人工智能与食品加工及安全ppt课件

,IndustrialRevolution,IntelligentManufacturing:MachinetoMachine(M2M)Communication,人工智能与食品加工及安全,4:1,History,WhatisAI?,Thetheoryanddevelopmentofcomputersystemsabletoperformtasksnormallyrequiringhumanintelligence,suchasvisualperception,speechrecognition,decision-making,andtranslationbetweenlanguages.,machinesthatthinklikehumans.machinesthatactlikehumans,machinesthatthinkrationally,machinesthatactrationally.,AIinFoodproductionandsafetyfield,IntelligentmanufactureandproductionSafetyDetection,ArtificialOlfactorySystems(AOS)orelectronicnose,AnAOSisaninstrumentcomposedofasensorarrayandofasuitabledatatreatmentsoftware,abletocharacterizeandrecognizesimpleandcomplexodours,Thistechnologyoperatinginasimilarwaytothehumanolfactivesystem,TheprincipleadvantagesofusingtheAOSare:SimplicityandsyntheticassessmentImmediateresultNosamplepretreatment,Artificialversusnaturalolfactorysystem,AOSoperatingsystem,SAMPLE,SENSORS,FINGERPRINT,SOFTWARE:Patternrecognition,DFARESULT,WorkingprincipleofMOSsensors,O2+(SnO2-X)*O_(SnO2-X)CO+O_(SnO2-X)CO2+(SnO2-X)*,Graphene-BasedNanosensors,CarbonNanotubeSensor,Patternrecognition,Patternrecognitionisaprocessthattakinginrawdataandmakinganactionbasedonthecategoryofthepattern.Whatdoesapatternmeans?“Apatternisessentiallyanarrangement”,N.Wiener“Apatternistheoppositeofachaos”,WatanabeTobesimplified,theinterestingpart,Whatcanwedoafteranalysis?Classification(Supervisedlearning)Clustering(Unsupervisedlearning)Otherapplications,Whyweneedpatternrecognition?,Humanbeingscaneasilyrecognizethingsorobjectsbasedonpastlearningexperiences!Thenhowaboutcomputers?,基于知识学习的模式识别算法主要的特征就是需要样本集和训练模型，通过对样本进行不断地训练，最终达到满足精度的模型。其数学本质是对数据样本中的数据进行分类，找到满足一定的类别特征的分类模型，使不同类数据之间的区别最大，同类数据之间的误差最小，是一种有偏估计模型,Machinelearningalgorithm,APPLES,ORANGES,人脑神经元结构：树突：又称晶枝，是引入输入信号的突起。轴突：是作为输出端的突起，只有一个。突触：树突的全长各部位都可与其它神经元的轴突末梢相互联系，形成“突触”。突触处两神经元并未连通，它只是发生信息传递的结合部。,人工神经元模型：输入：X输出：Y权值：各突触的连接强度用实系数wij表示，是对第j个输入的加权。所有的输入通过某种运算结合在一起，称为净输入，用Neti或Ii表示。,人工神经网络（ANN）是基于对人脑组织结构、活动机制的初步认识而提出的一种，具有自主学习能力的机器学习方法。,ArtificialNeuralNetwork(ANN),神经网络结构可分为前馈神经网络、反馈神经网络，其学习过程分为有监督学习和无监督学习。典型的前馈网络感知器网络、BP网络、RBF网络等。,SupportVectorMachines（SVM）,由于很多过程获取数据比较困难，如果能找到一种用很少样本就能训练模型，就可以弥补获取大量数据带来的困难，而支持向量机（SupportVectorMachines，SVM）就可以达到这一目的。,CompanyLogo,考虑训练集,w是超平面的法向量,即可调权值,正反例的间隔,最优分类超平面等价于求最大间隔,最终问题归结为一个二次最优求解问题,Artificialolfactorysystem,SENSORDATATOTHEPC,SENSORSCHAMBER,MASSFLOWCONTROLLER,INPUT:GCAIRSOURCE,16SAMPLEVIALS,TUBINGCONNECTORS,OUTPUT:EXTERNALAIR,Airflowcontrolsystem,4-WaysValve,16WaysValve(Autosampler),4,2,1,3,SerialconnectionbetweenPCandISENOSEmainunit,PC,Schematicofoperation,Schematicrepresentationofsampleanalysesandclassification,Discriminatebetweenlemonandorangesnoncontaminatedandcontaminated,Milkclassifcation:Thee-nosewasabletodiscriminatemilkfromthreespecies(cow,sheep,andgoat).,Detectionofadulterationofvirginoliveoils,Plotsofthedifferencesbetweendiscriminantscoresfortheclassification(a)andprediction(withfourcancellationgroups)(b)ofoilsamples.()Non-adulteratedvirginoliveoil;()virginoliveoiladulteratedwithsunfloweroil;()virginoliveoiladulteratedwitholivepomaceoil.Filled-insymbolsrepresentthesamplesinpredictionset,MonitoringofFoodSpoilage,Analysisofvolatilecompoundsfrompoultrymeatsamples,freshandafterrefrigeratedstorage,Monitoringstorageshelflifeoftomatousingelectronicnosetechnique,Artificialtastesystem,Forsaltytaste:Na2+ionsdirectlygointothecells(figure1)Forsourtaste:H+ionsarefixedonioniccanalthusliberatingK+ions(figure1).Then,forbothtastes,themembranecellisdepolarizedandanactionpotentialisgenerated,Forsweet,bitterandumami,tasteperceptionisbasedonthekey-lockprinciple:atastebudcanexpressseveralreceptorsandamoleculecanactivatevariousreceptors(figure2).,ApplicationtoFoodsandBeverages,Detectionandmonitoringofnitrate(硝酸盐）,nitrite（亚硝酸盐）andammonium（氨基）levelsinwaters,用于不同鲜味酱油的区分辨别,Computervision,ARTIFICIALINTELLIGENCEMEETSNUTRITION,Logameal,takesaphotobreaksthatphotodownintovisualattributes.Thesefeatures,ofdifferentcomplexity,rangefromsomethingassimpleasalineoracolour,totheshapeofagrainofriceusethesefeaturestomaptofood.machinelearningmodelsoptimisedforfoodTrainingthemodelswithlotsofpicturesoffood,keyvisualfeaturesdefinedifferentfoodsdontpredeterminewhatthefeaturesare,butrather,letourmodelslearnwhichfeaturesareimportantneedtoseeanewfoodanumberoftimesbeforeourmodellearnstorecognizeitAMEALSCOREBASEDONNUTRITIONALVALUENOTCALORIES,Nutrientdensityratherthanjustenergy,areyoueatinghealthy?MealscoreisaformuladevelopedwiththenutritionistsThescoregoesfrom0(poornutrientdensitywhichweequatetounhealthy)to100(nutrientrich,veryhealthy).Foodsthatpackahealthyspreadofnutrientsintofewcalorieswillscorehigh,Usingartificialintelligencetodeveloptastierveganfoods,deepmachinelearningtechniquestotrainitselflearningaboutthemolecularcompositionofdifferentfoods,differenttastesandtextures,andhowhumansperceivetheseusethedatatodevelopnewplant-basedrecipesthattastelikeanimal-basedfoods,NOTCompany,UsesArtificialIntelligencet