Flash heating process for efficient meat preservation.

Maintaining food safety and quality is critical for public health and food security. Conventional food preservation methods, such as pasteurization and dehydration, often change the overall organoleptic quality of the food products. Herein, we demonstrate a method that affects only a thin surface layer of the food, using beef as a model. In this method, Joule heating is generated by applying high electric power to a carbon substrate in <1 s, which causes a transient increase of the substrate temperature to > ~2000 K. The beef surface in direct contact with the heating substrate is subjected to ultra-high temperature flash heating, leading to the formation of a microbe-inactivated, dehydrated layer of ~100 µm in thickness. Aerobic mesophilic bacteria, Enterobacteriaceae, yeast and mold on the treated samples are inactivated to a level below the detection limit and remained low during room temperature storage of 5 days. Meanwhile, the product quality, including visual appearance, texture, and nutrient level of the beef, remains mostly unchanged. In contrast, microorganisms grow rapidly on the untreated control samples, along with a rapid deterioration of the meat quality. This method might serve as a promising preservation technology for securing food safety and quality.

Cobalt-Modified Black Phosphorus Nanosheets-Enabled Ferrate (VI) Activation for Efficient Chemiluminescence Detection of Thiabendazole.

The development of chemiluminescence-based innovation sensing systems and the construction of a sensing mechanism to improve the analytical performance of compounds remain a great challenge. Herein, we fabricated an advanced oxidation processes pretreated chemiluminescence (AOP-CL) sensing system via the introduction of cobalt-modified black phosphorus nanosheets (Co@BPNs) to achieve higher efficient thiabendazole (TBZ) detection. Co@BPNs, enriched with lattice oxygen, exhibited a superior catalytic performance for accelerating the decomposition of ferrate (VI). This Co@BPNs-based ferrate (VI) AOP system demonstrated a unique ability to selectively decompose TBZ, resulting in a strong CL emission. On this basis, a highly selective and sensitive CL sensing platform for TBZ was established, which exhibited strong resistance to common ions and pesticides interference. This was successfully applied to detecting TBZ in environmental samples such as tea and kiwi fruits. Besides, the TBZ detection mechanism was explored, Co@BPNs-based ferrate (VI) AOP system produced a high yield of ROS (mainly 1O2), which oxidized the thiazole-based structure of TBZ, generating chemical energy that was transferred to Co@BPNs via a chemical electron exchange luminescence (CIEEL) mechanism, leading to intense CL emission. Notably, this study not only proposed an innovative approach to enhance the chemical activity and CL properties of nanomaterials but also offered a new pathway for designing efficient CL probes for pollutant monitoring in complex samples.

Study on the host-guest interactions between tetramethyl cucurbit[6]uril and 2-heterocyclic-substituted benzimidazoles.

Cucurbit[n]urils (Q[n]s) are a class of supramolecular host compounds with hydrophilic carbonyl ports and hydrophobic cavities, which can selectively form host-guest inclusion complexes with guest molecules to change the properties of guest molecules. In this paper, tetramethyl cucurbit[6]uril (TMeQ[6]) was used as the host and three 2-heterocyclic substituted benzimidazole derivatives as the guests, and their modes of interaction were investigated using X-ray crystallography, 1H NMR spectrometry, and other analytical techniques. The results showed that TMeQ[6] formed a 1 : 1 host-guest inclusion complex with three guest molecules, and the binding process between them was mainly enthalpy-driven. The X-ray diffraction analysis indicated that the main driving forces for the formation of these three inclusion complexes included hydrogen bonding interactions and ion dipole interactions. There are two modes of interaction between G3 and TMeQ[6] in the liquid phase, indicating that the benzimidazole ring and heterocyclic substituents on the guest molecule compete with the cavity of TMeQ[6]. Besides, the addition of TMeQ[6] significantly enhanced the fluorescence of these guests and slightly improved their solubility.

Cucurbit[7]uril-based carbon dots for recognizing histamine.

Fluorescent carbon quantum dots (CQDs) were synthesized from cucurbit[7]uril (Q[7]) and 2,2-bis(hydroxymethyl)propionic (DMPA) by a hydrothermal method. The Q[7]-DMPA complex was confirmed by X-ray crystallography. The CQDs showed blue fluorescence, photostability, and ionic strength stability. They were used to detect histamine with a low limit of 2.33 × 10-6 M.

