长短期记忆神经网络在肉制品中铅含量风险预警的应用

本文基于2015～2018年国家肉制品监督抽检的5万批次检测数据,使用长短期记忆神经网络模型建立肉制品中铅含量的风险预警模型。参照国家食品检验标准并结合专家打分,构建肉制品中铅含量的6个食品安全风险等级;运用Softmax和汉宁窗对风险等级数据进行预处理,使用Tensorflow建立三层长短期记忆神经网络的时间序列风险预警模型,通过500轮模型训练实现对肉制品中铅含量的风险趋势预测。结果表明,长短期记忆神经网络对肉制品中铅含量的预测有较高的准确率,31个省份的平均误差为0.27,同实际检测风险基本匹配;模型稳定重现性较好,运行十次的平均误差为0.27。此模型可以实现对全国不同地域铅含量的趋势预警,为日常监督抽检和食品安全风险预警提供技术支撑。     

食品安全预警数据分析体系构建研究

本文以我国食品安全检测、监测数据为研究对象,初步构建食品安全预警数据分析体系的框架.通过数据筛选、清洗与转换,建立食品安全数据仓库;依据食品安全预警数据分析模型,建立食品安全预警数据分析系统;对系统发布的初预警信息进行人工干预,最终发布食品安全预警信息.     

大数据在食品安全监管风险预警中的应用

食品安全风险预警作为食品安全现代治理体系的重要一环,充分体现了“预防为主、风险管理、全程控制、社会共治”科学原则。文章重点研究了如何利用有效的数据归集和数据分析策略,通过监测、分析、评估等方式来实现食品安全风险预警,以更加科学准确的食品风险数据管理和评估体系支撑政府监管。     

现代生物传感器在食品安全检测中的应用

分析了生物传感器的基本原理、结构以及生物传感器的类型和特点,阐述了现代生物传感器技术在微生物检测、食品药物残留与兽药残留、食品添加剂检测和激素检测中的各种应用,讨论了传感技术在食品安全检测中的重要应用及未来发展方向.     

食品安全检测数据仓库技术的应用与研究

主要利用数据仓库技术,以山东地区进出口食品检测实验室的检测数据为分析对象,初次构建了进出口食品农产品实验室检测数据仓库系统,为食品安全风险分析提供了准确科学的数据支持,为实现数据的进一步挖掘和系统平台的开发做好了准备工作。     

微生物检测技术在食品安全检验中的应用

食品安全问题会对大众身体健康造成威胁,因此,加强食品安全检测,从根本上提升食品安全质量至关重要。调查显示,导致食品安全问题产生的最大原因为微生物,所谓微生物,即不易用肉眼看到的微小生物,其中某些致病菌已严重影响到人们的身体健康。由于微生物因素产生的食品安全问题,进而导致食用者出现腹泻现象的病例,全球每年就高达10.5亿人,其中不乏一些对儿童生命安全造成威胁的病例,因此加强食品微生物检测尤为重要。这样可以有效地从源头上提升食品卫生质量,为食用者的健康提供强有力的保障。     

2004年1月食品安全预警报告

二-注:“三无产品”指无注明生产单位、地址;无生产日期、保质期;无卫生许可证、企业标准号等产品澎补巍漆林娜钟琳卿价衬一膨瓣蜘娜娜象豁嘛妙嘛乒_2004年1月食品安全预警报告     

风险评估在企业食品安全管理中的应用

本文研究了如何将风险评估(RA,Risk Assessment)方法在食品企业中加以应用,确保食品安全。特别是结合广泛运用的食品安全管理体系蜒危害分析和关键控制点体系(HACCP,Hazard Analysis and Critical Control Point),探讨了如何应用风险评估要素来消除或减少食品安全风险。     

北京“十三五”时期食品安全风险评估与预警应急科技支撑工程

北京的食品主要由外埠供应,其食品安全环境日趋复杂,未知风险、人为风险和衍生风险较大。风险产生的主要原因包括全国不同地区产业基础参差不齐、大宗农副产品供应链存在问题、未全面采用与国际接轨的风险评估和控制技术,以及新技术、新工艺、新资源带来的食品安全新问题。"十三五"时期,建议北京市不断完善首都居民营养物质摄入和危害物质膳食暴露数据库,构建食品安全高风险物质毒理学评估技术平台、食源性致病菌和病因性食品溯源平台和食品安全预警应急体系。     

食品安全标准与食品检测标准在应用中的问题浅析

食品安全标准与食品检测标准是衡量食品质量的重要准则,只有保证其合理性、可行性,才能杜绝不安全食品的流出,净化食品市场.本文就对食品安全标准与食品检测标准应用中的问题及解决策略展开探讨说明,以供参考.     

人工神经网络在食品生物工程中的应用

简述了人工神经网络的基本原理与使用方法,并介绍了其在食品微生物发酵、食品酶工程、食品生物活性物质等食品生物工程领域的研究进展,旨在为人工神经网络在食品工业中的更广泛应用提供一定的理论基础依据。     

Applications of artificial neural networks (ANNs) in food science

Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.     

