登录
首页 » Others » 基于高光谱成像的蓝莓内部品质检测 特征波长选择方法研究

基于高光谱成像的蓝莓内部品质检测 特征波长选择方法研究

于 2020-12-07 发布
0 290
下载积分: 1 下载次数: 4

代码说明:

在特征波长选取方面有一些创新,可以作为参考。在特征波长选取方面有一些创新,可以作为参考。(基于高光谱成像的蓝莓内部品质检测特征波长选择方法研究古文君1 ,田有文 1* ,张芳1 ,赖兴涛 1 ,何宽1 ,姚萍1 ,刘博林 2)586-482016620010~15mm0.8~2.3g。fone3:(InSpector V10E, Spectral InFinland)1392pix×1040pixCCDL CCD2(IGV-B141OM, IMPERX Incorporated, USA), 150W1. CCD Camera; 2.Spectrometer; 3.Shot; 4. Light source; 5. Samples(3900 Illuminatior, Illumination Tech6.Translationplatform7.Lightsourcecontroller;8.computernologies inc.,USA)、(IRCP0076-19. Translation platform controllerCOM,)、(120cm×50cmx(DELL VoStro 5560D-1528Figure 1 Schematic diagram of hyperspectral imagingcmsystem400~1000nm,4722.8nmRRGY-4(10mm)(DBR45(successive projections algorithm, SPA(stepwise multiple linear regression, SMLR)(SPA)(SMLR)SPASPASMLRSPA-SPA、SMLR_SMLR、SPA- SMLRSMLR-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5871.6BP(error back propagation)BP17(correlation coeffiient of calibration, Re)(root mean square error of calibration set, RMSEC)correlation coeffiient of pre-diction, Rp)(root mean square error of prediction set, RMSEP)ENVI 4.8(Research System Inc, ), MATLAB 2014a(The Math Works Inc)、TheUnscrambler9.7、 Excel2010(Ⅵ icrosoftdgle banddWcvef.BP models for soluble solidsThe selected characteristic wavelengthCurve of relative reflectanceExtract the region of interescontent and firmness prediction2figure 2 Flow chart of data processing280mm,68ms,28mm·s-。99%202.2600nm600nm2b2c)21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5884823(2f)BPSavitzky-Golasavitzky -golayTable 1 The effect of different spectra preprocessingCalibration setPredictioSpectrum typeRMSECRMSEPOriginal spcctrum0.933/0.9230.3510.4040.9200.9100.508/0.319MSCThe spectrum after MSC processing0.940/0.9450.56lO.3120.9190.9320.516/0.282SNThe spectrum after SNV processin0.93709340.60210.24309220.9010.6320.462Savitzky-golayThe spectrum after Savitzky-Golay processing 0.955/0.9550.3240.2410.951/0.9490.400/0.2782.5SPA-SPA SMLRSMLR SPA-SMLR SMLR-SPASPA-SPASPASavitzky-GolaySPATable 2 The results of multi-stage characteristic wavelength selection methodnmCharacteristie wavelength selection methodSPA-SPA452,455,470,482,490,785,893,912,921,942,950455,470,482,785,893.912SMLR-SMLR457,508,516,534,543,51,556,568,712,720.774,778508,534,543,712,720,774SPA-SMLR452,455,470,482,490,785,893,912,921,942,950452,470,482,490,893,912SMLR-SPA457,508,516,534,543,551,556,568,712,720,774,78534,7202.6Savilzky-gola(FS)392SPA-SPASMLR-SMLRSMLR-SMLRSMLR-SPABPBP0.001500021994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct589BPBPSPA-SPARp RMseP0.9520.391°Brix,RpRMSEP0.9530.234BrixTable 3 Detection results of soluble solid content and firmness of blueberry based on different multi-stagecharacteristic wavelength selection methodsCalibration setPrediction setCharacteristic selection method Wavelength numberRMSECRMSEP3929550.9550.324/0.2410.9510.9490.400/0.278SPA-SPA0.9590.9560.3180.1530.9520.9530.391/0.234SMLR-SMLR0.9560.9340.414/0.243912109020.559/0.349SPA SMLR0.828/0.8581.3670.58582208091.440/0.719SMLR- SPA20.958/0.9360.402/0.3359320.9280.435/0,4041387nm1229nm91.5%BPRRMSEP0.904215.163lBP3Rv0.84V0.94Rv0.83,SEV0.63。400-1000nmSavitzky-GolayBPSPA-SPASPA-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct59048[1 KADER F,ROVEL. B Fractionation and identification of the phenolic compounds of highbush blueberries(Vaccinium corymbosumLUJ].Food Chemistry, 1996,55(1): 35-40「J,2012,33(1):340-342,2017,38(2):301-305.[4 MENDOZA F, LU R, ARIANA D,et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction ofple [ruil firmness and soluble solids conlenl[J] Poslharvesl Biology and Technology, 2011, 62(2: 149-160[5 SUN M J, ZHANG D, LIU L,et al. How to predict the sugariness and hardness of melons a near-infrared [J]. Food Chemistry,2017,218(3:413-42116 SIEDLISKA A, BARANOWSKI P, MAZUREK W, ct al. Classification models of bruise and cultivar detection on the basis of hy-perspectral imaging data[J]. Computers and Electronics in Agriculture, 2014, 106: 66-74[7 LIU D, SUN D W, ZENG X N, el al. Recenl aDvances in wavelength seleclion lechniques for hyperspectral image processing inthe food industry[J]. Food Bioprocess Technol, 2014, 7: 307-323[8 ZHANG C, GUO C T, LIU F,et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector ma-chine[j] Journal of Food Engincering, 2016, 179: 11-18[9J,2016,47(5:634-6402009,29(:1611-1615201536(12)171-17612]J,2012,32(11:3093309[13] LI B C, HOU B L, ZHANG D W,et al. Pears characteristics (soluble solids content and firmness prediction, varieties) testingInethods based on visible-near infrared hyperspecTral imaging[J]. OpLik, 2016, 127: 2624-2630[14] FAN S X, ZHANG B H,LI J B, et al. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data[J. Postharvest Biology and Technology, 2016, 121: 51-61[15 RAJKUMAR P, WANG N,EIMASRY G, et al.Studies on banana fruit quality and maturity stages using hyperspectral imaging[ JIJournal of Food Engineering 2012, 108: 194-200,2015,36(16):10172015,35(8:2297-2302[18]WANG N,2007,23(2:151-155.「192008,39(5):91-9320」201536(10:70-74.[21] WU D, SUN D WAdvanced applications of hyperspectral imaging technology for food quality and safety analysis and assess-ment a review part T[J]. Innovative Food Science and Emerging Technologies, 2013, 19(4): 1-14J2014,35(8:57-61BP,2012.124」13,44(2):142-146.25],201523(6:1530-1537M011:41-48.[27,2013,24(10:1972-19762010,30(10):2729-2733?1994-2018ChinaAcadcmicJournaleLcctronicPublishingHousc.Allrightsreservedhttp://www.cnki.nct