Metal Cation-Doped ns-Cucurbit[10]uril: Adsorption of Para-Phenylenediamine.

The separation of phenylenediamine (PDA) isomers is crucial in the field of chemical manufacturing. Herein, we presented a strategy for the separation of PDA isomers (para-phenylenediamine, p-PDA; meta-phenylenediamine, m-PDA; ortho-phenylenediamine, o-PDA) using four supramolecular framework materials of ns-cucurbit[10]uril (ns-Q[10]), (1) ns-Q[10](Cd), (2) ns-Q[10](Mn), (3) ns-Q[10](Cu), (4) ns-Q[10](Pb). Our findings indicated that these supramolecular framework materials of ns-Q[10] showed remarkable selectivity for para-phenylenediamine (p-PDA) in p-PDA, m-PDA, and o-PDA mixtures, respectively. The variations in selectivity observed in these four single-crystal structures arose from variations in the thermodynamic stabilities and binding modes of the host-guest complexes. Importantly, the supramolecular framework based on ns-Q[10] exhibited selective accommodation of p-PDA over its isomers. This study highlighted the practical application of ns-Q[10] in effectively separating PDA isomers and demonstrated the potential utility of ns-Q[10] in isolating other organic molecules.

Green preparation, safety control and intelligent processing of high-quality tea extract.

Tea contains a variety of bioactive components, including catechins, amino acids, tea pigments, caffeine and tea polysaccharides, which exhibit multiple biological activities. These functional components in tea provide a variety of unique flavors, such as bitterness, astringency, sourness, sweetness and umami, which meet the demand of people for natural plant drinks with health benefits and pleasant flavor. Meanwhile, the traditional process of tea plantation, manufacturing and circulation are often accompanied by the safety problems of pesticide residue, heavy metal, organic solvents and other exogenous risks. High-quality tea extract refers to the special tea extract obtained by enriching the specific components of tea. Through green and efficient extraction technologies, diversed high-quality tea extracts such as high-fragrance and high-amino acid tea extracts, low-caffeine and high-catechin tea extracts, high-bioavailability and high-theaflavin tea extracts, high-antioxidant and high-tea polysaccharide tea extracts, high-umami-taste and low-bitter and astringent taste tea extracts are produced. Furthermore, rapid detection, green control and intelligent processing are applied to monitor the quality of tea in real-time, which guarantee the stability and safety of high-quality tea extracts with enhanced efficiency. These emerging technologies will realize the functionalization and specialization of high-quality tea extracts, and promote the sustainable development of tea industry.

Main high-quality tea extracts and their preparation methods were introduced.Potential pollutants in the processing of tea extracts and their detection methods were proposed.Emerging intelligent processing technologies of tea extract were summarized.The applications of high-quality tea extracts in food industry were explored.Future trends of tea extracts and relevant suggestions were presented.

A fluorescent probe for detection of Hg2+ ions constructed by tetramethyl cucurbit[6]uril and 1,2-bis(4-pyridyl)ethene.

In this paper, tetramethyl cucurbit[6]uril (TMeQ[6]) and 1,2-bis(4-pyridyl)ethene (G) were used to construct a supramolecular fluorescent probe G@TMeQ[6]. The host-guest interaction between TMeQ[6] and G was investigated using 1H NMR spectroscopy, single-crystal X-ray diffraction and various experimental techniques. The results show that TMeQ[6] and G form an inclusion complex with a host-guest ratio of 1:1 and the equilibrium association constant (Ka) was 2.494 × 104 M-1. The G@TMeQ[6] fluorescent probe can sensitively recognize Hg2+ ions by fluorescence enhancement. The linear range is 0.33 × 10-5-1.65 × 10-5 mol·L-1, R2 = 0.9926, and the limit of detection is 4.12 × 10-8 mol·L-1. The fluorescent probe can be used to detect the concentration of Hg2+ ions in aqueous solution, and provides a theoretical basis for the development of new fluorescent probes for detecting heavy metal ions.

