[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-782":3,"related-782":62},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":57,"slug":60,"view_count":21,"doi":8,"paper":8,"created_at":61},782,"[福建省]8月19日台风「沙德尔」生成——未来两天福建沿海地区有中到大雨,局地暴雨,龙眼、葡萄、蜜柚秋梢管护预警","http:\u002F\u002Ffujiansannong.cn\u002Finfo\u002F106035","第18号台风「沙德尔」(热带风暴级)8月19日11时在距离美国关岛东南方向约1150公里洋面上生成。预计「沙德尔」将以每小时15~20公里的速度向北偏西方向移动,强度逐渐增强。8月20日至21日,福建雷雨天气仍将频繁「打卡」,多阵雨或雷阵雨,部分地区有中到大雨、局部暴雨。分区域提示:闽东龙眼果园需加固结果枝;福安葡萄成熟期连日高温高湿炭疽病、白腐病发生风险高,选用敌力脱、达科宁悬浮剂或特克多等杀菌剂;闽北水稻防范穗颈瘟、稻曲病、稻飞虱。",null,"根据气象部门预报，第18号台风“沙德尔”（热带风暴级）于8月19日11时在距离美国关岛东南方向约1150公里洋面上生成。预计“沙德尔”将以每小时15~20公里的速度向北偏西方向移动，强度逐渐增强。8月20日至21日，福建雷雨天气仍将频繁“打卡”，多阵雨或雷阵雨，部分地区有中到大雨、局部暴雨。","中国福建三农网 2026-08-19","2026-08-19T03:00:00Z","报道",10,false,41,{"impact":17,"substance":18,"depth":19,"authority":18,"freshness":20,"relevant":21,"comment":22},12,8,6,7,1,"省级农业气象预警，涉及台风对龙眼等作物影响，信息有一定实用性，但内容较常规。",[24],{"name":10,"url":6},[26,27,28,29,30],"福建","葡萄","台风沙德尔","龙眼","秋梢管护",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},24,true,[27,29],[27,29],[27,29],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"A1",3,[],[55],"水稻",[],[58,59],"台风沙德尔 秋梢管护 福建 葡萄","台风沙德尔 秋梢管护","台风沙德尔秋梢管护福建葡萄-782","2026-08-21T00:05:56.404179Z",{"total":19,"page":21,"page_size":19,"items":63},[64,112,157,221,299,372],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":8,"content":8,"source_name":69,"source_url":8,"published_at":70,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":77,"tags":79,"primary_topic":82,"secondary_topics":83,"classification":84,"search_phrases":106,"slug":109,"view_count":110,"doi":8,"paper":8,"created_at":111},1954,"2026年龙眼科技大会在福州举办 福建培育出冬宝9号、宝石1号、秋香等新品种","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F07\u002F991027754.html","由国家荔枝龙眼产业技术体系、福建省农业科学院主办的2026年龙眼科技大会9月7日在福州举办。福建省农业科学院党委书记、院长余文权介绍，福建作为我国龙眼优势主产区，依托国家龙眼枇杷种质资源圃，培育出冬宝9号、宝石1号、秋香等一批拥有自主知识产权的龙眼新品种。农业农村部南亚热带作物中心副主任黄辉分析说，建议科研单位加快特香型、加工专用型等突破性品种选育，攻坚山地农机、绿色生产、深加工新工艺。","中国农网 2026-09-07","2026-09-07T00:00:00Z",71,{"impact":73,"substance":74,"depth":75,"authority":17,"freshness":20,"relevant":21,"comment":76},20,18,14,"报道龙眼科技大会及新品种培育，属省级农业科技进展，信息具体，信源权威，时效尚可。",[78],{"name":69,"url":67},[80,26,29,81],"种业振兴","农业科技",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":85},{"candidates":86,"agricultural_evidence":46},[87,93,100],{"code":32,"score":88,"supported":46,"title":89,"abstract":90,"tags":92},15,[29],[91,29],"荔枝",[29],{"code":94,"score":95,"supported":46,"title":96,"abstract":97,"tags":99},"F1",4,[],[98],"种质资源",[80],{"code":101,"score":52,"supported":46,"title":102,"abstract":103,"tags":105},"F5",[],[104],"农机",[],[107,108],"农业科技 种业振兴 福建 龙眼","农业科技 