[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4737":3,"related-4737":105},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":104},4737,"Integrated assessment of agricultural and processing-related chemical hazards across the food chain: glyphosate occurrence and lipid changes in ready-to-eat corn-based foods","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1960698","Introduction Ready-to-eat foods may accumulate chemical hazards from both agricultural production and food processing. These hazards converge in the same food but are usually evaluated separately. This study assessed both types of hazard together in corn-based foods consumed on a Mexican university campus, as an integrated food-chain approach. Methods Ten tortilla and ten tortilla chip samples were collected from campus cafeterias and food stalls. They were screened for glyphosate by high-performance liquid chromatography with diode-array detection (HPLC-DAD), as an indicator of agricultural chemical hazards. Dietary exposure was estimated with a conservative upper-bound scenario and expressed as a hazard quotient (HQ) relative to the acceptable daily intake (ADI). Six paired frying-oil samples, collected before and after frying, were analyzed by Fourier-transform infrared spectroscopy (FTIR) to estimate iodine value (IV) as an indicator of lipid unsaturation. Results Glyphosate signals were detected in 8\u002F10 tortilla and 10\u002F10 tortilla chip samples (90% overall). Screening-level concentrations ranged from 0.107 to 1.994 mg kg −1 . Detected concentrations were above the Mexican maximum residue limit (MRL) for corn grain (0.1 mg kg −1 ) and below the Codex Alimentarius MRL for maize (5 mg kg −1 ); both limits were used only as regulatory reference points. HQ values were 0.0573 for women and 0.0511 for men, so estimated exposure was below the ADI under the assumed scenario. IV decreased significantly after frying (mean decrease 5.00 units; 95% CI 1.30–8.71; p = 0.0178). Discussion Ready-to-eat corn-based foods may carry both agricultural residues and processing-related lipid changes. HQ \u003C 1 does not imply absence of risk. The frequent detection of glyphosate supports continued monitoring and confirmatory analysis by liquid chromatography–tandem mass spectrometry (LC–MS\u002FMS). This integrated approach can support food safety surveillance in institutional food environments.","引言 即食食品可能同时积累来自农业生产的化学危害和来自食品加工的化学危害。这些危害汇聚于同一食品中，但通常被分别评估。本研究以墨西哥某大学校园内消费的玉米基食品为对象，采用食物链综合方法对这两类危害进行了联合评估。方法 从校园食堂和食品摊位采集了10份玉米饼和10份玉米片样品。采用高效液相色谱-二极管阵列检测法（HPLC-DAD）筛查草甘膦，作为农业化学危害的指标。膳食暴露量采用保守的上限情景进行估算，并以相对于可接受每日摄入量（ADI）的危害商（HQ）表示。采集了6对煎炸前后配对的煎炸油样品，通过傅里叶变换红外光谱（FTIR）分析，估算碘值（IV）作为脂质不饱和度的指标。结果 在8\u002F10份玉米饼和10\u002F10份玉米片样品中检测到草甘膦信号（总体90%）。筛查水平浓度范围为0.107至1.994 mg kg⁻¹。检出浓度高于墨西哥玉米粒最大残留限量（MRL）（0.1 mg kg⁻¹），低于国际食品法典委员会玉米最大残留限量（5 mg kg⁻¹）；两个限量仅用作监管参考点。女性的HQ值为0.0573，男性为0.0511，因此在假设情景下估算暴露量低于ADI。煎炸后IV显著下降（平均下降5.00个单位；95% CI 1.30–8.71；p = 0.0178）。讨论 即食玉米基食品可能同时携带农业残留物和加工相关的脂质变化。HQ \u003C 1并不意味着不存在风险。草甘膦的频繁检出支持继续开展监测，并通过液相色谱-串联质谱法（LC–MS\u002FMS）进行确证分析。