Postharvest fruit quality of tomatoes influenced by an ethylene signaling component during long-term cold storage.

Ethylene production is essential for improving cold resistance of postharvest tomatoes. However, the role of ethylene signaling pathway in maintaining fruit quality during long-term cold storage remains poorly understood. Here, we demonstrated that a partial loss of function in ethylene signaling by mutation of Ethylene Response Factor 2 (SlERF2), worsened fruit quality during cold storage, as determined by visual characterization, and physiological analyses of membrane damage and reactive oxygen species metabolism. In addition, the transcriptions of genes related to abscisic acid (ABA) biosynthesis and signaling were also altered by SlERF2 gene in response to cold storage. Furthermore, mutation of SlERF2 gene compromised cold-induced expression of genes in the C-repeat/dehydration-responsive binding factor (CBF) signaling pathway. Therefore, it's concluded that an ethylene signaling component, SlERF2 contributed to the regulations of ABA biosynthesis and signaling, as well as CBF cold signaling pathway, ultimately affecting the fruit quality during long-term cold storage of tomatoes.

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.

Formation, immunomodulatory activities, and enhancement of glucosinolates and sulforaphane in broccoli sprouts: a review for maximizing the health benefits to human.

Broccoli sprouts have been considered as functional foods which have received increasing attention because they have been highly prized for glucosinolates, phenolics, and vitamins in particular glucosinolates. One of hydrolysates-sulforaphane from glucoraphanin is positively associated with the attenuation of inflammatory, which could reduce diabetes, cardiovascular and cancer risk. In recent decades, the great interest in natural bioactive components especially for sulforaphane promotes numerous researchers to investigate the methods to enhance glucoraphanin levels in broccoli sprouts and evaluate the immunomodulatory activities of sulforaphane. Therefore, glucosinolates profiles are different in broccoli sprouts varied with genotypes and inducers. Physicochemical, biological elicitors, and storage conditions were widely studied to promote the accumulation of glucosinolates and sulforaphane in broccoli sprouts. These inducers would stimulate the biosynthesis pathway gene expression and enzyme activities of glucosinolates and sulforaphane to increase the concentration in broccoli sprouts. The immunomodulatory activity of sulforaphane was summarized to be a new therapy for diseases with immune dysregulation. The perspective of this review served as a potential reference for customers and industries by application of broccoli sprouts as a functional food and clinical medicine.

Development of Stable Pickering Emulsions with TEMPO-Oxidized Chitin Nanocrystals for Encapsulation of Quercetin.

Pickering emulsions stabilized by TEMPO-oxidized chitin nanocrystals (T-ChNCs) were developed for quercetin delivery. T-ChNCs were synthesized by TEMPO oxidation chitin and systematically characterized in terms of their physicochemical properties. T-ChNCs were rod-like with a length of 279.7 ± 11.5 nm and zeta potential around -56.1 ± 1.6 mV. The Pickering emulsions were analyzed through an optical microscope and CLSM. The results showed that the emulsion had a small droplet size (972.9 ± 86.0 to 1322.3 ± 447.7 nm), a high absolute zeta potential value (-48.2 ± 0.8 to -52.9 ± 1.9 mV) and a high encapsulation efficiency (quercetin: 79.6%). The emulsion stability was measured at different levels of T-ChNCs and pH values. The droplet size and zeta potential decreased with longer storage periods. The emulsions formed by T-ChNCs retarded the release of quercetin at half rate of that of the quercetin ethanol solution. These findings indicated that T-ChNCs are a promising candidate for effectively stabilizing Pickering emulsions and controlling release of quercetin.

Future foods: Alternative proteins, food architecture, sustainable packaging, and precision nutrition.

There are numerous challenges facing the modern food and agriculture industry that urgently need to be addressed, including feeding a growing global population, mitigating and adapting to climate change, decreasing pollution, waste, and biodiversity loss, and ensuring that people remain healthy. At the same time, foods should be safe, affordable, convenient, and delicious. The latest developments in science and technology are being deployed to address these issues. Some of the most important elements within this modern food design approach are encapsulated by the MATCHING model: Meat-reduced; Automation; Technology-driven; Consumer-centric; Healthy; Intelligent; Novel; and Globalization. In this review article, we focus on four key aspects that will be important for the creation of a new generation of healthier and more sustainable foods: emerging raw materials; structural design principles for creating innovative products; developments in eco-friendly packaging; and precision nutrition and customized production of foods. We also highlight some of the most important new developments in science and technology that are being used to create future foods, including food architecture, synthetic biology, nanoscience, and sensory perception.Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2022.2033683.