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • MATLAB迭代法计算信道容量
    参考北京邮电大学出版的信息论基础教程编写的迭代法计算信道容量,MATLAB语言。
    2020-11-27下载
    积分:1
  • 养老院管理系统.rar
    养老院管理系统代码,使用的mysql数据库:https://download.csdn.net/download/weixin_40490238/11484946
    2020-11-27下载
    积分:1
  • 极限学习机与偏最小二乘法
    由于神经网络具有拟合非线性的能力,所以可以用神经网络来处理内部模型的非线性特性,因此这种内部模型采用神经网络的非线性PLS方法得到了广泛的应用。传统的前馈神经网络在训练中采用梯度学习算法,网络中的参数需要迭代更新,不仅训练时间长,而且容易导致局部极小和过度训练等问题,另外其多隐层的结构也导致了样本训练速度慢,训练误差大"此外,Bartlett提出对于已达到最小训练误差的前馈神经网络,权值越小泛化特性越好,而传统的梯度学习算法仅仅考虑训练误差最小,忽视了权值大小对网络的影响,这些问题都将影响到模型的泛化特性。
    2020-12-09下载
    积分:1
  • International Reference Ionosphere - IRI (2019) 国际电离层模型 直到2019年数据
    International Reference Ionosphere - IRI (2016)的matlab代码~~亲测能用~非常方便输入参数,还可以画图
    2021-05-06下载
    积分:1
  • qt画图
    可以实现基本涂鸦功能并且可以绘制矩形,椭圆,直线等基本图形并进行缩放,移动和填充
    2020-12-07下载
    积分:1
  • GPS信号产生及捕获
    该程序为Matlab编写的GPS信号产生和捕获程序,最终得到导航数据,可根据不同的卫星号产生不同的C/A码,
    2021-05-06下载
    积分:1
  • 仿真板球系统,PID和神經網絡的比較
    把球坐标输入PID/神經網絡 控制器作计算,及后输出讯号至servo 模塊。 servo 模塊輸出摩打轉動角度(等於平台x y 軸的轉動角度平台,由以改變球的坐標,使球回至原點或作軌跡移動。最後輸出各種數據圖作比較用,以及實時在虛擬板球系統顯示破的位置變化。最終系统simulink模塊可參考圖1 及文獻[DESIGN OF VIRTUAL MODELS OF MECHATRONICS SYSTEMS WITH SIMULINK 3D ANIMATION TOOLBOX]。
    2020-12-11下载
    积分:1
  • 斯坦福大学吴恩达机器学习课 完整学习笔记+原始版讲义 全是高清版的ppt
    斯坦福大学吴恩达机器学习课程 完整学习笔记 原始版讲义 全是高清版的ppt该课件为中科院一位仁兄在学习斯坦福大学吴恩达机器学习课程时候所做的学习笔记,非常好,吴老师上课略过的一些内容笔记都详细给出,并且还做了适当补充。强烈推荐。
    2020-05-26下载
    积分:1
  • MATLAB优化算法案例分析与应用(基础篇)
    MATLAB优化算法案例分析与应用(基础篇),带章节目录,排版整洁,资源来源于互联网,仅供参考,请支持正版
    2020-12-08下载
    积分:1
  • 微机原理课设计——电子时钟
    该课程设计的内容为电子时钟的设计与实现,利用定时器从0开始进行计时,将计时的结果显示在数码管上。每隔1秒,秒钟计时一次,到60秒,分钟加1,到60分小时加1。8254芯片的计时从0秒到9秒,到9秒后又从0秒重新开始计时,同时将0秒~9秒的数字变动信息通过8255送数码管显示。 设计要求1、总体内容:设计一电子时钟,能在数码管上显示时间并计时。2、接口设计:根据题目和所用的接口电路芯片设计出完整的接口电路,并在实验系统上完成电路的连接和调试通过.3、程序设计:要求画出程序框图,设计出全部程序并给出程序设计说明和程序注释。
    2020-12-10下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载