这种综合方法可支持机构食品环境中的食品安全监督。",null,"Frontiers in Sustainable Food Systems","2026-10-11T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"该研究以食品链整合视角同时评估农业投入品残留与加工过程脂质变化，方法新颖、数据具体，对食品安全监测有参考价值，但属境外校园小样本研究，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"食品安全","农药残留","玉米制品","膳食暴露评估","食品链",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,57,62],{"code":33,"score":19,"supported":52,"title":53,"abstract":55,"tags":56},true,[54],"corn",[35,34,54],[],{"code":40,"score":17,"supported":52,"title":58,"abstract":59,"tags":61},[],[27,60],"food safety",[28,27],{"code":63,"score":64,"supported":52,"title":65,"abstract":66,"tags":69},"E1",6,[],[67,68],"食品加工","food processing",[],[71,72],"墨西哥 玉米饼 草甘膦","即食玉米食品 农药残留","墨西哥玉米饼草甘膦-4737",0,"10.3389\u002Ffsufs.2026.1960698",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":74,"oa_url":6,"card":97,"direction":101,"ingested_from":103},"W7221452266",[79,81,83,85,88,91,94],{"name":80,"orcid":9},"Wendy Jannette Ascencio-López",{"name":82,"orcid":9},"María Teresa Zayas-Pérez",{"name":84,"orcid":9},"T. Alexandra Ferreira",{"name":86,"orcid":87},"Ricardo Munguía-Pérez","https:\u002F\u002Forcid.org\u002F0000-0002-9131-0092",{"name":89,"orcid":90},"Guadalupe Virginia Nevárez‐Moorillón","https:\u002F\u002Forcid.org\u002F0000-0002-1827-6844",{"name":92,"orcid":93},"Teresa Soledad Cid-Pérez","https:\u002F\u002Forcid.org\u002F0000-0003-2382-4072",{"name":95,"orcid":96},"Raúl Ávila-Sosa","https:\u002F\u002Forcid.org\u002F0000-0001-7721-0135",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"整合评估即食玉米食品中草甘膦残留与煎炸油脂变化两类化学危害。","HPLC-DAD检测草甘膦，FTIR测碘值，估算危害商，墨西哥校园采样。","90%样品检出草甘膦，部分超墨西哥限量；煎炸后碘值显著下降。","农业绿色发展与碳","可拓展至其他即食食品链，结合LC-MS\u002FMS确证与多危害联合暴露风险评估。","openalex","2026-10-12T23:30:06.510266Z",{"total":64,"page":22,"page_size":64,"items":106},[107,208,254,314,407,450],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":121,"tags":123,"primary_topic":128,"secondary_topics":134,"classification":136,"search_phrases":180,"slug":183,"view_count":74,"doi":184,"paper":185,"created_at":207},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":117,"substance":118,"depth":117,"authority":119,"freshness":17,"relevant":22,"comment":120},18,22,14,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[122],{"name":113,"url":110},[124,125,126,27,127],"智慧农业","农业人工智能","机器学习","文献计量",{"code":129,"slug":130,"name":131,"path":132,"parent_code":133},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[135],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":137},{"candidates":138,"agricultural_evidence":52},[139,150,155,161,168,175],{"code":129,"score":140,"supported":52,"title":141,"abstract":143,"tags":149},19,[142],"artificial intelligence",[144,126,145,146,147,142,148],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[125,126],{"code":40,"score":151,"supported":52,"title":152,"abstract":153,"tags":154},15,[60],[27,60],[27],{"code":156,"score":151,"supported":52,"title":157,"abstract":159,"tags":160},"I7",[158],"bibliometric",[127,158],[127],{"code":162,"score":64,"supported":52,"title":163,"abstract":164,"tags":167},"H7",[],[165,166],"气候变化","climate change",[],{"code":169,"score":170,"supported":52,"title":171,"abstract":172,"tags":174},"A8",3,[],[173],"food