Neural network in food analytics.

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

Study on the coordination of cyclopentanocucurbit[5,6]uril with Fe3+, Co2+ and Ni2+ ions.

This paper reports the coordination of cyclopentanocucurbit[5]uril (CyP5Q[5]) and cyclopentanocucurbit[6]uril (CyP6Q[6]) with Fe(ClO4)3, Co(ClO4)2 and Ni(ClO4)2. Single crystal X-ray diffraction analysis shows the metal ions are directly coordinated with the portal of the cucurbit[n]uril to form a one-dimensional supramolecular chain or independent systems in the CyP5Q[5]@Fe(ClO4)3, CyP5Q[5]@Co(ClO4)2, CyP6Q[6]@Co(ClO4)2 and CyP5Q[5]@Ni(ClO4)2 complexes. In CyP6Q[6]@Fe(ClO4)3, the metal ion is not directly coordinated with the cucurbit[n]uril portal, but after forming Fe(H2O)6, it interacts with the cucurbit[n]uril portal via a hydrogen bond. The CyP6Q[6]@Ni(ClO4)2 complex is quite special; in this system, there are both metal ions directly coordinated with the cucurbit[n]uril portal and free on the outer surface of the cucurbit[n]uril.

An Integrated Food Freshness Sensor Array System Augmented by a Metal-Organic Framework Mixed-Matrix Membrane and Deep Learning.

The static labels presently prevalent on the food market are confronted with challenges due to the assumption that a food product only undergoes a limited range of predefined conditions, which cause elevated safety risks or waste of perishable food products. Hence, integrated systems for measuring food freshness in real time have been developed for improving the reliability, safety, and sustainability of the food supply. However, these systems are limited by poor sensitivity and accuracy. Here, a metal-organic framework mixed-matrix membrane and deep learning technology were combined to tackle these challenges. UiO-66-OH and polyvinyl alcohol were impregnated with six chromogenic indicators to prepare sensor array composites. The sensors underwent color changes after being exposed to ammonia at different pH values. The limit of detection of 80 ppm for trimethylamine was obtained, which was practically acceptable in the food industry. Four state-of-the-art deep convolutional neural networks were applied to recognize the color change, endowing it with high-accuracy freshness estimation. The simulation test for chicken freshness estimation achieved accuracy up to 98.95% by the WISeR-50 algorithm. Moreover, 3D printing was applied to create a mold for possible scale-up production, and a portable food freshness detector platform was conceptually built. This approach has the potential to advance integrated and real-time food freshness estimation.

Understanding the Interaction between Regulatory Focus and Message Framing in Determining Chinese Consumers' Attitudes toward Artificial Meat.

While production and consumption of meat cast a shadow over the prospects for sustainable development, artificial meat may be the solution. However, consumer acceptability of artificial meat is a major impediment to its use as a suitable alternative. This study analyzed the relationship between regulatory focus and consumer acceptance of artificial meat using randomized controlled trial data. Results showed that promotion focus results in a higher acceptance of artificial meat products due to a higher perceived benefit and lower perceived risk, whereas prevention focus results in a lower acceptance of artificial meat products due to perceived benefit being lower and perceived risk being higher. The moderating effect of the message framing was investigated employing structural equation modeling (SEM). It was discovered that a gain-oriented message framing could greatly strengthen the association between promotion focus and perceived benefit, whereas an avoidance-oriented message framing could significantly diminish the relationship between prevention focus and perceived risk. This study has crucial implications for how policymakers and industries communicate with consumers about artificial meat.

Deep learning accurately predicts food categories and nutrients based on ingredient statements.

