Modernization of digital food safety control.

Foodborne illness remains a pressing global issue due to the complexities of modern food supply chains and the vast array of potential contaminants that can arise at every stage of food processing from farm to fork. Traditional food safety control systems are increasingly challenged to identify these intricate hazards. The U.S. Food and Drug Administration's (FDA) New Era of Smarter Food Safety represents a revolutionary shift in food safety methodology by leveraging cutting-edge digital technologies. Digital food safety control systems employ modern solutions to monitor food quality by efficiently detecting in real time a wide range of contaminants across diverse food matrices within a short timeframe. These systems also utilize digital tools for data analysis, providing highly predictive assessments of food safety risks. In addition, digital food safety systems can deliver a secure and reliable food supply chain with comprehensive traceability, safeguarding public health through innovative technological approaches. By utilizing new digital food safety methods, food safety authorities and businesses can establish an efficient regulatory framework that genuinely ensures food safety. These cutting-edge approaches, when applied throughout the food chain, enable the delivery of safe, contaminant-free food products to consumers.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety.

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Progress and Challenge of Sensors for Dairy Food Safety Monitoring.

One of the most consumed foods is milk and milk products, and guaranteeing the suitability of these products is one of the major concerns in our society. This has led to the development of numerous sensors to enhance quality controls in the food chain. However, this is not a simple task, because it is necessary to establish the parameters to be analyzed and often, not only one compound is responsible for food contamination or degradation. To attempt to address this problem, a multiplex analysis together with a non-directed (e.g., general parameters such as pH) analysis are the most relevant alternatives to identifying the safety of dairy food. In recent years, the use of new technologies in the development of devices/platforms with optical or electrochemical signals has accelerated and intensified the pursuit of systems that provide a simple, rapid, cost-effective, and/or multiparametric response to the presence of contaminants, markers of various diseases, and/or indicators of safety levels. However, achieving the simultaneous determination of two or more analytes in situ, in a single measurement, and in real time, using only one working 'real sensor', remains one of the most daunting challenges, primarily due to the complexity of the sample matrix. To address these requirements, different approaches have been explored. The state of the art on food safety sensors will be summarized in this review including optical, electrochemical, and other sensor-based detection methods such as magnetoelastic or mass-based sensors.

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman).

A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.

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.

An evaluation of the quality of Annona muricata leaf products.

OBJECTIVES: Annona muricata, also known as graviola, is traditionally used for the treatment of a range of disorders including cancer. Interest in A. muricata use has increased in recent years. This study investigated the quality and safety of a selection of commercially available A. muricata leaf products. METHODS: Seven commercially available products were purchased via online shopping sites. Each product was assessed for quality indicators including weight variation, quantification of the bioactive constituent annonacin, presence of annonaceous acetogenins and contaminants. The samples were evaluated by thin-layer chromatography, high-performance liquid chromatography, liquid chromatography-mass spectroscopy, low-resolution mass spectrometry and nuclear magnetic resonance spectroscopy. Microbial analysis was carried out in accordance with the British Pharmacopoeia. Heavy metals were analysed by inductive coupled plasma mass spectrometry. KEY FINDINGS: Of the seven products analysed, one product contained less than half of the content stated on the label. The labelled dosage recommendation varied between products. There was a high variation in annonacin concentration (1.05-3.09 mg/g) and the presence of annonaceous acetogenins. One of the products was found to have a total aerobic microbial count above the United States Pharmacopoeia limit. CONCLUSIONS: The variation in the indicators of quality and safety of commercially available A. muricata leaf products tested have implications for clinicians and people living with cancer who use these herbal products.

Investigation of Unsafe Acts Influence Law Based on System Dynamics Simulation: Thoughts on Behavior Mechanism and Safety Control.

In modern safety management, it is very important to study the influence of the whole safety system on unsafe acts in order to prevent accidents. However, theoretical research in this area is sparse. In order to obtain the influence law of various factors in the safety system on unsafe acts, this paper used system dynamics simulation to carry out theoretical research. First, based on a summary of the causes of the coal and gas outburst accidents, a dynamic simulation model for unsafe acts was established. Second, the system dynamics model is applied to investigate the influence of various safety system factors on unsafe acts. Third, the mechanism and the control measures of unsafe acts in the enterprise safety system are studied. This study's main result and conclusions are as follows: (1) In the new coalmines, the influence of the safety culture, safety management system, and safety ability on the safety acts were similar. The order of influence on the safety acts in production coalmines is as follows: safety management system > safety ability > safety culture. The difference is most evident in months ten to eighteen. The higher the safety level and safety construction standard of the company, the greater the difference. (2) In the construction of the safety culture, the order of influence was as follows: safety measure elements > safety responsibility elements = safety discipline elements > safety concept elements. It shows the difference in influence from the 6th month and attains its maximum value from the 12th month to the 14th month. (3) In the construction of the safety management system, the degree of influence in new coalmines was as follows: safety policy > safety management organization structure > safety management procedures. Among them, especially in the first 18 months, the impact of the safety policy was most apparent. However, in the production mine, the degree of influence was as follows: safety management organization structure > safety management procedures > safety policy, but the difference is very small. (4) The degree of influence on the construct of safety ability was as follows: safety knowledge > safety psychology = safety habits > safety awareness, but the difference on the impact was small.