security",[],{"code":176,"score":22,"supported":14,"title":177,"abstract":178,"tags":179},"I1",[],[],[124],[181,182],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":184,"openalex_id":186,"authors":187,"venue":113,"cited_by_count":74,"oa_url":9,"card":201,"direction":205,"ingested_from":103},"W7222920742",[188,190,192,195,198],{"name":189,"orcid":9},"Harang Lee",{"name":191,"orcid":9},"지은서",{"name":193,"orcid":194},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":196,"orcid":197},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":199,"orcid":200},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":202,"method":203,"finding":204,"direction":205,"opportunity":206},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","农业人工智能与决策模型","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":9,"content":9,"source_name":213,"source_url":9,"published_at":214,"category":215,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":216,"sources":222,"tags":224,"primary_topic":228,"secondary_topics":229,"classification":230,"search_phrases":249,"slug":252,"view_count":74,"doi":9,"paper":9,"created_at":253},5060,"云南省农科院质标所参与2026年云南省食品安全宣传周主场活动 自主研发快检技术现场演示","https:\u002F\u002Fwww.yn.xinhuanet.com\u002F20261010\u002F59bfd10da16b4a36ad8de9272595ce5e\u002Fc.html","10月9日,云南省食品安全宣传周在昆明启动,云南省农科院质标所受邀参加主场活动,围绕\"从农田到餐桌\"的源头检测提供技术支撑。重点演示薄层色谱法(2分钟快速鉴别肥料中隐性添加的禁限用农药)、自主研发的丙溴磷等金纳米抗体侧流层析核酸试纸条(灵敏度较商品试纸条提升10倍)以及合成诱导圆偏振发光探针和微针贴片技术(10秒内捕捉稳定信号)。","新华网云南频道 2026年10月10日","2026-10-10T00:00:00Z","报道",{"impact":217,"substance":218,"depth":151,"authority":219,"freshness":220,"relevant":22,"comment":221},16,20,12,5,"省级食品安全宣传周活动，含自主研发快检技术现场演示，技术细节具体，具备一定行业参考价值。",[223],{"name":213,"url":211},[225,27,226,227],"云南省农科院","农业科技","快检技术",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":231},{"candidates":232,"agricultural_evidence":52},[233,237,243],{"code":40,"score":219,"supported":52,"title":234,"abstract":235,"tags":236},[27],[27],[27],{"code":238,"score":170,"supported":52,"title":239,"abstract":240,"tags":242},"F3",[],[241],"肥料",[],{"code":244,"score":170,"supported":52,"title":245,"abstract":246,"tags":248},"F4",[],[247],"农药",[],[250,251],"云南农科院 质标所 快检技术","食品安全宣传周 云南","云南农科院质标所快检技术-5060","2026-10-16T00:13:12.742863Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":261,"published_at":262,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":266,"tags":268,"primary_topic":272,"secondary_topics":273,"classification":274,"search_phrases":295,"slug":298,"view_count":74,"doi":299,"paper":300,"created_at":313},4919,"การออกแบบและพฒนาระบบควบคมดวยไมโครคอนโทรลเลอรสำหรบเครองปลกถวงอกกงอตโนมต","https:\u002F\u002Fdoi.org\u002F10.65411\u002Frst.2026.271208","The cultivation of high-quality bean sprouts is often hindered by inconsistent environmental control and hazardous chemical contamination. This research develops a closed-loop environmental control system, utilizing an Arduino Mega 2560 microcontroller to actively manage the microclimate within a semi-automatic cultivation machine. The system integrates thermoelectric (Peltier) cooling to effectively dissipate accumulated metabolic heat, combined with a precision humidity control mechanism. Performance was evaluated in triplicate (n = 3) against traditional timer-based irrigation systems. Statistical analysis (one-way ANOVA, p \u003C 0.05) demonstrated that the prototype successfully stabilized water temperature (22–25 °C) and relative humidity (80–90%) throughout the cultivation period. These optimal conditions led to a 31–65% increase in total net yield compared to traditional chemical-free methods. While this yield remains slightly lower than intensive commercial practices that rely on growth-accelerating chemicals, laboratory tests by an ISO\u002FIEC 17025 accredited facility confirmed undetectable levels of formaldehyde and sulfur dioxide. This study provides a novel engineering framework for chemical-free precision agriculture, demonstrating significant potential for enhancing food safety and reducing manual labor in commercial-scale production.","优质豆芽的培育常受环境控制不稳定和有害化学污染的制约。本研究开发了一套闭环环境控制系统，利用Arduino Mega 2560微控制器主动调控半自动培育机内的微气候。该系统集成了热电（珀尔帖）冷却以有效散除积累的代谢热，并结合精密湿度控制机制。性能评估以传统定时灌溉系统为对照，进行三次重复试验（n = 3）。统计分析（单因素方差分析，p \u003C 0.05）表明，该原型在整个培育期内成功将水温稳定在22–25 °C、相对湿度稳定在80–90%。这些最优条件使总净产量较传统无化学方法提高了31–65%。尽管该产量仍略低于依赖促生长化学品的集约化商业实践，但由ISO\u002FIEC 17025认可实验室进行的检测确认，甲醛和二氧化硫均未检出。本研究为无化学精准农业提供了一种新颖的工程框架，展示了在商业规模生产中提升食品安全和减少人工劳动的巨大潜力。","Recent Science and Technology","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Frmutsvrj\u002Farticle\u002Fview\u002F271208","2026-10-12T00:00:00Z",67,{"impact":219,"substance":18,"depth":19,"authority":21,"freshness":17,"relevant":22,"comment":265},"论文提出基于Arduino的闭环环境控制系统，实现豆芽无化学精准栽培并显著增产，方法新颖、数据可靠，但属细分领域技术进展，公共影响有限。",[267],{"name":260,"url":261},[124,27,269,270,271],"环境调控","无土栽培","豆芽生产",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":275},{"candidates":276,"agricultural_evidence":52},[277,282,288],{"code":40,"score":278,"supported":52,"title":279,"abstract":280,"tags":281},7,[],[27,60],[27],{"code":176,"score":278,"supported":52,"title":283,"abstract":284,"tags":287},[],[285,286],"精准农业","precision agriculture",[124],{"code":289,"score":64,"supported":52,"title":290,"abstract":291,"tags":294},"H3",[],[292,293],"灌溉","irrigation",[],[296,297],"Arduino Mega 2560 豆芽机","豆芽 无化学 栽培 环境控制","ArduinoMega2560豆芽机-4919","10.65411\u002Frst.2026.271208",{"doi":299,"openalex_id":301,"authors":302,"venue":260,"cited_by_count":74,"oa_url":306,"card":307,"direction":101,"ingested_from":103},"W7221576059",[303],{"name":304,"orcid":305},"Panot Sripakarach","https:\u002F\u002Forcid.org\u002F0000-0002-7861-5406","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Frmutsvrj\u002Farticle\u002Fdownload\u002F271208\u002F182844",{"tldr":308,"method":309,"finding":310,"direction":311,"opportunity":312},"设计基于Arduino Mega 2560的闭环环境控制系统，用于半自动豆芽机，实现无化学添加的高产","Arduino Mega 2560、热电（Peltier）冷却、湿度控制，与定时","系统稳定水温22–25°C、湿度80–90%，净产量提高31–65%，甲醛和二氧化硫未检出。","智慧农业 \u002F 农业物联网","可探索多环境因子协同优化与低成本传感器集成，并验证规模化商业生产中的能耗与产量平衡。","2026-10-14T23:31:07.397575Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":10,"source_url":317,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":324,"tags":326,"primary_topic":331,"secondary_topics":332,"classification":333,"search_phrases":366,"slug":369,"view_count":22,"doi":370,"paper":371,"created_at":406},4206,"Physicochemical, biochemical, and functional characterization of improved cassava varieties resistant to cassava brown streak disease","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1980809","Improved cassava varieties resistant to the Cassava Brown