Determining attributes such as classification, creating taxonomies and nutrients for foods can be a challenging and resource-intensive task, albeit important for a better understanding of foods. In this study, a novel dataset, 134 k BFPD, was collected from USDA Branded Food Products Database with modification and labeled with three food taxonomy and nutrient values and became an artificial intelligence (AI) dataset that covered the largest food types to date. Overall, the Multi-Layer Perceptron (MLP)-TF-SE method obtained the highest learning efficiency for food natural language processing tasks using AI, which achieved up to 99% accuracy for food classification and 0.98 R2 for calcium estimation (0.93 âˆ¼ 0.97 for calories, protein, sodium, total carbohydrate, total lipids, etc.). The deep learning approach has great potential to be embedded in other food classification and regression tasks and as an extension to other applications in the food and nutrient scope.

Application of deep learning for image-based Chinese market food nutrients estimation.

With commercialization of deep learning (DL) models, daily precision dietary record based on images from smartphones becomes possible. This study took advantage of DL techniques on visual recognition tasks and proposed a suite of big-data-driven DL models regressing from food images to their nutrient estimation. We established and publicized the first food image database from the Chinese market, named ChinaMartFood-109. It contained 10,921 images with 23 nutrient contents, covering 18 main food groups. Inception V3 was optimized using other state-of-the-art deep convolutional neural networks, achieving up to 78 % and 94 % for top-1 and top-5 accuracy, respectively. Besides, this research compared three nutrient estimation algorithms and achieved the best regression coefficient (R2) by normalization + AM compared with arithmetic mean and harmonic mean, validating applicability in practice as well as theory. These encouraging results provide further evidence supporting artificial intelligence in the field of food analysis.

Microstructure and Properties of Al-6.0Mg-0.3Sc Alloy Deposited by Double-Wire Arc Additive Manufacturing.

Al-6.0Mg-0.3Sc alloy deposits are prepared by means of a double-wire arc additive manufacturing process. The formation, porosity, metallographic structure, type of precipitated phase, and mechanical properties of the deposit are studied. Double-wire arc forming affords precision advantages over single-wire-arc forming, which is evidenced by the increased surface uniformity of the deposit. Compared with the deposit of single-wire-arc formed, the deposit of double-wire arc formed exhibits only fewer and smaller pores, and the lower process heat yields rapid solidification and tiny precipitate sizes. A larger amount of Mg and Mn is observed to be dissolved in the Al matrix of double-wire arc-formed deposit, which increases the alloy strength, and smaller primary Al3Sc phase, which exhibits excellent grain refinement. Furthermore, the presence of a high amount of Sc solid solution in the matrix of double-wire arc-formed deposit strengthens the alloy, and the melting of the rear wire "heat-treats" the substrate formed by the front wire, promotes secondary Al3Sc phase precipitation, and further strengthens the alloy. Compared with the deposit of single-wire-arc formed, the mechanical properties of double-wire arc-formed deposit show an improvement: the tensile strength, yield strength, and elongation of the horizontally oriented specimens are estimated as 363 MPa, 258 MPa, and 26%, respectively. This successful implementation of the cold metal transfer + pulse process to prepare Al-Mg alloy parts can pave the way to their industrial production. The proposed method can find wide utility in the fields of aviation, aerospace, military, and shipbuilding.

Study on the host-guest complex of dicyclohexanocucurbit[6]uril and 2-phenylbenzimidazole, and its recognition effect toward Fe3.

This paper has selected dicyclohexanocucurbit[6]uril (CyH2Q[6]) as the host and 2-phenylbenzimidazole (G) as the guest to investigate the host-guest interaction mode between CyH2Q[6] and G. Under acidic conditions, the complex was characterized using nuclear magnetic resonance, ultraviolet and fluorescence spectroscopy. The results show that the molecular ratio of CyH2Q[6] to G is 2 : 1. The crystals were cultured with ZnCl2 as a structural inducer under acidic conditions and single crystal X-ray diffraction showed that the molecular ratio of CyH2Q[6] to G is 1 : 3. The G@CyH2Q[6] was used as a fluorescent probe to identify metal cations. The probe exhibits a good selective recognition effect toward Fe3+ ions, which involves a reduced fluorescence intensity with a limit of detection of 1.321 × 10-6 mol l-1.