种业振兴","农业科技种业振兴福建龙眼-1954",2,"2026-09-09T00:03:55.740839Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":8,"content":117,"source_name":118,"source_url":8,"published_at":119,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"primary_topic":133,"secondary_topics":134,"classification":135,"search_phrases":151,"slug":154,"view_count":155,"doi":8,"paper":8,"created_at":156},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z",65,{"impact":122,"substance":123,"depth":124,"authority":124,"freshness":19,"relevant":21,"comment":125},16,17,13,"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[127],{"name":118,"url":115},[129,130,27,131,132],"智慧农业","智能农机","栽培技术","国际交流",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":136},{"candidates":137,"agricultural_evidence":46},[138,142,147],{"code":32,"score":17,"supported":46,"title":139,"abstract":140,"tags":141},[27],[27],[27],{"code":143,"score":95,"supported":46,"title":144,"abstract":145,"tags":146},"I1",[],[129],[129],{"code":101,"score":21,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[130],[152,153],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516",0,"2026-10-22T00:07:04.061911Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":8,"source_name":163,"source_url":160,"published_at":164,"category":165,"cover_url":8,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":171,"tags":173,"primary_topic":177,"secondary_topics":178,"classification":179,"search_phrases":196,"slug":199,"view_count":52,"doi":200,"paper":201,"created_at":220},3255,"Grape yield estimation using on-the-fly MIMO millimeter-wave radar at operational field speed: A variety-dependent proof of concept","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112395","Accurate pre-harvest grape yield estimation is a critical challenge in precision viticulture, yet current optical approaches (RGB cameras, LiDAR) are fundamentally limited by foliage occlusion and sensitivity to ambient lighting conditions. This paper presents a ground-based grape yield estimation system using a dual 77 GHz Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous-Wave (FM-CW) radar mounted on a rover operating at an average speed of 1.0 m\u002Fs (3.7 km\u002Fh), matching practical vineyard tractor speeds. Unlike prior radar-based studies conducted in static or slow-moving setups, the proposed pipeline achieves continuous 3D reconstruction of vinerows through digital beamforming in the elevation plane, combined with an IMU-fused Kalman filter for point cloud stabilization on irregular terrain. An experimental campaign was conducted on 136 km of scanned vinerows across seven grape varieties and three phenological growth stages (BBCH 75–89) in a commercial vineyard in Gascony, France. Statistical features extracted from 3D radar echo level images are used to train a Support Vector Regressor (SVR) evaluated in cross-validation. The primary out-of-sample result is a cross-validation Mean Absolute Error (CV MAE) of 31.2% on the full multi-variety dataset without any defoliation, achieved against certified wine-vat bulk weights as the ground truth reference. This figure should be contextualized against the ∼ 24–25% discrepancy observed between portable field weighing plates and vat-certified weights across the same dataset, and against manual pre-harvest estimation errors routinely exceeding 