鲜食葡萄冷链运输监测方法研究
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摘要
鲜食葡萄属浆果类,具有易腐性,在运输过程中容易出现腐烂、褐变、干枝、掉粒等问题,造成经济损失。冷链运输是解决此问题的有效途径。冷链运输过程中,冷藏车厢环境状态决定鲜食葡萄贮藏期和货架期的长短,如何准确、实时、低成本的监测冷藏车厢环境参数是保障鲜食葡萄冷链运输顺利进行的关键。因此,开展鲜食葡萄冷链运输过程的智能化动态监测方法研究,确保冷链运输过程中监测参数的有效采集、传输与处理,对推动我国鲜食葡萄冷链运输业的高速发展有着重要意义。
基于此,本文以红地球葡萄为研究对象,以无线传感器网络技术、多传感器数据融合技术、灰色理论、神经网络理论等为数据获取和处理的手段,结合保鲜技术、温度场分析方法等对鲜食葡萄冷链运输监测方法进行研究,主要结论如下：
1.温度、相对湿度和二氧化硫体积浓度是影响鲜食葡萄运输品质的主要因素,是冷链运输需要监测的参数。冷藏车厢温度的空间差异性、货物的装载方式、开关门时间长短等是引起这些参数发生变化的主要原因。
2.提出了多口标决策模糊物元分析法,实现了冷藏车厢内传感器布局的点位优化。结果表明,该方法能够在保证监测结果准确的前提下,传感器数量从27个减少到7个,降低了冷链运输成本。采用统计分析方法和温度场分析法,验证了优化后传感器布局的合理性。优化后传感器监测值具有95%以上的置信水平,优化前后温度场分布图相似率达到90%以上,达到冷链运输中既节约成本又准确监测的双重要求。
3.提出了时间-空间数据融合算法,对冷链运输监测值进行融合估计,实现了冷藏车厢环境状态的准确反映。结果表明,时间-空间数据融合算法可以利用修正方差自适应的调节各组传感器的修正因子,消弱较大误差对监测结果的影响,融合精度要优于分批估计法和算数平均法,处理后的结果能够更准确的反映冷藏车厢内的环境状态。
4.提出MGM-RBF神经网络预测模型,能够对冷藏车厢环境状态进行准确预测。结果表明,均方根相对误差为0.60%,平均相对误差为0.44%,明显优于单一的MGM预测和RBF神经网络预测。基于统计过程控制理论对冷藏车厢温度进行判异,定义了三种预警模式,对比固定阈值法能够明显减少误警或者频繁报警现象,提高了报警的准确度,实现了分类、分级、分层次报警的目的。
5.从关键参数的辨识、参数的获取、参数的估计以及状态预报等方面对鲜食葡萄冷链运输过程展开研究,形成了一套高效实用的冷链运输动态监测方法。
The phenomenon of decay, browning and decay is easy to appear in the table grape transportation, which causes no little financial loss. Cold-chain transportation is an effective measure to prevent those problems. At present, the cold-chain transportation construction is still in initial stage in our country. It is urgent to conduct a research on the intelligent monitoring technology of cold-chain transportation to complete data collection and processing. The way guarantees the quality of the table grapes and promotes the rapid development of table grapes cold-chain transportation.
In the paper, red globe grape was chosen as the research object. Wireless sensor network, multi-sensor data fusion technology and neural network theory were used for data acquisition and processing method combined with the preservation technology to guarantee the security of table grapes cold-chain transportation. By means of investigation research, emulation and simulation, some methods were put forward. Main conclusions were as follows:
(1) Temperature, relative humidity, and volume concentration of sulfur dioxide are the main factors influencing the quality of table grapes transportation, and the three factors were chosen as the the main monitoring parameters in the cold-chain transportation. Spatial difference of temperature and operation nonstandard were the main reasons for those factors changes.
(2)Multi-objective fuzzy matter element analysis method was put forward to optimize the sensor quantity.27sensors was reduced to7in the refrigerator car, which reduced the cold-chain transport costs. The statistical analysis method and the temperature field analysis were applied to validate the rationality of the optimization algorithm. Results possessed more than95%of confidence level.