基于案例推理的食品安全突发事件风险预警系统探索

食品安全突发事件爆发时间短、影响大,应用案例推理方法建立食品安全突发事件风险预警系统,规范化描述食品安全突发事件并与风险预警系统中案例库的案例解比较和评估,及时发出预警警告,有效降低食品安全突发事件发生频率和影响范围;并对建立食品安全突发事件案例推理预警系统提出了建议和措施。     

Prediction of texture characteristics from extrusion food surface images using a computer vision system and artificial neural networks

Surface images and the texture characteristics of 17 samples and the 25 different parts within one sample were detected using a computer vision system and texture profile analysis in extruded food. According to the linear fitting model, the hardness and gumminess score can be reflected directly by the a^* and Intensity based on correlation coefficient of 0.9558, 0.9741 and 0.9429, 0.9619, respectively. The springiness could be reflected from color values through calculating from hardness and gumminess scores, indirectly. Neither of cohesiveness and chewiness presented relationship with two different color spaces. A desirable and accurate two hidden layers of back-propagation artificial neural network was trained for simulating and predicting the hardness and gumminess scores from a^* and Intensity based on the data in 17 samples, respectively. The simulation processing in ANN showed higher correlation coefficient of 0.9671 and 0.9856 than linear fitting model.     

RFID在食品安全追踪系统中的应用

将RFID用于食品安全追踪系统,有利于彻底实现“源头”食品追踪和食品安全科学化、透明化管理。阐明了RFID的工作原理及RFID在食品安全追踪中的应用方法和应用案例,综述了RFID在食品安全追踪中应注意和解决的问题及发展方向。     

Application of artificial neural networks in the geographical identification of coffee samples

The recognition of the characteristics of coffee associated with a given agricultural system and aimed at adding value and attending the consumers’ demands stimulates the production of types of coffee properly described. The objective of this study was to explore and to explain the physicochemical characteristics and sensory attributes of the coffee grown in Parana State (Southern Brazil) based on an integrated approach of the terrior and the application of artificial neural network. Physicochemical variables of green coffee beans and roasted coffee beans were determined, as well as sensory attributes of the beverage. One hundred and seventy-two coffee samples were analyzed for moisture, proteins, chlorogenic acids, tannins, total acidity, total lipids, caffeine, total and reducing sugars and minerals. These properties were tabulated and presented to artificial neural network multilayer perceptron to be identified as the region and the city of planting. The artificial neural network classified correctly and tested 100% of the samples grown by region. For the database containing information by city, the automatic mode of the software Statistica 9.0 was used. The neural network showed 99% accuracy in training and 100% accuracy in the stage of testing and validation.     

基于计算机视觉和人工神经网络预测挤压食品的质构特征

应用计算机视觉系统分别提取不同配方的挤压食品和同一样品不同部位的颜色值(HSI和L*、a*、b*),同时用质构分析仪测定样品质构特征。借助线性拟合模型通过样品的颜色对挤压食品的质构特征进行相关性分析,并利用BP神经网络模型通过颜色预测挤压食品的质构。线性拟合模型显示,硬度和胶粘度分别与a*值和对比度之间高度相关。两组实验中硬度与a*值之间的R2分别为0.9558、0.9429;胶粘度与对比度之间的R2分别为0.9741、0.9619。弹性与a*值和对比度之间具有一定的相关性,两组实验中弹性与a*值和对比度之间的R2分别为0.8675和0.8320。利用实验所得硬度、胶粘度、a*值以及对比度数据优化含有2个隐层的BP神经网络,得到两组实验对应最优网络模型结构,即每层所含神经元的数量分别为20、20,均方根(RMS,%)为4.25;20、40,均方根(RMS,%)为3.85。利用最优神经网络运用a*值和对比度对两组实验中的硬度和胶粘度进行模拟,得到的相关系数高于线性拟合模型拟合结果,两组实验中硬度与a*值之间的R2分别为0.9671、0.9770;胶粘度与对比度之间的R2分别为0.9766和0.9856。采用最优网络模型用颜色信息对挤压食品硬度和胶粘度的预测和验证结果表明,利用计算机视觉系统所提取的颜色值可以通过人工神经网络快速准确预测挤压食品的质构特征。      

HACCP在食品安全与质量体系建设中的应用

HACCP是国际上公认和接受的确保食品安全的一种预防性管理控制体系。现就HACCP的基本程序,传统的食品安全监督面临的挑战,建立HACCP的必要性,HACCP在食品安全与质量体系建设中的重要作用及目前存在的主要问题等作一综述,以促进HACCP管理体系在食品行业中的广泛应用。     

Microbial growth modelling with artificial neural networks

There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.     

Spectroscopic quantification of bacteria using artificial neural networks

Fourier transform-infrared spectroscopy, in conjunction with artificial neural networks, has been used for identification and classification of selected foodborne pathogens. Five bacterial species (Enterococcus faecium, Salmonella Enteritidis, Bacillus cereus, Yersinia enterocolitica, Shigella boydii) and five Escherichia coli strains (O103, O55, O121, O30, O26) suspended in phosphate-buffered saline were enumerated to provide seven different concentrations ranging from 10(9) to 10(3) CFU/ ml. The trained artificial neural networks were then validated with an independent subset of samples and compared with the traditional plate count method. It was found that the concentration-based classification of the species was 100% correct and the strain-based classification was 90 to 100% accurate.