Tb^(3+)-nucleic acid probe-based label-free and rapid detection of mercury pollution in food

Mercury is a threatening pollutant in food,herein,we developed a Tb^(3+)-nucleic acid probe-based label-free assay for mix-and-read,rapid detection of mercury pollution.The assay utilized the feature of light-up fluorescence of terbium ions(Tb^(3+))via binding with single-strand DNA.Mercury ion,Hg^(2+)induced thymine(T)-rich DNA strand to form a double-strand structure(T-Hg^(2+)-T),thus leading to fluorescence reduction.Based on the principle,Hg^(2+)can be quantified based on the fluorescence of Tb^(3+),the limit of detection was 0.0689μmol/L and the linear range was 0.1-6.0μmol/L.Due to the specificity of T-Hg^(2+)-T artificial base pair,the assay could distinguish Hg^(2+)from other metal ions.The recovery rate was ranged in 98.71%-101.34%for detecting mercury pollution in three food samples.The assay is low-cost,separation-free and mix-to-read,thus was a competitive tool for detection of mercury pollution to ensure food safety.

The detection and regulation advances of food allergens

Food allergy has become an important food quality and safety issue,posing a challenge to the food industry and affecting consumer health.On the one hand,from the perspective of food processing industry,the diversity of food raw material ingredients,exogenous additives,and processing forms make the presence of allergens in modern food processing more complex.In addition,due to the lack of allergen identification,effective detection and allergenicity evaluation systems,there are serious deficiencies in the current theories and techniques for food allergen screening and detection,tracking and prediction,intervention and control;On the other hand,from the perspective of public health,meeting consumers'right to know whether there are raw materials containing food allergens in processed foods,and improving the credibility of government and people's satisfaction have become urgent matters;In addition,as people come into contact with more and more new borne novel foods,the probability of food allergy is also increasing.The food safety and health problems induced by increasingly complex,widespread and severe food allergy are difficult to avoid.In view of this,in response to the increasingly serious food allergy issues,this paper introduced the detection methods of food allergens,summarized the reduction and control techniques of food allergens,and elaborated hypoallergenic foods,which aims to provide the basis for preventing and controlling food allergy and ensuring the physical health of food allergy patients.

Personal glucose meters coupled with signal amplification technologies for quantitative detection of non-glucose targets:Recent progress and challenges in food safety hazards analysis

Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.

The Fluorescence Immunochromatographic Strips for Natamycin and Its Application in Food Safety Detection

A fluorescence immunochromatographic strip was developed in this study for natamycin detection in food. The results showed that the best amount of labeled antibody was 10 μg, for every 50 μl of fluorescent microspheres with a 2.5%(w/v) concentration. This labeled antibody was diluted for 10 times, and the diluted solution was dispensed into conjugate pad at the amount of 3 μl/cm. The concentrations of natamycin labeled BSA for test line and goat anti-mouse IgG for control line were 2.0 and 1 mg/ml, respectively, which performed best. With the best conditions, the limit of detection was 1 ng/ml, the linearity ranged from 2 to 100 ng/ml, the recovery was about 80% to 120%, and the CV was below 23%.

Food Safety Detection Methods Applied to National Special Rectification of Product Quality and Food Safety

Afour-month period of national special rectification for product quality and food safety officially started on August 25, and was focused on eight fields, including those of agricultural products and processed foods.……

Methods to detect a vian influenza virus for food safety surveillance

Avian influenza （AI）, caused by the influenza A virus, has been a global concern for public health. AI outbreaks not only impact the poultry production, but also give rise to a risk in food safety caused by viral contamination of poultry products in the food supply chain. Distinctions in AI outbreak between strains H5N1 and H7N9 indicate that early detection of the AI virus in poultry is crucial for the effective warning and control of AI to ensure food safety. Therefore, the establishment of a poultry surveillance system for food safety by early detection is urgent and critical. In this article, methods to detect AI virus, including current methods recommended by the World Health Organization （WHO） and the World Organisation for Animal Health （Office International des Epizooties, OIE） and novel techniques not commonly used or commercialized are reviewed and evaluated for feasibility of use in the poultry surveillance system. Conventional methods usually applied for the purpose of AI diagnosis face some practical challenges to establishing a comprehensive poultry surveillance program in the poultry supply chain. Diverse development of new technologies can meet the specific requirements of AI virus detection in various stages or scenarios throughout the poultry supply chain where onsite, rapid and ultrasensitive methods are emphasized. Systematic approaches or integrated methods ought to be employed according to the application scenarios at every stage of the poultry supply chain to prevent AI outbreaks.

HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine tunnels.Although existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective distortion.So,a new safety helmet-wearing detection method based on deep learning is proposed.Firstly,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural network.Secondly,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction part.This block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small objects.Moreover,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance further.Extensive experiments on open dataset validate the proposed method.It reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art method.To be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version 7.The generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

Research Progress on Analysis and Detection Techniques of Veterinary Drug Residues in Animal Foods

As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.They are an important guarantee for the healthy development of animal husbandry,food safety and public health.However,the irrational use and abuse of veterinary drugs and feed pharmaceutical additives are widespread,causing harmful substances in animal foods and damage to human health,and threatening the sustainable development of the environment and animal husbandry as well.In order to ensure human health,it is urgent to develop a simple,rapid,high-sensitivity,high-throughput and low-cost veterinary drug residue detection technology.In this paper,the sample pretreatment methods and detection techniques for the analysis of veterinary drug residues in animal foods were reviewed.

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

AI-Based Helmet Violation Detection for Traffic Management System

Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.

Food Hygiene and Safety Practices amongst Food Vendors in the Western Area, Sierra Leone

Food safety and hygiene practices require a multisectoral approach including food, water, sanitation, waste management, transport, education, trade, policies and programs that enable safe food preparation, storage and service. Unsafe food can cause illness keeping people from achieving their full potential and death. This was a descriptive study that uses a mixed method approach to derive insights into the characteristics of food vendors related to demography, knowledge, practices, infrastructure, compliance and recommendation for a policymaking framework. Using the Lemeshows’ sample size formula, 473 vendors from formal (restaurants) and informal (cookri-baffa/table top) sites were interviewed and observed. We found from discussions that respondents had a good understanding on how to keep food safe. However, observed practices were poor 93% handled food with their bare hands, 83% did not cover their hair, and 76% did not wear an apron whilst handling, preparing or serving food, 61% did not keep their finger nails clean or short and 57% did not wash their hand before preparing or serving food. Over half (51%) had access to a toilet but 32% reported their use required payment and emphasized their poor condition/inadequate management. Nearly half (47%) of the vending sites did not have a handwashing facility, with soap and water available. Only 7% reported having any authority oversight of food safety. Food safety and hygiene practices in most cookri shops and restaurants was ‘poor’ with very limited surveillance system in place by competent authorities for compliance of food operators. Hand washing, clean surroundings, and covered food were the most common and emphasized practices to mitigate the risks associated with unsafe food.

Detection of Atrazine Residue in Food Samples by a Monoclonal Antibody-based Enzyme-linked Immunosorbent Assay

Atrazine（AT,2-chloro-4-ethylamino-6-isopropyl-amino-s-triazine）has been detected in ground water in several areas of the United States for many years,as well as in China,wherein the growth rate of its gross

Effect of a Nutrition Education Intervention on Food Safety Knowledge, Attitudes and Practices of Mothers of Children under 5 Years in the Peri-Urban Areas of Bobo-Dioulasso: Before and after Study