(3) A new data fusion method based on time-space data fusion theory was investigated to multi-sensor data processing.The mentioned algorithm could gradually reduce the influence of sensors with poor precision by means of introducing correction factor into the weighting coefficient, which taked advantage of the variance of the single-sensor fusion data and the final fusion data to adaptively adjust the weights of each sensor, and by means of multi-step fusion, gradually weakened the influence of some sensors with larger errors on fusion accuracy. Experimental results showed that the proposed algorithm outperforms traditional method.
(4)MGM-RBF neural network predictive model was put forward to predictive the refrigerator compartment temperature. Results showed that mean square relative error was0.60%and average relative error was0.44%, that were superior to single MGM arithmetic and RBF neural network prediction arithmetic. Based on the theory of statistical process control, three early-warning modes were defined. Compared with the fixed threshold method, the proposed algorithm had a less false-alarm rate.
(5)Monitoring research involved four dimensions:key parameters identification, parameters acquisition, parameters estimation and parameters forecast. Those formed an efficient and practical method for cold-chain transportation dynamic monitoring.
基于此,本文以红地球葡萄为研究对象,以无线传感器网络技术、多传感器数据融合技术、灰色理论、神经网络理论等为数据获取和处理的手段,结合保鲜技术、温度场分析方法等对鲜食葡萄冷链运输监测方法进行研究,主要结论如下：
1.温度、相对湿度和二氧化硫体积浓度是影响鲜食葡萄运输品质的主要因素,是冷链运输需要监测的参数。冷藏车厢温度的空间差异性、货物的装载方式、开关门时间长短等是引起这些参数发生变化的主要原因。
2.提出了多口标决策模糊物元分析法,实现了冷藏车厢内传感器布局的点位优化。结果表明,该方法能够在保证监测结果准确的前提下,传感器数量从27个减少到7个,降低了冷链运输成本。采用统计分析方法和温度场分析法,验证了优化后传感器布局的合理性。优化后传感器监测值具有95%以上的置信水平,优化前后温度场分布图相似率达到90%以上,达到冷链运输中既节约成本又准确监测的双重要求。
3.提出了时间-空间数据融合算法,对冷链运输监测值进行融合估计,实现了冷藏车厢环境状态的准确反映。结果表明,时间-空间数据融合算法可以利用修正方差自适应的调节各组传感器的修正因子,消弱较大误差对监测结果的影响,融合精度要优于分批估计法和算数平均法,处理后的结果能够更准确的反映冷藏车厢内的环境状态。
4.提出MGM-RBF神经网络预测模型,能够对冷藏车厢环境状态进行准确预测。结果表明,均方根相对误差为0.60%,平均相对误差为0.44%,明显优于单一的MGM预测和RBF神经网络预测。基于统计过程控制理论对冷藏车厢温度进行判异,定义了三种预警模式,对比固定阈值法能够明显减少误警或者频繁报警现象,提高了报警的准确度,实现了分类、分级、分层次报警的目的。
5.从关键参数的辨识、参数的获取、参数的估计以及状态预报等方面对鲜食葡萄冷链运输过程展开研究,形成了一套高效实用的冷链运输动态监测方法。
The phenomenon of decay, browning and decay is easy to appear in the table grape transportation, which causes no little financial loss. Cold-chain transportation is an effective measure to prevent those problems. At present, the cold-chain transportation construction is still in initial stage in our country. It is urgent to conduct a research on the intelligent monitoring technology of cold-chain transportation to complete data collection and processing. The way guarantees the quality of the table grapes and promotes the rapid development of table grapes cold-chain transportation.
In the paper, red globe grape was chosen as the research object. Wireless sensor network, multi-sensor data fusion technology and neural network theory were used for data acquisition and processing method combined with the preservation technology to guarantee the security of table grapes cold-chain transportation. By means of investigation research, emulation and simulation, some methods were put forward. Main conclusions were as follows:
(1) Temperature, relative humidity, and volume concentration of sulfur dioxide are the main factors influencing the quality of table grapes transportation, and the three factors were chosen as the the main monitoring parameters in the cold-chain transportation. Spatial difference of temperature and operation nonstandard were the main reasons for those factors changes.
(2)Multi-objective fuzzy matter element analysis method was put forward to optimize the sensor quantity.27sensors was reduced to7in the refrigerator car, which reduced the cold-chain transport costs. The statistical analysis method and the temperature field analysis were applied to validate the rationality of the optimization algorithm. Results possessed more than95%of confidence level.