Streak Disease (CBSD) have been released in the eastern Democratic Republic of the Congo (DRC) and subsequently introduced into other agro-ecological zones of the country and across sub-Saharan Africa. However, information regarding their food quality attributes remain limited. This study evaluated the physicochemical, biochemical, and functional characteristics of four improved CBSD-resistant varieties (Mundola, Bukenge, Sikirou, and Mulezi) and two local checks (Nambiyombiyo and Ngorodima) grown in DRC, using standard analytical procedures. Significant varietal differences were observed for most traits ( p \u003C 0.001); CBSD-resistant cassava varieties exhibiting distinct combinations of food-safety, starch-composition, and functional traits, some of which compare favourably with locally preferred varieties while others reveal clear breeding trade-offs. For instance, the improved varieties generally contained lower hydrogen cyanide (HCN) levels than local checks, with Sikirou recording the lowest concentration. They also exhibited higher carbohydrate and starch contents, with Mulezi showing the highest values. In contrast, the local varieties had higher total sugar contents and better ash content, water absorption capacity, and gelation properties. Among the improved varieties, Mulezi and Sikirou encompassed desirable technological and nutritional characteristics, including high starch content and comparatively low HCN, making them promising candidates for processing and value addition. However, the superior functional attributes of the local checks highlight the need to incorporate these traits into future cassava breeding programmes. Further multi-location evaluation of processing performance, consumer acceptability, and effective detoxification practices is recommended to support the wider adoption of these improved varieties.","改良木薯品种抗木薯褐条病（CBSD）已在刚果民主共和国（DRC）东部发布，随后被引入该国其他农业生态区及撒哈拉以南非洲各地。然而，关于其食品品质特性的信息仍然有限。本研究采用标准分析方法，评估了在刚果民主共和国种植的四个改良CBSD抗性品种（Mundola、Bukenge、Sikirou和Mulezi）及两个当地对照品种（Nambiyombiyo和Ngorodima）的理化、生化和功能特性。大多数性状均观察到显著的品种间差异（p \u003C 0.001）；CBSD抗性木薯品种在食品安全、淀粉组成和功能特性方面表现出不同的组合，其中一些与当地偏好品种相比具有优势，而另一些则显示出明显的育种权衡。例如，改良品种的氢氰酸（HCN）含量通常低于当地对照品种，其中Sikirou的浓度最低。改良品种还表现出更高的碳水化合物和淀粉含量，其中Mulezi的数值最高。相比之下，当地品种的总糖含量更高，灰分含量、吸水能力和糊化特性也更好。在改良品种中，Mulezi和Sikirou兼具理想的技术和营养特性，包括高淀粉含量和相对较低的HCN，使其成为加工和增值利用的有前景的候选品种。然而，当地对照品种优越的功能特性凸显了将这些性状纳入未来木薯育种计划的必要性。建议进一步开展多地点评估，考察加工性能、消费者可接受性及有效脱毒实践，以支持这些改良品种的更广泛采用。","2026-10-05T00:00:00Z",79,{"impact":217,"substance":118,"depth":117,"authority":20,"freshness":13,"relevant":22,"comment":323},"针对刚果（金）抗木薯褐条病新品种的理化与功能品质评价，数据翔实、结论明确，对木薯育种与加工利用有参考价值，但属区域性研究，影响范围有限。",[325],{"name":10,"url":317},[327,328,27,329,330],"种业振兴","木薯","农产品加工","抗病育种",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":334},{"candidates":335,"agricultural_evidence":52},[336,341,347,352,356,361],{"code":40,"score":337,"supported":52,"title":338,"abstract":339,"tags":340},4,[],[27],[27],{"code":342,"score":170,"supported":52,"title":343,"abstract":344,"tags":346},"H6",[],[345],"农业生态",[],{"code":348,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},"A5",[],[],[328],{"code":63,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[329],{"code":357,"score":22,"supported":14,"title":358,"abstract":359,"tags":360},"F1",[],[],[327],{"code":362,"score":22,"supported":14,"title":363,"abstract":364,"tags":365},"F2",[],[],[330],[367,368],"刚果金 