30% in viticultural practice. A sensitivity analysis over physical parameters reveals that grape variety is the dominant −and limiting- source of performance heterogeneity: CV MAE ranges from 7% for Tannat to 69% for Baco, demonstrating that a single global model is insufficient for multi-variety deployment and that variety-specific calibration is a necessary condition for operational use. These results are best understood as an architectural proof of concept: the primary contribution is the demonstration that MIMO digital beamforming removes the speed bottleneck of prior mechanically-scanned radar systems, enabling gapless 3D vinerow reconstruction at practical field speeds. The yield estimation results provide a first quantitative characterization of the system’s sensing capability and its variety-dependent limitations and establish a baseline for future variety-specific model development.","准确的采前葡萄产量估测是精准葡萄栽培中的一项关键挑战，然而当前的光学方法（RGB相机、LiDAR）从根本上受到冠层遮挡和对环境光照条件敏感性的限制。本文提出了一种地基葡萄产量估测系统，采用双77 GHz多输入多输出（MIMO）调频连续波（FM-CW）雷达，搭载于以平均速度1.0 m\u002Fs（3.7 km\u002Fh）行驶的移动平台上，与实际葡萄园拖拉机作业速度相匹配。与先前在静态或慢速移动装置中进行的雷达研究不同，所提出的处理流程通过仰角平面上的数字波束成形实现葡萄行连续三维重建，并结合IMU融合卡尔曼滤波器在不规则地形上实现点云稳定。在法国加斯科尼的一个商业葡萄园中，对七个葡萄品种和三个物候生长期（BBCH 75–89）的136 km扫描葡萄行进行了实验。从三维雷达回波强度图像中提取的统计特征用于训练支持向量回归器（SVR），并通过交叉验证进行评估。主要样本外结果为：在完整多品种数据集上、未进行任何去叶处理的情况下，交叉验证平均绝对误差（CV MAE）为31.2%，以认证酒罐散装重量作为地面真值参考。该数值应结合以下背景加以理解：在同一数据集上，便携式田间称重板与酒罐认证重量之间观察到约24–25%的差异，而葡萄栽培实践中人工采前估测误差通常超过30%。对物理参数的敏感性分析表明，葡萄品种是性能异质性的主要——也是限制性——来源：CV MAE从Tannat的7%到Baco的69%不等，表明单一全局模型不足以支持多品种部署，品种特异性校准是实际应用的必要条件。这些结果最好被理解为一种架构层面的概念验证：主要贡献在于证明了MIMO数字波束成形消除了先前机械扫描雷达系统的速度瓶颈，从而能够在实际田间速度下实现无间隙三维葡萄行重建。产量估测结果首次定量表征了该系统的感知能力及其品种依赖性局限，并为未来","Computers and Electronics in Agriculture","2026-09-23T00:00:00Z","论文",84,{"impact":74,"substance":168,"depth":169,"authority":75,"freshness":13,"relevant":21,"comment":170},23,19,"首次在实用田间车速下用MIMO毫米波雷达实现葡萄产量预估，136公里商业园数据与品种依赖性结论具参考价值。",[172],{"name":163,"url":160},[129,174,27,175,176],"精准农业","产量预估","毫米波雷达",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":180},{"candidates":181,"agricultural_evidence":46},[182,187,192],{"code":32,"score":88,"supported":46,"title":183,"abstract":185,"tags":186},[184],"grape",[27,184],[27],{"code":101,"score":52,"supported":46,"title":188,"abstract":189,"tags":191},[],[190],"tractor",[],{"code":143,"score":110,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[129,174],[197,198],"MIMO 毫米波雷达 葡萄 产量预估","葡萄园 雷达 三维重建","MIMO毫米波雷达葡萄产量预估-3255","10.1016\u002Fj.compag.2026.112395",{"doi":200,"openalex_id":202,"authors":203,"venue":163,"cited_by_count":155,"oa_url":160,"card":213,"direction":217,"ingested_from":219},"W7214043753",[204,207,210],{"name":205,"orcid":206},"Etienne Dedic","https:\u002F\u002Forcid.org\u002F0000-0002-1414-019X",{"name":208,"orcid":209},"Dominique Henry","https:\u002F\u002Forcid.org\u002F0000-0002-6016-140X",{"name":211,"orcid":212},"H. Aubert","https:\u002F\u002Forcid.org\u002F0000-0002-6113-0648",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"用双77GHz