Combining nanoflares biosensor and mathematical resolution technique for multi-class mycotoxin analysis in complex food matrices.

A multi-functional nanoflares biosensor of spherical gold nanoparticle (Au NP) modified by fluorophore-labeled oligonucleotides (ONS) was designed for ultra-sensitive multi-target mycotoxin analysis in food. Au NP was densely modified with multiplex highly oriented hairpins of oligonucleotides (ONS), each ONS was hybridized to a reporter with a distinct fluorophore label and specifically affiliative to its corresponding mycotoxin target. The fluorescent signals of reporters were pre-quenched by Au NP based on ONS hairpin structures and recovered when exposed to ONS's targets. Excitation-emission matrix (EEM) fluorescence detection was performed in EX and EM wavelength of 200-800 nm. Heavily overlapping spectra of fluorophores, mycotoxins and backgrounds were resolved by alternative trilinear decomposition (ATLD) algorithm, pure spectra of specific fluorophore responding to mycotoxin target can be extracted out for quantitative analysis. Four mycotoxins (Aflatoxin B1, zearalenone, Fumonisins B1, ochratoxin A) were simultaneously quantified at extremely low level with limit of detection <0.02 µg kg-1, the average recovery accuracies were higher than 91.7 % in various matrices of cereals, nuts, edible oils. This study realized an important breakthrough of the application of nanoflares biosensor and maybe promising to be as an alternative strategy for onsite mycotoxins monitoring of food.

Results of the implementation of a virtual control approach to improve the effectiveness and quality of Safety and Health inspections at workplaces.

SUMMARY: During the last years, a significant technological and organisational evolution is taking place in the industrial activities, thanks to Advanced Manufacturing together with a more and more widespread use of Cloud Big Data Analytics. With special reference to the OSH (Occupational Safety and Health), these changes involve a new attention, both to the technological and employee's level. The paper discusses the characteristics, and the possible original use of a new methodology, based on virtual reality and digital checklist. This methodology can be used both to easily carry out surveillance activities and checks at workplaces, and to create a virtual itinerary of the area under investigation, in which checks and operative instructions can be virtually introduced for workers in charge of particular machinery or operations. The main results obtained thanks to the implementation of the methodology are: a reduction of the remedial actions' duration on non-compliances brought into evidence, and the deriving possible increase in the frequency and efficiency of inspections. Moreover, this approach intrinsically favors the involvement of workers and staff in charge of activities related to Prevention and Protection, supporting a process of continuous improvement and of general spread of the Culture of Safety in the Company.

Regulation of fungal community and the quality formation and safety control of Pu-erh tea.

Pu-erh tea belongs to dark tea among six major teas in China. As an important kind of post-fermented tea with complex microbial composition, Pu-erh tea is highly praised by many consumers owing to its unique and rich flavor and taste. In recent years, Pu-erh tea has exhibited various physiological activities to prevent and treat metabolic diseases. This review focuses on the fungi in Pu-erh tea and introduces the sources, types, and functions of fungi in Pu-erh tea, as well as the influence on the quality of Pu-erh tea and potential safety risks. During the process of fermentation and aging of Pu-erh tea, fungi contribute to complex chemical changes in bioactive components of tea. Therefore, we examine the important role that fungi play in the quality formation of Pu-erh tea. The associations among the microbial composition, chemicals excreted, and potential food hazards are discussed during the pile-fermentation of Pu-erh tea. The quality of Pu-erh tea has exhibited profound changes during the process of pile-fermentation, including color, aroma, taste, and the bottom of the leaves, which are inseparable from the fungus in the pile-fermentation of Pu-erh tea. Specifically, the application prospects of various detection methods of mycotoxins in assessing the safety of Pu-erh tea are proposed. This review aims to fully understand the importance of fungi in the production of Pu-erh tea and further provides new insights into subtly regulating the piling process to improve the nutritional properties and guarantee the safety of Pu-erh tea.

Micro/nanomotor technology: the new era for food safety control.

