农药残留检测关键用酶固定化研究进展

为保障消费者食用安全,迫切需要研发农产品和食品中的农药残留快速检测技术.酶抑制法检测是目前农药残留快速检测技术中的主要研究方向之一,而酶的固定化是用基于酶抑制法原理对农药残留检测研究中的重要步骤.通过物理或化学的方法高效地将酶固定于载体上,同时保持酶的催化活性是开发各类基于酶抑制法检测农药残留传感器的关键.本文将从固定...     

初级农产品质量安全检测技术研究进展

综述了初级农产品质量安全检测技术研究进展,包括样品前处理检测技术、仪器检测技术、确证检测技术和快速检测技术。样品前处理检测技术中的免疫亲和净化小柱在生物毒素检测中被广泛使用;确证检测技术中色谱—质谱连用技术因定性定量准确,在有条件的实验室中得到了快速发展;快速检测技术中酶抑制法简便、快速等优点使这项技术与大型仪器互补,在农药残留检测中被大力推广。     

激光喇曼光谱技术在食品科学中的应用

激光喇曼光谱技术是一种非侵入、非弹性的光散射技术,它能够无损地提供丰富的分子结构和物质成分的信息。近来它在食品工业领域表现出很大的应用潜力。本文综述了激光喇曼光谱技术在食品科学中的应用及其新进展。主要包括果蔬农药残留的检测、肉类产品质量检测、伪劣食品鉴定、食物蛋白的研究以及食品加工监控等方面的应用。并对喇曼光谱技术在这些方面的应用前景作了进一步的展望。     

农产品安全检测中生物传感器的应用

围绕农药和化肥残留、重金属污染、激素和兽药残留以及生物污染物等农产品安全性的主要指标．该对生物传感器在农产品安全检测领域中最新的发展动态进行了综述,并对其发展前景和存在问题进行了评述。     

广州地区农产品中有机磷农药残留调查

采用国家标准方法气相色谱(GC)法进行分析,通过对蔬菜、水果、粮食中16种有机磷农药残留量的检测,了解有机磷农药在广州地区农产品中的残留及其分布情况.50份农产品样品中有机磷农药各组分总的检出率为96.0%,其中蔬菜、水果、粮食检出率分别为96.8%、100%、80.0%.50份农产品样品中有机磷农药各组分总的超标率为36.0%,其中蔬菜、水果、粮食超标率分别为41.9%、21.4%、40.0%,16种有机磷农药中以马拉硫磷的超标率最高,为32.0%.不同组分有机磷农药在农产品中的残留量,以杀螟磷的平均含量最高,为1308.7μg·kg^-1,其次是辛硫磷和甲胺磷,分别为970.1μg·kg^-1和737.6μg·kg^-1;因此应提倡合理使用农药,加强农药使用的引导和监管.     

用于柑桔成熟度无损检测的色度频度序列法研究

农产品内部品质无损检测技术是确定水果最合适的采收期和对成熟度不一致的农产品进行准确分级的关键．为了建立利用计算机视觉技术进行柑桔成熟度的无损检测的方法,本研究建立了用于柑桔成熟度检测的计算机视觉系统,研究了柑桔图像颜色的描述方法,通过分析比较,认为在利用水果可见光彩色图像检测水果成熟度时,宜采用HSI颜色模型空间;提出了用与各个色度对应的像素在图像中出现的频度构成的频度序列描述图像的颜色信息的新方法,并利用人工神经网络方法建立了根据柑桔图像的色度频度序列判断柑桔成熟度的映射器,这一映射器检验252只成熟度不同的尾张系柑桔的结果为,对成熟果实和未熟果实的判断正确率分别为 79 1％和63．6％,总的判断正确率为77．8％,这表明尾张系柑桔果实的表皮颜色与成熟度之间具有相关性,可以通过利用计算机视觉技术测定柑桔的表皮颜色信息来判断柑桔的成熟度．     

食品中农药残留的新型生物检测技术研究进展

食品安全是关系国计民生的重大战略问题。农药残留（简称农残）是造成食品质量与安全问题的重要因素。食品中农药残留不仅发生率高，而且超标严重，涉及范围广，长期接触或食用含有农药残留的食品会危害人体健康。传统的农残分析技术已经很难满足多样化食品安全检测的实际需求，现代新型生物技术因特异性强、灵敏度高、简单快速等优势，在食品的农残检测中得到广泛应用和持续发展。分析了食品中农药残留及检测技术现状，综述了酶联免疫吸附测定法、荧光免疫法和生物条形码免疫法等分析技术，及电化学、光学、压电传感器等生物传感技术在食品农残检测中的应用进展，讨论了各种技术的优势和局限，以期为相关研究提供参考，从而进一步提高我国食品农残的检测效率，保障食品安全，促进我国食品行业的可持续发展。     