木薯 抗褐条病品种","木薯 抗病品种 品质评价","刚果金木薯抗褐条病品种-4206","10.3389\u002Ffsufs.2026.1980809",{"doi":370,"openalex_id":372,"authors":373,"venue":10,"cited_by_count":74,"oa_url":317,"card":400,"direction":404,"ingested_from":103},"W7219784046",[374,376,379,382,385,387,389,391,394,397],{"name":375,"orcid":9},"Patrick M. Bakwanamaha",{"name":377,"orcid":378},"Ariel K. Buzera","https:\u002F\u002Forcid.org\u002F0000-0001-5420-8358",{"name":380,"orcid":381},"Jean Mubalama Mondo","https:\u002F\u002Forcid.org\u002F0000-0001-7197-6551",{"name":383,"orcid":384},"Joyce Eka Nshombo","https:\u002F\u002Forcid.org\u002F0009-0003-0058-7462",{"name":386,"orcid":9},"Monique N. Kondo",{"name":388,"orcid":9},"Augustin M. Mugenga",{"name":390,"orcid":9},"Eric Musungayi",{"name":392,"orcid":393},"Mouritala Sikirou","https:\u002F\u002Forcid.org\u002F0000-0002-5164-6456",{"name":395,"orcid":396},"Adebayo Busura Abass","https:\u002F\u002Forcid.org\u002F0000-0003-1376-3608",{"name":398,"orcid":399},"Abdul-Rasaq Adesola Adebowale","https:\u002F\u002Forcid.org\u002F0000-0003-1464-7190",{"tldr":401,"method":402,"finding":403,"direction":404,"opportunity":405},"评估刚果（金）四个抗木薯褐条病改良品种与两个地方品种的理化、生化及功能特性。","采用标准分析程序测定6个木薯品种的理化、生化与功能指标。","改良品种氰化物更低、淀粉更高，但地方品种糖分、灰分及功能特性更优。","其他","可开展多地点加工性能、消费者接受度及脱毒实践研究，以推动抗病品种推广。","2026-10-05T23:30:07.366310Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":9,"content":412,"source_name":413,"source_url":9,"published_at":414,"category":215,"cover_url":9,"hotness":13,"is_selected":14,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":423,"secondary_topics":424,"classification":425,"search_phrases":445,"slug":448,"view_count":170,"doi":9,"paper":9,"created_at":449},4146,"湖北农科院数智质检对话——农业数智质检为农产品质量安全「守门」","https:\u002F\u002Fwww.toutiao.com\u002Fa7690406073396429338","湖北省农业科学院农业质量标准与检测技术研究所（湖北农科院质标所）党委书记、农业农村部食品质量检验测试中心（武汉）负责人周有祥介绍，研究所打造了AI知识引擎、数据驾驶舱、标准查询数据库、自动化检测设备等创新成果，特别是其牵头研发的《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统，精准破解了行业标准查询难、数据分析弱、应用场景单一的痛点。","**——对话湖北省农业科学院农业质量标准与检测技术研究所党委书记、农业农村部食品质量检验测试中心（武汉）负责人周有祥**\n\n当前，智慧农业、数字农业已成为现代农业高质量发展的重要引擎，农业质检作为农产品质量安全的前端，正加速推进数智技术与传统检测的深度融合，为守护群众 “舌尖上的安全” 注入科技动能。近年来，湖北省农业科学院农业质量标准与检测技术研究所（以下简称“湖北农科院质标所”）打造了AI知识引擎、数据驾驶舱、标准查询数据库、自动化检测设备等一系列创新成果，特别是其牵头研发的《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统，精准破解了行业标准查询难、数据分析弱、应用场景单一的痛点，为农产品质量安全监管、种养殖户便捷查询、产业提质增效提供了数字化支撑。近日，湖北农科院质标所党委书记、农业农村部食品质量检验测试中心（武汉）负责人周有祥就党建与科研融合、数字化质检成果、特色农产品提质、成果推广落地等方面与本报进行了对话。\n\n**本报：农业农村部食品质量检验测试中心（武汉）在发挥党员先锋模范作用、党建与科研深度融合、筑牢科研作风等方面，形成了哪些特色工作机制？**\n\n**周有祥：**近年来，我们聚焦农业数智质检领域，坚持把党建工作深度嵌入科研检测全链条，赋能科研攻关和产业发展。在推动党建与科研深度融合方面，我们创新“三学”工作机制：一方面，围绕中央、湖北省委关于智慧农业、农产品质量安全的重大部署组织集中学习，变“孤立学习”为“贯通学习”；另一方面，积极引导党员骨干锚定农业质检数字化转型目标，主动学习机器视觉等前沿技术，变“纸上学习”为“实干践学”。在发挥党员先锋模范作用方面，我们建强“党员先锋岗”“党员突击队”，组织党员骨干聚焦农产品质量安全“保底线、拉高线”的需求。在筑牢科研作风方面，我们推动党小组建在课题一线、实验室一线，把党性教育融入项目研讨、技术试验的全过程。由党员骨干带头严守科研诚信、恪守学术规范，在样品检测、标准验证、系统开发等环节亮身份、作表率，以严实党风涵养清正学风，锻造出一支作风过硬、善打技术攻坚战的农业质检科研队伍。\n\n**本报：相较于传统查询模式，《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统在数据维度、可视化分析、实操应用上实现了哪些重要突破？目前有哪些渠道便于公众获取和了解？