Food poisoning caused by eating contaminated food remains a threat to global public health. Making the situation even worse is the aggravated global environmental pollution, which poses a major threat to the safety of agricultural resources. Food adulteration has been rampant owing to negligent national food safety regulations. The speed at which contaminated food is detected and disposed of determines the extent to which consumers' lives are safeguarded and agricultural economic losses are prevented. Micro/nanomotors offer a high-speed mobile loading platform that substantially increases the chemical reaction rates and, accordingly, exhibit great potential as alternatives to conventional detection and degradation techniques. This review summarizes the propulsion modes applicable to micro/nanomotors in food systems and the advantages of using micro/nanomotors, highlighting examples of their potential use in recent years for the detection and removal of food contaminants. Micro/nanomotors are an emerging technology for food applications that is moving toward mass production, simple preparation, and important functions.

Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment.

Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning-a convolutional neural network-is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.

[Analysis of medical records as an element of quality control and safety of medical activities]. / Analiz meditsinskoi dokumentatsii kak element kontrolya kachestva i bezopasnosti meditsinskoi deyatel'nosti.

OBJECTIVE: Studying the issue of quality control and safety of medical activities. MATERIAL AND METHODS: The analysis of domestic literature on the quality of filling out medical documentation has been carried out. RESULTS: A dental patient's medical record (ICSD) is not only the most important legal document, but also a certain indicator of the level of professional competence of a dentist. To find out all sorts of reasons that led to professional errors and adverse consequences after the provision of dental care, a thorough study and general analysis of medical documentation is of great importance, the maintenance and correct execution of which is also necessary for solving a number of other, including legal issues. The most common drawbacks in the design of a dental patient's medical record have been identified, which indicates a decrease in the quality of medical care provided to patients. CONCLUSION: At the moment, there are still unresolved issues related to complete completion of medical documentation. When analyzing the literature, we did not find a purposeful study of the medical history of a dental patient during surgical treatment, which requires further research.

Assessment of Sorbate and Benzoate Content in Mustard, Ketchup and Tomato Sauce by Sub-Minute Capillary Electrophoresis.

RESEARCH BACKGROUND: Sorbate and benzoate are important preservatives in food products, but these compounds can also have genotoxic effects, causing health risks to consumers. In this regard, this study aims to determine the mass fractions of sorbate and benzoate in Brazilian samples of mustard, ketchup and tomato sauce using an adequately validated sub-minute capillary electrophoresis method. EXPERIMENTAL APPROACH: In this study, sorbate and benzoate were evaluated in sauce samples by capillary electrophoresis using a simple sample preparation procedure. Previously, the method was validated according to Eurachem guidelines, and its greenness was assessed by Eco-Scale. RESULTS AND CONCLUSIONS: The fitness for purpose of the method, as well as its suitability for the analysis of the studied matrices and its agreement with the principles of green chemistry were checked and confirmed. Also, according to our findings, among the 30 commercial samples assessed, six of them presented some mislabeling or non-compliance with European or Brazilian legislation, reinforcing the constant need for quality assessment and surveillance of food products. NOVELTY AND SCIENTIFIC CONTRIBUTION: So far, there have been few studies related to investigating the preservatives such as sorbate and benzoate in mustard, ketchup and tomato sauce, highlighting the significance and contribution of the obtained results to the knowledge in the field.

Development of a Direct and Rapid Detection Method for Viable but Non-culturable State of Pediococcus acidilactici.

Pediococcus acidilactici may significantly reduce the pH-value, and thus has different influence, including serving as a probiotic in human microbiota but a spoilage in human food as it could change the flavor. Pediococcus acidilactici is also capable of entering into the viable but non-culturable (VBNC) state causing false negative results of standard culture-based detection method. Thus, development of detection method for VBNC state P. acidilactici is of great significance. In this study, propidium monoazide (PMA) combined with cross priming amplification (CPA) was developed to detect the VBNC cells of P. acidilactici and applied on the detection in different systems. With detection limit of 104 cells/ml, high sensitivity, and 100% specificity, PMA-CPA can successfully detect VBNC cells of P. acidilactici and be applied in with high robustness.

Neutronic feasibility study of using a multipurpose MNSR for BNCT, NR, and NAA.

In this study, the conceptual design of a multipurpose research reactor for boron neutron capture therapy (BNCT), neutron radiography (NR) and neutron activation analysis (NAA) applications has been performed. Specifically, a suitable epithermal neutron flux (φEpi) of about 1×109 (n.cm-2.s-1) for BNCT and high quality thermal neutron flux (φTh) of above 1×106 (n.cm-2.s-1)for NR are carried out based on our LEU reactor core designing. The UO2 fuel with density of 10.5 g/cm3 and enrichment of 12.4% is applied as an appropriate LEU fuel. The reactor safety is assured by designing the safety control rod system with two banks. A fission converter facility (FCF) consists of 19 fuel rods of UO2 is used to increase φEpi for BNCT treatment. Furthermore, a unique thermal column of heavy water is used to increase φTh for NR purpose. Four internal irradiation sites, two external irradiation sites, and one large thermal column irradiation site are considered at the reactor which can be used for NAA application. The results of neutronic calculations show that the reactor meets the neutronic design limits for a low power research reactor.