MIMO雷达在1m\u002Fs车速下重建葡萄藤行并估算产量，验证品种依赖性。","双77GHz MIMO FMCW雷达、数字波束成形、IMU卡尔曼滤波、SVR，1","全品种交叉验证MAE为31.2%，但品种间差异大（Tannat 7%至Baco 69%），需按品种校","农业遥感与作物表型","可研究品种自适应建模与迁移学习，解决多品种部署时单一模型失效问题，并融合光学与雷达数据。","openalex","2026-09-23T23:30:01.516727Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":8,"source_name":227,"source_url":224,"published_at":228,"category":165,"cover_url":8,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":232,"tags":234,"primary_topic":238,"secondary_topics":239,"classification":246,"search_phrases":277,"slug":280,"view_count":110,"doi":281,"paper":282,"created_at":298},2874,"Laboratory Evaluation of Multispectral Grape Quality Grade Prediction with Compensation for Controlled Illumination Variation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-8695\u002Faea952","Abstract Accurate fruit quality assessment is important for selective harvesting in agricultural robotics, where multispectral sensing provides a non-destructive approach but remains sen- sitive to variability in illumination conditions. This study presents a controlled laboratory evaluation conducted in a dark box under halogen illumination with fixed sensor and sam- ple geometry, in which illumination variation was simulated by adjusting lamp intensity. Prediction models for visually assigned grape quality grades were first established under fixed illumination using an AS7265x multispectral sensor with 18 channels from 410 to 940 nm, covering the visible and part of the near-infrared range. Spectral data were prepro- cessed using standard normal variate transformation, standardization, variable importance in projection feature selection, and mixup interpolation. Three machine learning meth- ods, partial least squares regression, support vector regression, and random forest, were evaluated with nested cross-validation, out-of-fold prediction, and 2000-iteration bootstrap resampling. Support vector regression achieved the best performance, with R² = 0.9084 [95% CI: 0.851–0.945], RMSE = 0.3352, and MAE = 0.2374. To address illumination vari- ability, three compensation methods, direct standardization, extended multiplicative scatter correction, and a polynomial per-band fitting method, were implemented and compared across five lamp intensity levels from 20% to 100% of rated power. The three methods and the uncompensated condition reached similar mean accuracy across the five levels, with the exception of extended multiplicative scatter correction, which was lower at every level. Di- rect standardization reduced the spread of the five predictions obtained for the same berry by about 31% relative to no compensation, and gave the highest grade-level accuracy of the four conditions. These results provide laboratory feasibility evidence that direct standard- ization can improve the consistency of grade prediction across controlled illumination levels, and establish a basis for further validation under natural field illumination toward future robotic harvesting applications.","摘要 准确的水果品质评估对于农业机器人中的选择性采收至关重要，其中多光谱传感提供了一种非破坏性方法，但仍对光照条件的变化较为敏感。