Background: This study assessed the effect of a nutrition education intervention. This intervention aimed to improve the knowledge, attitudes, and practices of mothers on food safety in the peri-urban areas of Bobo-Dioulasso in Burkina Faso. Methods: A total of 243 mothers of children under 5 years in the peri-urban of Bobo-Dioulasso were administered the FAO questionnaire for food safety knowledge, attitudes, and practices assessment during two cross-sectional surveys, before and after the intervention, in January and October 2017. The intervention included two components consisting of a theoretical phase (counselling and discussion) and cooking demonstrations implemented for ten months. To account for the before-and-after design of the study, the McNemar’s test was used to assess the effect of the intervention on food safety KAP of mothers of children under 5 years. Results: The mean age of mothers was 29 ± 6.2 years and 50.6% of them were between 20 and 29 years old. One for knowledge (cooking thoroughly with, p-value = 0.0001) and another for attitudes (perceived benefits of reheating leftovers before eating them, p-value = 0.0001), significantly increased after the intervention. In terms of food safety practices, all the indicators (cleaning of dirty surfaces, plates and utensils and storage of perishable foods) significantly increased (all p = 0.0001 Conclusion: This study provided some evidence of an effective nutrition education intervention for improving maternal KAP on food safety for their child’s feeding.

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

Teaching Status and Teaching Reform Ideas of Physical Chemistry Experiments for Food Quality and Safety Major

Physical chemistry experiments are an important branch of chemical experiments.In view of problems and shortcomings in physical chemistry experiment teaching of food quality and safety major in Chengdu University,the teaching methods of physical chemistry experiment course of food quality and safety major were explored and practiced,aiming to arouse students enthusiasm for experiments and cultivate their ability of independent learning,comprehensive thinking and independent problem solving.

Current trends in nanomaterials-mediated biosensing platforms and signal amplification strategies for antibiotics detection in dairy products

Dairy products have become one of the most prevalent daily foods worldwide,but safety concerns are rising.In dairy farming,unscrupulous traders misuse antibiotics to treat some diseases such as mastitis in cows,leading to antibiotic residues in dairy products.Rapid,sensitive,and simple detection methods for antibiotic residues are particularly important for food safety in dairy products.Traditional detection technology can effectively detect antibiotics,but there are defects such as complicated pre-treatment and high cost.Biosensors are widely used in food safety due to fast detection speed,low detection cost,strong anti-interference ability,and suitability for the field application.Nevertheless,these sensors often fail to trigger the signal conversion output due to low target concentration.To cope with this issue,some high-efficiency signal amplification systems can be introduced to improve the detection sensitivity and linear range of biosensors.In this review,we focused on:(i)Sources and toxicity of major antibiotics in animal-derived foods.(ii)Nanomaterial-mediated biosensors for real-time detection of target antibiotics in animal-derived foods.(iii)Signal amplification techniques to increase the sensitivity of biosensors.Finally,future prospects and challenges in this research field are discussed.

Colloidal Gold Test Strip for the Detection of Fluoroquinolones in Foods Using an Analyzer

A test strip was developed to rapidly detect fluoroquinolones in foods by colloidal gold immunochromatography method in combination with a food safety analyzer. The results showed that the test strip has the detection sensitivity of 20 μg/L for enrofloxacin, sarafloxacin, difloxacin, ofloxacin, norfloxacin, ciprofloxacin, pefloxacin, flumequine and danofloxacin, and the detection sensitivity of 40 μg/L for enoxacin and oxolinic acid. The test strip consumes only 10 min for detection, and the false positive rate and the false negative rate are both zero. The test strip can accurately, reliably and easily detect residual fluoroquinolones in foods, and is suitable for on-site detection of a large number of samples.

Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

Major allergens in 8 types of allergenic foods and allergen detection techniques

In recent years,the prevalence of allergens in food warning notices,both domestically and internationally,has become the second leading concern after microbial contamination.Among the various factors that threaten human health reported by the World Health Organization,food allergy ranks fourth,and food allergy has become a global security problem.As of now,no definitive treatment for food allergies exists,making the avoidance of allergen-containing foods the most effective prevention method.Consequently,labeling foods with allergen information serves as a crucial warning for allergic populations.Moreover,to enhance comprehension of food allergies,this article provides a brief overview of their definition and sensitization mechanisms.The main focus lies in highlighting the structure of primary allergens found in eight commonly allergenic foods and the resulting clinical symptoms they cause.Additionally,a summary of commonly employed allergen detection techniques is presented,with an analysis of their principles,advantages,and limitations.Looking ahead,the integration of diverse technological approaches to establish an efficient,accurate,and affordable allergen detection method remains a significant trend.This article has certain reference value for understanding the direction of food allergies.