Visual detection of microRNA146a by using RNA-functionalized gold nanoparticles.

Gold nanoparticles of different sizes have been synthesized and surface-functionalized with selected RNA probes in order to develop a rapid, low-cost and sensitive method for detection of microRNA146a (miR146a). The strategy is based on the change of colour that can be observed visually after aggregation of the RNA modified-gold nanoparticles (AuNPs) in presence of miR146a. Experimental conditions have been carefully selected in order to obtain a good sensitivity that allows to perform visual detection of microRNA at the nM level, achieving a detection limit of 5 nM. Good repeatability and selectivity versus other sequences that only differ from miR146a in 3 bases was achieved. miR146a has been described as one of the main microRNA involved in the immune response of bovine mastitis, being expressed in tissue, blood and milk samples. The method was successfully applied to the detection of miR146a in raw cow milk samples. The present scheme constitutes a rapid and low-cost alternative to perform highly sensitive detection of microRNA without the need of instrumentation and amplification steps for the early detection of bovine mastitis in the agrofood industry. Graphical abstract Schematic representation of the assay based on aggregation of RNA-modified gold nanoparticles (blue) in presence of microRNA146a generating a dark blue spot onto a solid support, versus a pink spot observed in absence of miR146a due to dispersed gold nanoparticles (red).

Genetically Modified Micro-Organisms for Industrial Food Enzyme Production: An Overview.

The use of food enzymes (FE) by the industrial food industry is continuously increasing. These FE are mainly obtained by microbial fermentation, for which both wild-type (WT) and genetically modified (GM) strains are used. The FE production yield can be increased by optimizing the fermentation process, either by using genetically modified micro-organism (GMM) strains or by producing recombinant enzymes. This review provides a general overview of the different methods used to produce FE preparations and how the use of GMM can increase the production yield. Additionally, information regarding the construction of these GMM strains is provided. Thereafter, an overview of the different European regulations concerning the authorization of FE preparations on the European market and the use of GMM strains is given. Potential issues related to the authorization and control of FE preparations sold on the European market are then identified and illustrated by a case study. This process highlighted the importance for control of FE preparations and the consequent need for appropriate detection methods targeting the presence of GMM, which is used in fermentation products.

Molecularly imprinted polymers as receptors for assays of antibiotics.

The use of excessive antibiotics in medical treatment and animal breeding has led to their prevalence in the environment and foods. Thereby, rapid, cheap, and sustainable techniques are required to detect and control the potential risk related to antibiotics. Actually, immunoassays have wide applications for this purpose, and improved assay formats with enzymatic, fluorescent, nanodispersed, and other tracers have enhanced the efficiency of the technique. However, there are several shortcomings of immunoassay due to the protein nature of antibodies. Thereby, molecular imprinting technology has evolved as growing artificial analytical receptor for molecular recognition with binding properties similar to natural antibodies. Molecularly imprinted polymers (MIPs) are defined as "plastibodies" or substitutes for antibodies in immunoassays. This review gives a general overview of the application of molecular imprinting to analytical systems, its state of art, and perspective. The application of MIP-based assays in the detection of antibiotics in food and environmental samples is explored herein.

Development of a Virtual Force Sensor for a Low-Cost Collaborative Robot and Applications to Safety Control.

To protect operators and conform to safety standards for human-machine interactions, the design of collaborative robot arms often incorporates flexible mechanisms and force sensors to detect and absorb external impact forces. However, this approach increases production costs, making the introduction of such robot arms into low-cost service applications difficult. This study proposes a low-cost, sensorless rigid robot arm design that employs a virtual force sensor and stiffness control to enable the safety collision detection and low-precision force control of robot arms. In this design, when a robot arm is subjected to an external force while in motion, the contact force observer estimates the external torques on each joint according to the motor electric current and calculation errors of the system model, which are then used to estimate the external contact force exerted on the robot arm's end-effector. Additionally, a torque saturation limiter is added to the servo drive for each axis to enable the real-time adjustment of joint torque output according to the estimated external force, regulation of system stiffness, and achievement of impedance control that can be applied in safety measures and force control. The design this study developed is a departure from the conventional multisensor flexible mechanism approach. Moreover, it is a low-cost and sensorless design that relies on model-based control for stiffness regulation, thereby improving the safety and force control in robot arm applications.