本研究提出了一种在暗箱中、卤素灯照明下、传感器与样品几何构型固定的受控实验室评估方法，通过调节灯强度来模拟光照变化。首先在固定光照条件下，使用AS7265x多光谱传感器（具有410至940 nm范围内的18个通道，覆盖可见光及部分近红外波段）建立了视觉 assigned 葡萄品质等级的预测模型。光谱数据采用标准正态变量变换、标准化、投影变量重要性特征选择以及mixup插值进行预处理。采用嵌套交叉验证、折外预测和2000次迭代自助重采样，对偏最小二乘回归、支持向量回归和随机森林三种机器学习方法进行了评估。支持向量回归取得了最佳性能，R² = 0.9084 [95% CI: 0.851–0.945]，RMSE = 0.3352，MAE = 0.2374。为解决光照变化问题，实施了直接标准化、扩展多元散射校正和逐波段多项式拟合三种补偿方法，并在额定功率20%至100%的五个灯强度水平下进行了比较。三种方法及未补偿条件在五个水平下达到了相似的平均准确率，但扩展多元散射校正除外，其在每个水平下均较低。与未补偿相比，直接标准化使同一浆果五次预测结果的离散度降低了约31%，并在四种条件中给出了最高的等级准确率。这些结果为直接标准化能够提高受控光照水平下等级预测的一致性提供了实验室可行性证据，并为未来机器人采收应用中在自然田间光照下进一步验证奠定了基础。","Engineering Research Express","2026-09-17T00:00:00Z",66,{"impact":18,"substance":73,"depth":123,"authority":124,"freshness":18,"relevant":21,"comment":231},"实验室条件下多光谱葡萄品质分级与光照补偿研究，方法扎实、结论可靠，但属实验室可行性验证，产业影响有限。",[233],{"name":227,"url":224},[129,235,27,236,237],"农业人工智能","多光谱检测","水果品质分级",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[240],{"code":241,"slug":242,"name":243,"path":244,"parent_code":245},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":247},{"candidates":248,"agricultural_evidence":46},[249,253,259,266,273],{"code":32,"score":88,"supported":46,"title":250,"abstract":251,"tags":252},[184],[27,184],[27],{"code":241,"score":254,"supported":46,"title":255,"abstract":257,"tags":258},11,[256],"multispectral",[256],[],{"code":260,"score":20,"supported":46,"title":261,"abstract":262,"tags":265},"I2",[],[263,264],"机器学习","machine learning",[235],{"code":267,"score":19,"supported":46,"title":268,"abstract":269,"tags":272},"F6",[],[270,271],"农业机器人","robotic harvesting",[],{"code":143,"score":21,"supported":14,"title":274,"abstract":275,"tags":276},[],[],[129],[278,279],"多光谱 葡萄 品质分级","AS7265x 多光谱传感器","多光谱葡萄品质分级-2874","10.1088\u002F2631-8695\u002Faea952",{"doi":281,"openalex_id":283,"authors":284,"venue":227,"cited_by_count":155,"oa_url":224,"card":292,"direction":297,"ingested_from":219},"W7213509882",[285,287,289],{"name":286,"orcid":8},"Guodong Xiu",{"name":288,"orcid":8},"Sára Strakošová",{"name":290,"orcid":291},"Ikuo Mizuuchi","https:\u002F\u002Forcid.org\u002F0000-0003-4657-2613",{"tldr":293,"method":294,"finding":295,"direction":217,"opportunity":296},"在暗箱中评估多光谱葡萄品质分级预测，并比较三种光照补偿方法。","AS7265x多光谱传感器、SNV与VIP预处理、SVR\u002FRF建模、直接标准化等","SVR预测最佳（R²=0.9084）；直接标准化使同果预测离散度降低约31%。","可延伸至自然田间光照下的验证，并融合机器人采摘的实时多光谱分级系统。","农业人工智能与决策模型","2026-09-18T23:30:52.790038Z",{"id":300,"title":301,"url":302,"summary":303,"summary_zh":304,"content":8,"source_name":305,"source_url":302,"published_at":306,"category":165,"cover_url":8,"hotness":13,"is_selected":14,"score":307,"score_detail":308,"sources":311,"tags":313,"primary_topic":318,"secondary_topics":319,"classification":320,"search_phrases":332,"slug":335,"view_count":52,"doi":336,"paper":337,"created_at":371},2630,"Effects of microwave heating on concentration properties and quality attributes of blueberry–grape blended pulp","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1920261","Blended