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.

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.

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.

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.

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.……

Food Microbiology and Safety 实验课的教改探索

该文作者所在大学与新西兰梅西大学合作开展了食品科学与工程专业国际化培养本科教育项目,全英文课程Food Microbiology and Safety是该项目的核心课程之一。该实验课以应用性为前提,以课程思政为引领,强调食品微生物学与食品安全学的融合,中外师资合作开展了系列教学探索与实践。课程设计以食品产品和食品生产为情境,以微生物安全为话题,模拟真实生产场景,丰富内涵和拓展外延;自主设计实验激发主观能动性、自主规划进程锻炼统筹能力、线上资源平台支撑自主学习与评价、随机分组培养团队合作能力。实践表明,课程有效实现了食品微生物学与食品安全学的有机融合,目标驱动型实验教学过程更利于激发学生的主动性,大量综合性和设计性实验提高了课程的两性一度。与同期对照班级相比,该项目所培养学生具有更强的专业素养和实践能力,为食品专业其他实验课程的教学改革提供了参考。

Filtration assisted pretreatment for rapid enrichment and accurate detection of Salmonella in vegetables

Rapid detection of target foodborne pathogens plays more and more significant roles in food safety,which requires the efficiency,sensitivity,and accuracy.In this research,we proposed a new st rategy of isothermal-molecular-amplification integrated with lateral-flow-strip for rapid detection of Salmonella without traditional enrichment-culture.Th e designed syringe-assisted-filtration can contribute to simultaneous collection and concentration of target bacterium from vegetable samples in just 3 min,resolving the drawbacks of traditional random sampling protocols.After simple and convenient ultrasonication,samples can be directly amplified at 39℃ in 25 min and the amplicons are qualitatively and quantitatively analyzed with the designed lateral-flow-strip in 5 min.Finally,satisfied results have been achieved within 40 min,which greatly improve the efficiency while the accuracy is also guaranteed.Furthermore,all detection steps can be completed under instrument-free conditions.This method will hold great promise for target pathogen detection in the resource-limited district,or for emergency on-site identification.

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.

Fire Detection Algorithm Based on an Improved Strategy of YOLOv5 and Flame Threshold Segmentation

Due to the rapid growth and spread of fire,it poses a major threat to human life and property.Timely use of fire detection technology can reduce disaster losses.The traditional threshold segmentation method is unstable,and the flame recognition methods of deep learning require a large amount of labeled data for training.In order to solve these problems,this paper proposes a new method combining You Only Look Once version 5(YOLOv5)network model and improved flame segmentation algorithm.On the basis of the traditional color space threshold segmentation method,the original segmentation threshold is replaced by the proportion threshold,and the characteristic information of the flame is maximally retained.In the YOLOv5 network model,the training module is set by combining the ideas of Bootstrapping and cross validation,and the data distribution of YOLOv5 network training is adjusted.At the same time,the feature information after segmentation is added to the data set.Different from the training method that uses large-scale data sets for model training,the proposed method trains the model on the basis of a small data set,and achieves better model detection results,and the detection accuracy of the model in the validation set reaches 0.96.Experimental results show that the proposed method can detect flame features with faster speed and higher accuracy compared with the original method.

BARN:Behavior-Aware Relation Network for multi-label behavior detection in socially housed macaques

Quantification of behaviors in macaques provides crucial support for various scientific disciplines,including pharmacology,neuroscience,and ethology.Despite recent advancements in the analysis of macaque behavior,research on multi-label behavior detection in socially housed macaques,including consideration of interactions among them,remains scarce.Given the lack of relevant approaches and datasets,we developed the Behavior-Aware Relation Network(BARN)for multi-label behavior detection of socially housed macaques.Our approach models the relationship of behavioral similarity between macaques,guided by a behavior-aware module and novel behavior classifier,which is suitable for multi-label classification.We also constructed a behavior dataset of rhesus macaques using ordinary RGB cameras mounted outside their cages.The dataset included 65?913 labels for19 behaviors and 60?367 proposals,including identities and locations of the macaques.Experimental results showed that BARN significantly improved the baseline SlowFast network and outperformed existing relation networks.In conclusion,we successfully achieved multilabel behavior detection of socially housed macaques with both economic efficiency and high accuracy.