**\n\n**周有祥：**《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）是守护群众“舌尖上的安全”的强制性国家食品安全标准。在相关主管部门的指导下，我们构建了《食品安全国家标准 食品中农药最大残留限量》（GB2763—2026）可视化查询和分析系统，精准破解了行业标准查询难、数据分析弱、应用场景单一的痛点，为农产品质量安全监管、种养殖户便捷查询、产业提质增效提供了数字化支撑。\n\n在数据维度层面，该系统融合了GB2763限量值与食品类别、农药类别、检测方法、禁限用规定、绿色食品要求以及特色小宗作物等多源数据，尤其破解了组限量情形下的食品限量查询难题。在可视化分析层面，依托商业智能（BI）技术，该系统能将限量数据转化为可联动的数据驾驶舱，直观展示单一食品中的农药限量、单一农药在不同食品中的限量等多维信息，为生产经营、监管执法、检测认证和贸易消费等各类主体提供精准服务。在实操应用层面，该系统支持联动查询，通过单点触发、多维度同步刷新，实现多字段关联检索，节约了跨库检索、人工匹配的时间，可快速完成限量判定。目前，公众可通过农业农村部农产品质量安全中心“农安科普”平台以及湖北省农科院质标所公众号查询使用该系统。\n\n**本报：湖北农科院质标所如何立足检测、标准、评价三大核心职能，系统性推动湖北特色农产品质量安全水平与品质双向提升？**\n\n**周有祥：**我们长期承担湖北省农产品质量安全监测任务，严格开展风险监测和监督抽查，紧盯重点品种、重点区域以及农兽药残留等突出风险，做到及时发现问题、研判风险、服务监管。近年来，湖北省农产品质量安全监测合格率稳定保持在98%以上。湖北农科院质标所围绕湖北稻米、小龙虾、莲藕等优势特色产业开展标准研制。以莲藕为例，我们联合武汉市农业科学院、全省莲藕龙头企业，充分发挥湖北水生蔬菜的科研、产业和资源优势，先后牵头制定了《绿色食品 藕及藕制品》《莲藕生产全程质量控制技术规范》等行业标准，将质量安全要求贯穿产地环境、生产管理、投入品使用、采收加工等全环节，引导产业由‘经验生产’向‘标准化生产’转型。下一步，我们将持续开展优质农产品的品质评价与分等分级研究，推进绿色、有机、名特优新和地理标志等优质农产品的评价认证工作，让更多湖北优质农产品被市场看见、被消费者认可。\n\n**本报：湖北农科院质标所建成了覆盖湖北省十大农业产业链的湖北农业地方标准库、全国“菜篮子”数据驾驶舱、省级抽样监管平台等数字化成果。请问这些数智化成果后续在省内外推广落地、规模化应用方面有哪些具体部署？**\n\n周有祥：一直以来，湖北农科院质标所始终重点推动科研成果的场景化落地应用，着力打通农产品生产、追溯、抽检、监管、认证全流程数据链。湖北农业地方标准库将持续聚焦湖北十大农业产业链，完善标准动态更新、分类检索和场景化应用功能，把标准要求转化为生产主体查得到、看得懂、用得上的技术指引；全国“菜篮子”数据驾驶舱将进一步强化产量统计、多源数据汇聚、风险分析和趋势研判功能，提升监管服务能力；省级抽样监管平台重点推进抽样、审核、追溯等环节在线协同，提升监管工作的精准性和时效性。目前，以GB2763可视化数据库为代表的数智化成果正在主管部门的指导下迭代升级，后续将在全国推广应用。\n\n**本报：当前湖北正加快建设科技农业、绿色农业、质量农业、品牌农业，打造国家优质农产品生产区。下一步，湖北农科院质标所将如何把现有数智化科研成果、标准体系优势、品质评价能力深度结合，进一步拓宽应用场景、加大成果推广力度？**\n\n**周有祥：**湖北农科院质标所将把数智化科研成果、标准体系优势、品质评价能力三者融合贯通，重点做好夯实智能底座、强化场景开发等三方面工作：\n\n一是夯实智能底座，推动算力平台建设，推动法律法规、标准规范、科研文献以及检测数据融合，构建农产品质量安全垂直领域知识图谱，加强智能体工程化技术研究，为技术落地、场景应用、预警研判提供稳定可靠的算力与数据保障；二是强化场景开发，系统推动面向生产、监管、消费主体的农安数智化应用落地，重点在标准示范推广、电子承诺达标合格证应用、追溯体系落地等场景做好赋能支撑，探索构建农产品质量安全人工智能标准体系，提升数智化服务水平；三是做好农检应用，聚焦农产品质量安全检测实验室的自动化、智能化需求，探索开展自动化检测分析、数字化质量管理、智能化报告解读等技术研究，重点破解检测质效难题。**（赵平 叶家茂）**","今日头条·2026年9月底","2026-09-30T00:00:00Z",75,{"impact":118,"substance":218,"depth":19,"authority":13,"freshness":64,"relevant":22,"comment":417},"省级农科院数智质检成果的深度对话，含GB2763-2026可视化系统等实质信息，具行业参考价值。",[419],{"name":413,"url":410},[124,125,421,422,28],"农产品质量安全","农业标准",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":426},{"candidates":427,"agricultural_evidence":52},[428,433,437,441],{"code":40,"score":20,"supported":52,"title":429,"abstract":431,"tags":432},[430],"农产品质量",[27],[421,28],{"code":244,"score":170,"supported":52,"title":434,"abstract":435,"tags":436},[],[247],[],{"code":176,"score":22,"supported":14,"title":438,"abstract":439,"tags":440},[],[],[124],{"code":129,"score":22,"supported":14,"title":442,"abstract":443,"tags":444},[],[],[125],[446,447],"湖北农科院 数智质检 GB2763","农药最大残留限量 可视化查询系统","湖北农科院数智质检GB2763-4146","2026-10-04T00:09:08.196478Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":9,"source_name":10,"source_url":453,"published_at":456,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":457,"score_detail":458,"sources":460,"tags":462,"primary_topic":466,"secondary_topics":467,"classification":468,"search_phrases":482,"slug":485,"view_count":278,"doi":486,"paper":487,"created_at":502},3820,"Aptamer-based surface plasmon resonance sensor for α-amanitin","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1946446","α-Amanitin is a highly toxic amatoxin associated