blueberry–grape pulp (BBGP) was concentrated using microwave heating concentration (MHC) to evaluate the effects of microwave intensity (2–6 W\u002Fg), blueberry-to-grape mass ratio (1:1–1:7), citric acid addition (0.2%–0.4%), and granulated sugar addition (10%–15%) on temperature, moisture content, anthocyanin retention ratio, soluble solids content, and sensory quality. Microwave intensity and blueberry-to-grape mass ratio each exhibited a biphasic effect on anthocyanin retention and sensory scores, with optimal levels around 4 W\u002Fg and 1:3 mass ratio, whereas soluble solids content remained unaffected by variations in these parameters or citric acid concentration. Citric acid addition enhanced anthocyanin stability but impaired palatability at excessive levels, while granulated sugar addition did not significantly influence anthocyanin retention yet effectively modulated soluble solids and sweet-sour balance, with 12.5% yielding the highest sensory performance. The recommended processing parameters for MHC of BBGP are 4–6 W\u002Fg, 1:3 blueberry-to-grape mass ratio, 0.3% citric acid, and 12.5% granulated sugar. These findings provide practical guidance for optimizing MHC processes to produce high-quality blended fruit concentrates such as BBGP.","采用微波加热浓缩（MHC）对蓝莓-葡萄混合果浆（BBGP）进行浓缩，以评估微波强度（2–6 W\u002Fg）、蓝莓与葡萄质量比（1:1–1:7）、柠檬酸添加量（0.2%–0.4%）和砂糖添加量（10%–15%）对温度、水分含量、花青素保留率、可溶性固形物含量和感官品质的影响。微波强度和蓝莓与葡萄质量比均对花青素保留率和感官评分表现出双相效应，最佳水平分别约为4 W\u002Fg和质量比1:3，而可溶性固形物含量不受这些参数或柠檬酸浓度变化的影响。添加柠檬酸增强了花青素稳定性，但过量会损害适口性；添加砂糖对花青素保留率无显著影响，但能有效调节可溶性固形物和酸甜平衡，其中12.5%的添加量获得最佳感官表现。BBGP微波加热浓缩的推荐工艺参数为：微波强度4–6 W\u002Fg、蓝莓与葡萄质量比1:3、柠檬酸0.3%、砂糖12.5%。这些发现为优化微波加热浓缩工艺以生产BBGP等高品质混合果汁浓缩液提供了实践指导。","Frontiers in Sustainable Food Systems","2026-09-15T00:00:00Z",63,{"impact":18,"substance":74,"depth":88,"authority":124,"freshness":309,"relevant":21,"comment":310},9,"蓝莓葡萄复合果浆微波浓缩工艺优化的实证研究，给出可落地的参数组合，对果蔬加工与品质保持有参考价值，但属细分领域技术论文，公共影响有限。",[312],{"name":305,"url":302},[314,315,27,316,317],"农产品加工","花青素","蓝莓","微波浓缩",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":321},{"candidates":322,"agricultural_evidence":46},[323,327],{"code":32,"score":122,"supported":46,"title":324,"abstract":325,"tags":326},[184],[27,184],[27,316],{"code":328,"score":21,"supported":14,"title":329,"abstract":330,"tags":331},"E1",[],[],[314],[333,334],"农产品加工 微波浓缩 花青素 葡萄","农产品加工 微波浓缩","农产品加工微波浓缩花青素葡萄-2630","10.3389\u002Ffsufs.2026.1920261",{"doi":336,"openalex_id":338,"authors":339,"venue":305,"cited_by_count":155,"oa_url":302,"card":365,"direction":369,"ingested_from":219},"W7213252471",[340,342,344,347,349,351,353,355,357,359,361,363],{"name":341,"orcid":8},"Xinglong Zhao",{"name":343,"orcid":8},"A. Wangze",{"name":345,"orcid":346},"Yuhan Zhang","https:\u002F\u002Forcid.org\u002F0009-0009-0505-6322",{"name":348,"orcid":8},"Chunhao Kan",{"name":350,"orcid":8},"Yuedong Wang",{"name":352,"orcid":8},"Chenghai Liu",{"name":354,"orcid":8},"Chai Liu",{"name":356,"orcid":8},"Wei