传感技术在农药残留快速检测中的应用

从生物传感、光学传感等传感技术着手,简述了传感技术在农药残留快速检测中的应用进展。     

关于农药残留检测方法的创新策略分析

为了有效的对害虫、病菌以及杂草等物种的生长进行控制,并使农作物产量得到提高,则致使农药被广泛的应用在农作物中。而农药残留不断的累积以及出现的非靶向副作用直接影响着人类健康和生态环境的平衡,并导致出现了很多严重疾病从而使农药残留检测逐渐的受到人们的广泛关注和重视,而农药残留分析所具有的重要作用也在不断的提升。本文主要对农药残留检测技术创新策略进行了具体的分析,并对农药残留检测手段不断创新的策略进行了详细的介绍,以此对该领域发展的趋势进行展望。     

激光在生命科学研究中的应用

我们知道一切生物体赖于生存的能源最终都来自“光能”,因此光与生物体相互作用的研究是生命科学中的一个主要问题,60年代初,激光器的发明为这一领域的研究提供了一种新型的工具,激光技术和生物,医学工程技术相结合形成了一门新兴的边缘学科—激光生命科学,它主要包括两个方面的内容,其一是用激光来刺激或操纵生物体从而引起生物体结构,性质和功能的变化,这是激光育种,激光生物基因诱变和激光医学等的基础;其二是以激光作为分析和检测的工具来研究生物分子和细胞的结构、性质、功能以及生物物理和生物化学的反应机制,这是研究和探索生命现象奥秘的一种有效手段,近年来关于激光与生物体相互作     

基于酶生物传感器法检测果蔬中毒死蜱残留的提取工艺研究

使用自主研制的酶生物传感器型农残检测仪进行农药残留检测,通过筛选可测果蔬种类、调整优化样品处理大小、样品加标后静置时间、样品与提取液比例和振荡提取速度,提高检测用酶对农药的抑制敏感性,从而达到降低农药检出限、提高回收率的目的。主要设置的参数如下:样品处理方式分为打碎、切碎(切成1 cm×1 cm和1. 5 cm×3 cm大小);加标后静置时间为5 min、15min、30 min、60 min和90 min;料液比为1∶1、1∶2. 5、1∶5、1∶7. 5和1∶10;振荡提取速度为0 r/min、110 r/min、190 r/min和225 r/min;主要测定的农药为毒死蜱。结果发现,不同果蔬品种对固定化酶的抑制率影响小,样品大小为1 cm×1 cm、加标后静置时间为5 min、料液比为1∶1、加入提取剂后的振荡速度为190 r/min为最佳前处理方式组合。酶生物传感器农残检测仪能够满足快速检测果蔬中有机磷农药残留的需要。     

A method for comparing pesticide usage patterns between farmers

A method is described for standardizing data from surveys of insecticide and fungicide usage, to enable comparisons to be made between farmers. Pesticide usage by the fruit and vegetable farmers surveyed was initially coded in units, and then standardized by taking each farmer's deviation from a regional average for the crop, expressed as standard measure. The vegetable farmers studied were less variable in their usage of pesticides for the control of aphid, caterpillar and blight on crops harvested late in the season rather than early (cabbage, lettuce, potatoes and sprouts). There was evidence that farmers did not heed the Beaumont period warnings issued by the Agricultural Development and Advisory Service (ADAS) for blight attack on potatoes. Also on potato crops, insecticide use against aphids was influenced by the level of fungicide usage for blight control. Standardized insecticide and fungicide usage values for individual farmers were very uniform. A two-way analysis of variance showed that most of the variation in the model was accounted for by variation between farmers rather than by variation between crops on the same farm. By expressing pesticide usage data in this standardized form, one can measure the element in pesticide usage which is not related to pest incidence. This can then be used to study the influence of other factors on pesticide usage.     

Determination of tsumacide residues in vegetable samples using a flow-injection chemiluminescence method.

A sensitive, simple and rapid flow-injection chemiluminescence (FI-CL) method is described to determine tsumacide pesticide residue based on the CL reaction of the alkaline degradation product of tsumacide with acidic KMnO(4) when rhodamine 6G was present. Under the optimum conditions, the relative CL intensity is linear with the concentration of tsumacide in the range of 2.0 x 10(-3)-0.20 mg/L. The detection limit is 6.6 x 10(-4) mg/L (3sigma) and the relative standard deviation for 2.0 x 10(-2) mg/L tsumacide solution was 2.28% (intra-day) and 4.85% (inter-day). The proposed method has been applied to determine the residue of tsumacide in vegetable samples and the recovery test is very satisfactory.     