with poisonous mushroom poisoning and represents a significant threat to food safety and public health. The complexity and time requirements of conventional instrumental methods highlight the need for rapid and sensitive screening strategies. Herein, we developed a label-free, direct, and regenerable aptamer-based surface plasmon resonance (SPR) biosensor for the detection of α-amanitin. Three previously reported aptamers were systematically screened under identical SPR conditions, and Apt41-BT was selected as the optimal recognition element based on its superior experimental response. The selected aptamer was immobilized on a streptavidin-modified sensor chip through a biotin–streptavidin interaction, enabling real-time monitoring of α-amanitin binding without the need for additional labeling or amplification. The resulting biosensor exhibited a concentration-dependent response over the range of 5–500 nM, with a practical detection limit of 5 nM. Importantly, the sensor surface could be regenerated for repeated measurements while maintaining stable performance. The applicability of the proposed platform was further demonstrated using spiked mushroom samples, which showed satisfactory recoveries and good agreement with LC–MS results. This work establishes an experimentally screened aptamer–SPR sensing strategy that integrates direct detection, sensor regeneration, and mushroom-matrix validation, providing a rapid and potentially field-deployable approach for α-amanitin screening.","α-鹅膏蕈碱（α-Amanitin）是一种与毒蘑菇中毒相关的高毒性鹅膏毒肽，对食品安全和公共卫生构成重大威胁。传统仪器方法复杂且耗时，凸显了对快速、灵敏筛查策略的需求。在此，我们开发了一种基于适配体的无标记、直接且可再生的表面等离子体共振（SPR）生物传感器，用于检测α-鹅膏蕈碱。我们在相同的SPR条件下系统筛选了三种已报道的适配体，并基于其优异的实验响应选择Apt41-BT作为最佳识别元件。所选适配体通过生物素–链霉亲和素相互作用固定在链霉亲和素修饰的传感器芯片上，从而无需额外标记或扩增即可实时监测α-鹅膏蕈碱的结合。所得生物传感器在5–500 nM范围内表现出浓度依赖性响应，实际检测限为5 nM。重要的是，传感器表面可再生以进行重复测量，同时保持稳定性能。所提出平台的适用性进一步通过加标蘑菇样品得到验证，显示出令人满意的回收率并与LC–MS结果良好一致。本研究建立了一种经实验筛选的适配体–SPR传感策略，整合了直接检测、传感器再生和蘑菇基质验证，为α-鹅膏蕈碱筛查提供了一种快速且可能适合现场部署的方法。","2026-09-29T00:00:00Z",74,{"impact":151,"substance":218,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":459},"适配体-SPR无标记传感器实现α-鹅膏蕈碱快速检测，方法新颖且经蘑菇基质验证，对食品安全检测有实用价值。",[461],{"name":10,"url":453},[27,463,464,465],"生物传感器","真菌毒素","快速检测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":469},{"candidates":470,"agricultural_evidence":52},[471,475],{"code":40,"score":278,"supported":52,"title":472,"abstract":473,"tags":474},[],[27,60],[27],{"code":476,"score":64,"supported":52,"title":477,"abstract":478,"tags":481},"B5",[],[479,480],"蘑菇","mushroom",[],[483,484],"α-amanitin 适配体 SPR 传感器","蘑菇毒素 快速检测","α-amanitin适配体SPR传感器-3820","10.3389\u002Ffsufs.2026.1946446",{"doi":486,"openalex_id":488,"authors":489,"venue":10,"cited_by_count":74,"oa_url":453,"card":497,"direction":404,"ingested_from":103},"W7214794484",[490,492,494],{"name":491,"orcid":9},"Qian Li",{"name":493,"orcid":9},"Lei Lei",{"name":495,"orcid":496},"Sha Sha","https:\u002F\u002Forcid.org\u002F0000-0002-4514-5671",{"tldr":498,"method":499,"finding":500,"direction":404,"opportunity":501},"开发基于适配体的SPR生物传感器，无需标记即可快速检测蘑菇中的α-鹅膏毒肽。","筛选三条适配体，将Apt41-BT固定于链霉亲和素芯片，SPR实时检测并验证加标","传感器在5–500 nM范围呈浓度依赖响应，检测限5 nM，可再生且与LC-MS结果一致。","可探索适配体SPR传感器在农产品多种真菌毒素现场快速筛查中的集成与便携化。","2026-09-30T23:30:07.583787Z"]