Environment and Awareness Influencing Food Safety in the Western Area, Sierra Leone

Food safety, nutrition, health and wellbeing are inextricably linked. This study investigated the environmental, awareness and practices amongst both food vendors and consumers in and around Freetown the capital of Sierra Leone. The population of the area has vastly out-grown the infrastructure laid down before and since independence. De-centralization of authority to local councils as recommended post-war has only been partial. The role of local versus central government in the planning and maintenance of water, sanitation and market-infrastructure and related policies was explored. This is a qualitative study that used semi-structured interviews with vendors, consumers and 29 key informants. Amongst an estimated population of 5386 actors in food services a sample size of 309 vendors, consumers in and around the 12 busiest market hubs in Western Area Urban and Rural Districts were sampled. Most of the respondents described “food safety” as “good-to-eat, well taken care-off, and free from germs”. The respondents cited that main causes to unsafe food are poor hygienic practices: 38%, contamination by flies: 28%, uncovered food/ improper handling/poor personal hygiene: 21%, and environmental factors, such as improper refuse disposal: 11%. Respondents expressed that consuming unsafe foods can lead to diarrhoea: 34%, food poisoning: 24%, vomiting: 17%, stomach ache/pain: 16%, or typhoid: 9%. Various measures to improve food safety were cited as hand washing: 60%, cleaning surroundings: 57%, covering food: 56%, washing utensils: 52%, covering hair: 42% and using a face mask: 32%. However almost 76% of food vendors interviewed were trading near gutters, where muddy/dirty water settled, with slippery floors, discarded plastic waste, flies and inadequate water, sanitation, and/or storage facilities. None of the food vendors had had any formal training on food safety. Despite progress recently made in solid and liquid waste management during the “Transform Freetown” agenda much more improvement in infrastructure and behaviour change is required. Similarly, despite improvements in formal market facilities the pattern of informal street trading remains the most prevalent especially for the poor. The link between “safe food” and good health was only partially understood, whilst the widespread application of recommended practices to reduce risks was lacking due in part to inadequate infrastructure. As the importance of food safety becomes better understood by administrators and the public the investments required in infrastructure and social and behavioural change will contribute towards a healthier environment and outcomes.

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

Trends of Laboratory-Detected Heavy Metals in Children: Solutions for Heavy Metal Contamination in Infant Food Products

In 2019, an investigation by the U.S. House of Representatives revealed major infant food conglomerates had products with high levels of arsenic, lead, cadmium, and mercury, posing concerns for infants’ vulnerability to the neurotoxic effects of these metals. Trends of laboratory-detected heavy metals were analyzed in children aged zero to five from 1999-2020, providing insights on heavy metal contamination in infant food products. Utilizing National Health and Nutrition Examination Survey (NHANES) data, mean heavy metal levels in children were calculated, considering gender, race, and income-to-poverty ratio as proxies for assessing associations with increasing heavy metal rates in infant food. Findings indicated an overall decrease in mean concentrations over time, though remaining elevated. Black children exhibited higher lead levels than the overall average, while the Asian subgroup displayed higher levels of total blood mercury and cadmium levels. Lack of internal standards in regulatory bodies, particularly the FDA, exacerbates the issue, with no legally enforceable guidelines or strict maximum levels for heavy metals in infant foods. Urgent FDA interventions are needed, addressing contamination at the sources of raw materials, implementing transparent and extensive product testing, and comprehensive manufacturer labeling to inform consumers about elevated heavy metal levels in infant products.

A Study to Assess Basic Food Safety Knowledge among University Students

Background: University students who visit off-campus university cafeterias during their academic life may lack some basic food safety knowledge. Study Aim: To evaluate university students’ basic knowledge of food safety in off-campus cafeterias. To explore whether the knowledge reported was influenced by the students’ educational backgrounds. Objects and Methods: Students from three different universities in Saudi Arabia were included. University A represented medical majors, University B represented a mix of both majors medical and non-medical, and University C represented only non-medical majors. Data Collection: An electronic questionnaire was circulated among 500 university students to evaluate their knowledge of food safety. The questionnaire had three main sections covering the cafeteria’s location as a food serving area, staff hygiene skills, and food preparation standards. The 5-point Likert scale consists of the following points: yes, maybe, no, it does not matter, I do not know. Only a “yes” answer indicated a positive response and proper awareness of the subject. Results: A total of 408 students participated, and the results showed significant differences in student knowledge between the three universities. In fact, University A’s students had better overall knowledge than University C’s students (0.001 < P). However, there was no significant difference between University A’s and University B’s students. The positive responses for the first part of the questionnaire which focused on off-campus cafeteria location, for 18 statements were from 26% to 90.4%. The positive responses for the second part of the questionnaire that focused on staff skills, for 8 statements were from 54.4% to 87.3%. Finally, the positive responses for the third part of the questionnaire which focused on food preparation, for 9 statements were 54.4% to 87.3%. Conclusion: Based on the overall comparison among universities, University A’s students have more knowledge in food safety in comparison to University C’s. University B’s students showed no significant difference with the latter two universities. The overall results suggest that students’ educational backgrounds can contribute effectively to the general knowledge of food safety.

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.