stability and solvent resistance due to the high loading content of betanin and shielding effect of lumen of halloysite.

FRCD: A comprehensive food risk component database with molecular scaffold, chemical diversity, toxicity, and biodegradability analysis

The presence of natural toxins, pesticide residues, and illegal additives in food products has been associated with a range of potential health hazards. However, no systematic database exists that comprehensively includes and integrates all research information on these compounds, and valuable information remains scattered across numerous databases and extensive literature reports. Thus, using natural language processing technology, we curated 12,018 food risk components from 152,737 literature reports, 12 authoritative databases, and numerous related regulatory documents. Data on molecular structures, physicochemical properties, chemical taxonomy, absorption, distribution, metabolism, excretion, toxicity properties, and physiological targets within the human body were integrated to afford the comprehensive food risk component database (FRCD, http://www.rxnfinder.org/frcd/). We also analyzed the molecular scaffold and chemical diversity, in addition to evaluating the toxicity and biodegradability of the food risk components. The FRCD could be considered a highly promising tool for future food safety studies.