敌敌畏、丙溴磷及丁草胺在菜用大豆上的安全性试验

采用气相色谱法及田间示范试验方法,并根据GB2763-2005、日本及欧盟规定的最高残留限量(MRL)要求,研究了敌敌畏、丙溴磷与丁草胺3种农药在菜用大豆上的安全性。结果表明,敌敌畏的安全间隔期为3 d,在菜用大豆上进行合理使用最终产品质量是安全的;丙溴磷产品内销(参照甘蓝)的安全间隔期为18 d,出口日本及欧盟的安全间隔期为28 d,在菜用大豆生长中后期及后期使用不当易引起最终产品农残超标;在菜用大豆种植后当天,合理施用丁草胺,其最终产品质量是安全的。     

Investigating Factors Affecting Unit-to-Unit Variability in Non-Systemic Pesticide Residues by Stochastic Simulation Modelling

The estimation of acute dietary intake plays a key role in the safety assessment of pesticide residues and needs knowledge of the unit-to-unit variability in residues. There is limited knowledge of contributions of factors to often observed large unit-to-unit variability in residues. A stochastic simulation study was conducted to investigate the effects of sample size and heterogeneity in factors driving residue dissipation of non-systemic pesticides on the unit-to-unit variability among individual apples. The heterogeneity in driving factors was expressed in terms of variability in three dissipation parameters. The variability factor (VF), calculated as the ratio of residues in individual fruit at the 97.5th percentile and the mean residue, was used to represent the unit-to-unit variability. As the rate of dissipation increased, the variability in the corresponding parameter led to larger increases in the variability VF of residues over time. Thus, under field conditions, the relative in pesticide residues is expected to increase with time although the absolute level of residues decreases. The coefficient of variation (CV) was used to describe residue variability in samples of small sizes (5, 10, 20, 40, and 80). When sample size was fewer than 40 fruit, the sample CV increased steeply with decreasing sample size. Measuring residues as concentrations, instead of per fruit, also led to an average 4–8% increase in the CV of residues because of variability in fruit expansion.     

An improved Faster R-CNN model for multi-object tomato maturity detection in complex scenarios

Accurate detection of tomato maturity is significant in automatic tomato picking. Although there are many detection methods, they are often sensitive to occlusion, overlap, uneven illumination, and other complex factors, leading to a limited performance in complex environment scenes. To address this problem, this study designed an improved Faster R-CNN model named MatDet for tomato maturity detection. First, MatDet used ResNet-50 as the backbone to improve the representation ability and robustness of the model. Second, RoIAlign was used to obtain more precise bounding boxes in the feature mapping stage. Third, a Path Aggregation Network (PANet) was introduced to address the difficulty of detecting tomato maturity in complex scenarios. Experimental results showed that the proposed model achieved the best detection results in terms of branch occlusion, fruit overlapping and illumination influence under complex scenarios. Specifically, the mean average precision (mAP) of the proposed algorithm is 96.14%, which is better than that of common object detection models. Through many multi-angle comparative experiments, it was confirmed that our method can overcome complex factors such as branch occlusion, fruit overlap and light influence, and achieve the best detection effect. Meanwhile, this research has certain theoretical and practical significance for the intelligent and precise picking of tomatoes, thereby promoting the directional cultivation of crops such as fruits and vegetables, and providing technical support for the development of ecological monitoring technology and ecological planting.     

果实类蔬菜套袋技术效果评价

田间系统研究了不同袋型的套袋效果及不同果实类蔬菜最佳套袋的时期。室内测定分析了套袋果实蔬菜的营养物含量及农药残留量。结果表明：黄瓜、番茄、西葫芦、茄子套膜袋均有显著的增产效果,分别为16．4％、11．6％、10．1％、13．7％。套纸袋除番茄外亦均有明显增产效果。套膜袋VC含量增加22．0％～152．9％。畸形瓜(果)降低11．5％～32．3％,袋体有效阻隔了化学农药对果实类蔬菜直接接触污染。套膜袋黄瓜、番茄、茄子、西葫芦中农药残留量分别降低83．3％、84．5％、79．7％、85．7％,套纸袋依次降低73．2％、79．9％、70．9％、83．1％。套袋栽培协调了病虫防治与绿色食品蔬菜生产的矛盾。