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.

Highly defective copper-based metal-organic frameworks for the efficient adsorption and detection of organophosphorus pesticides: An experimental and computational investigation.

Organophosphorus pesticide (OP) residues pose a serious threat to human health, motivating the search for novel adsorbents and detection methods. Herein, defective copper-based metal organic frameworks (Cu-MOFs) were synthesized by the reaction of Cu2+ ions and 1,3,5-benzenetricarboxylate linkers in the presence of acetic acid. As the amount of acetic acid increased, the crystallization kinetics and morphology of the Cu-MOFs changed, leading to mesoporous Cu-MOFs with many large surface pores (defects). Adsorption studies of OPs revealed the defective Cu-MOFs showed faster pesticide adsorption kinetics and higher pesticide adsorption capacities. Density functional theory calculations showed that pesticide adsorption in the Cu-MOFs was mainly electrostatic. A dispersive solid phase extraction method was developed based on a defective Cu-MOF-6 for rapidly extracting pesticides from food samples. The method allowed pesticide detection over a wide linear concentration range, low limits of detection (0.0067-0.0164 µg L-1) and good recoveries in pesticide-spiked samples (81.03-109.55%).

A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area.

This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit. The SMA is solved by means of a new method composed of three successive phases, namely, grid space construction, candidate strip generation and strip selection. First, the grid space construction is proposed to discretize the irregular area into a set of points in a specific plane rectangular coordinate system and calculate the total profit of a solution of SMA. Then, the candidate strip generation is designed to produce numerous candidate strips based on the grid space of the first phase. At last, in the strip selection, the optimal schedule for all the SAR satellites is developed based on the result of the candidate strip generation. In addition, this paper proposes a normalized grid space construction algorithm, a candidate strip generation algorithm and a tabu search algorithm with variable neighborhoods for the three successive phases, respectively. To verify the effectiveness of the proposed method in this paper, we perform simulation experiments on several scenarios and compare our method with the other seven methods. Compared to the best of the other seven methods, our proposed method can improve profit by 6.38% using the same resources.

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization.

For positioning tasks of mobile robots in indoor environments, the emerging positioning technique based on visual inertial odometry (VIO) is heavily influenced by light and suffers from cumulative errors, which cannot meet the requirements of long-term navigation and positioning. In contrast, positioning techniques that rely on indoor signal sources such as 5G and geomagnetism can provide drift-free global positioning results, but their overall positioning accuracy is low. In order to obtain higher precision and more reliable positioning, this paper proposes a fused 5G/geomagnetism/VIO indoor localization method. Firstly, the error back propagation neural network (BPNN) model is used to fuse 5G and geomagnetic signals to obtain more reliable global positioning results; secondly, the conversion relationship from VIO local positioning results to the global coordinate system is established through the least squares principle; and finally, a fused 5G/geomagnetism/VIO localization system based on the error state extended Kalman filter (ES-EKF) is constructed. The experimental results show that the 5G/geomagnetism fusion localization method overcomes the problem of low accuracy of single sensor localization and can provide more accurate global localization results. Additionally, after fusing the local and global positioning results, the average positioning error of the mobile robot in the two scenarios is 0.61 m and 0.72 m. Compared with the VINS-mono algorithm, our approach improves the average positioning accuracy in indoor environments by 69.0% and 67.2%, respectively.

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.

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.

Mapping a highly conserved linear neutralizing epitope at the N-terminus of the gD glycoprotein of bovine herpesvirus type I using a monoclonal antibody.

Bovine herpesvirus type 1 (BoHV-1), a member of the Alphaherpesvirinae subfamily, causes significant economic losses to the cattle industry worldwide. Envelope glycoprotein D (gD) of BoHV-1 plays an essential role in the viral entry into permissive cells and possibly cooperates with other envelope glycoproteins. The herpesvirus gD induces a protective immune response against diseases in cattle or animal models. Mapping epitopes on gD will facilitate the understanding of the BoHV-1 pathogenesis and development of alternative vaccines against various diseases associated with the virus. In this study, a monoclonal antibody (MAb), designated as 3C1, was generated using naive BoHV-1 in vaccination of mice, demonstrating that 3C1 was specific to gD and represents a neutralizing activity against BoHV-1 infection in Madin-Darby bovine kidney cells. Panels of overlapping gD recombinant proteins with glutathione S-transferase tag were prepared to define the epitope recognized by 3C1. The data demonstrated that the N-terminus of gD 23APRVTVYVD31 was recognized by 3C1. Furthermore, the 26VTVYVD31 motif was the minimal amino acid sequence for the recognition. The epitope identified in this study is highly conserved among the typical strains of BoHV-1 and BoHV-5, suggesting that this epitope may be useful in the diagnosis of diseases. In addition, the defined region on gD of BoHV-1 might be essential in viral entry upon comparison with the prototype virus in herpes simplex virus (Alphaherpesvirinae). The data will elucidate the roles of gD of BoHV-1 in viral entry and pathogenesis and its potential application for the development of vaccine candidates and diagnostic techniques based on the conserved epitopes on gD or in combination with those of other herpesvirus glycoproteins.

Determination of 20 perfluoroalkyl substances in greenhouse vegetables with a modified one-step pretreatment approach coupled with ultra performance liquid chromatography tandem mass spectrometry(UPLC-MS-MS).

A new rapid modified one-step QuEChERS (quick, easy, cheap, effective, rugged, and safe) approach was developed for the simultaneous determination of 20 perfluoroalkyl substances (PFASs) from samples of cucumber, lettuce, eggplant, tomato, and leek. Several parameters were optimized. Similar pretreatment approaches were employed for comparison; the results verified the satisfactory performance of this new method. The detection limits of this method for the selected matrices ranged from 0.003 to 0.034 µg kg-1, and the method was verified to be satisfactory in terms of precision, accuracy, and matrix effects. 35 greenhouse vegetable samples were determined. PFASs were detected in 26 samples, with perfluorooctanoic acid (PFOA) being detected most frequently and much higher levels of PFBA and PFPeA being found in some samples. The total PFAS concentrations ranged from not detectable to 0.683 µg kg-1. This method could be applied for large scale determination of vegetables to research the migration and accumulation trends of PFASs from the environment to crops in the future.