Zhao",{"name":358,"orcid":8},"Gang Shi",{"name":360,"orcid":8},"Chao Ma",{"name":362,"orcid":8},"Li Xi",{"name":364,"orcid":8},"Jie Jiang",{"tldr":366,"method":367,"finding":368,"direction":369,"opportunity":370},"研究微波加热浓缩蓝莓-葡萄混合果浆的工艺参数对品质的影响。","微波加热浓缩，考察微波强度、配比、柠檬酸和糖添加量。","最佳工艺为4-6 W\u002Fg、1:3配比、0.3%柠檬酸、12.5%糖，花青素保留和感官最佳。","其他","可探索微波浓缩中花青素降解动力学与在线监测，结合智能控制优化品质。","2026-09-16T23:30:07.980146Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":8,"content":377,"source_name":378,"source_url":8,"published_at":379,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":380,"score_detail":381,"sources":383,"tags":385,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":401,"slug":404,"view_count":21,"doi":8,"paper":8,"created_at":405},2594,"福建农科院 多个龙眼新品种 冬宝9号宝石1号秋香冬香","https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fhtml\u002Ffjsnykxy\u002F2026-09-14\u002F1777733040.html","2026年龙眼科技大会在福州举行。福建省农业科学院果树研究所拥有全国唯一的国家级福州龙眼枇杷种质圃。该所育成冬宝9号是世界第一个杂交龙眼新品种，宝石1号早熟优质，秋香属中晚熟香型品种是国内首个香型龙眼新品种，冬香则是国内最晚熟的龙眼新品种。该所育成的龙眼品种占我省龙眼新种植面积的90%以上。","今天是 2026年9月16日 星期三 \n\n![Image 1](https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Ftemplates\u002F810617426856940000\u002Fimages\u002Flogo.png)\n\n![Image 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7](https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Fpages\u002F810695262618360004\u002Fimages\u002FQQ20260914-115904.png)\n\n 本报讯 （记者 张辉 通讯员 黄雄峰） 近日，由国家荔枝龙眼产业技术体系、福建省农业科学院、华南农业大学主办的2026年龙眼科技大会在福州举行。行业专家围绕龙眼种质创新、品种结构优化、采后加工利用、智慧果园建设等议题开展深度研讨。\n龙眼是我国热带与亚热带地区的重要特色果树，也是我省的优势果品。记者从会议上获悉，2025年，全省龙眼种植面积达44.4万亩，产量27.2万吨，生产规模稳居全国前列。福建省农业科学院果树研究所拥有全国唯一的国家级福州龙眼枇杷种质圃，龙眼种质资源保存量位居世界前列。依托丰富的种质资源，该所已成功培育出一系列具有自主知识产权的优良新品种。\n\n其中，“冬宝9号”是世界上第一个杂交龙眼新品种；“宝石1号”早熟优质、风味浓甜、丰产稳产；“秋香”属中晚熟香型品种，果肉清甜自带香气，是国内首个香型龙眼新品种；“冬香”则是国内最晚熟的龙眼新品种。\n\n据悉，该所育成的龙眼品种占我省龙眼新种植面积的90%以上，一些新品种在四川泸州、重庆江津、云南屏边、广东化州、广西大化等龙眼主产区大面积示范推广。\n\n扫一扫在手机打开当前页\n\n![Image 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总访问量：16508829人\n\n中文域名：福建省农业科学院.公益\n\n[![Image 10](https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Ftemplates\u002F530422591548990000\u002Fimages\u002Fblue.png)](https:\u002F\u002Fbszs.conac.cn\u002Fsitename?method=show&id=B58A832EAD09B003E05310291AACA538)\n\n![Image 11](https:\u002F\u002Fwww.faas.cn\u002Fcms\u002Ftemplates\u002F810617426872330000\u002Fimages\u002Fgzh.jpg)\n\n关注公众号","福建日报 2026-09-14","2026-09-14T00:00:00Z",72,{"impact":74,"substance":73,"depth":88,"authority":254,"freshness":18,"relevant":21,"comment":382},"省级官媒报道的龙眼新品种选育进展，含种植面积、产量等新数据及多个首创品种信息，专业性与信息增量较好，但属细分作物领域，影响范围有限。",[384],{"name":378,"url":375},[80,98,386,29,387],"福建省农科院","新品种选育",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":391},{"candidates":392,"agricultural_evidence":46},[393,397],{"code":32,"score":88,"supported":46,"title":394,"abstract":395,"tags":396},[29],[34,29],[29],{"code":94,"score":110,"supported":14,"title":398,"abstract":399,"tags":400},[],[],[80,98],[402,403],"福建省农科院 新品种选育 种业振兴 种质资源","福建省农科院 新品种选育","福建省农科院新品种选育种业振兴种质资源-2594","2026-09-16T00:03:49.292412Z"]