(3) A new data fusion method based on time-space data fusion theory was investigated to multi-sensor data processing.The mentioned algorithm could gradually reduce the influence of sensors with poor precision by means of introducing correction factor into the weighting coefficient, which taked advantage of the variance of the single-sensor fusion data and the final fusion data to adaptively adjust the weights of each sensor, and by means of multi-step fusion, gradually weakened the influence of some sensors with larger errors on fusion accuracy. Experimental results showed that the proposed algorithm outperforms traditional method.
(4)MGM-RBF neural network predictive model was put forward to predictive the refrigerator compartment temperature. Results showed that mean square relative error was0.60%and average relative error was0.44%, that were superior to single MGM arithmetic and RBF neural network prediction arithmetic. Based on the theory of statistical process control, three early-warning modes were defined. Compared with the fixed threshold method, the proposed algorithm had a less false-alarm rate.
(5)Monitoring research involved four dimensions:key parameters identification, parameters acquisition, parameters estimation and parameters forecast. Those formed an efficient and practical method for cold-chain transportation dynamic monitoring.
引文
[1]Abad E, Palacio F, Nuin M, et al. RFID smart tag for traceability and cold chain monitoring of foods: demonstration in an intercontinental fresh fish logistic chain. Journal of Food Engineering,2009,93(4): 394-399
[2]Arici T, Altunbasak Y. Adaptive sensing for environment monitoring using wireless sensor networks. IEEE WCNC,2004,5(1):2350-2355
[3]Baranyi J, Roberts T A. A dynamic approach to predicting microbial growth in food.International journal of food microbiology,1994,23:277-294
[4]Baucer B A, Knorr D. The impact of pressure,temperature and treatment time on starches: pressure-induced starch gelatinization as pressure time temperature indicator for high hydrostatic pressure processing. Journal of food engineering,2005,68(3):329-334
[5]Becker M, Yuan S, Jedermann R, et al. Challenges of aplying wireless sensor networks in logistics. In:CEWIT 2009. Wireless and IT driving Healthcare, Energy and Infrastructure Transformation,2009
[6]Bobelyn E, Hertog M L A T M, Nicolai B M. Applicability of an enzymatic time temperature integrator as a quality indicator for mushrooms in the distributrion chain. Postharvest biology and technology,2006,42(1):104-114
[7]Carol C. Safety Stars with Temperature Control. Food Logistics,2007,11:16-20
[8]Chen J, Pang Z, Zhang Z, et al. A novel acceleration data compression scheme for wireless sensor network application in fresh food tracking system. ICEMI'09,2009
[9]Christian C, Thomas B, Mandenius C F. Integration of distributed multi-analyzer monitoring and control in bioprogressing based on a real-time expert system. Journal of Biotechnology,2003,103: 237-248
[10]Chakrabarty K, Iyengar S S, Qi H, et al. Grid coverage for surveillance and target location in distributed sensor networks. IEEE transactions on computers,2002,51:1448-1453
[11]Chen P Y, Chen W T. A group tourguide system with RFIDs and wireless sensor networks. IPSN, 2007:561-562
[12]Chunbae L, Wen W, Sangsik Y, et al. Development of SAW-based multi-gas sensor for simultaneous detection of CO2 and NO2. Sensors and Actuators B:Chemical,2011,154(1):6-16
[13]Costa C, Lucera A, Conte A, et al. Effects of passive and active modified atmosphere packaging conditions on ready-to-eat table grape. Journal of Food Engineering,2011,102:115-121
[14]Deligiannakis A, Kotidis Y, RoussopoulosN. Compressing Historical Informafion in Sensor Networks. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, USA,2004:527-538
[15]Deng Y, Wu Y, Li Y, et al. Studies of postharvest berry abscission of 'Kyoho' table grapes during cold storage and high oxygen atmospheres. Postharvest Biology and Technology,2007,43:95-101
[16]Elisabeth I Z, Zsolt K, Fred V B, et al. A survey of applications and requirements of unique identification systems and RFID techniques. Computers in Industry,2011,62(3):227-252
[17]Ellouze M, Augustin J C. Applicability of biological time temperature integrators as quality and safety indicators for meat products. International Journal of Food Microbiology,2010,138:119-129
[18]Eunji K, Dong Y C, Hyun C K, et al. Calibrations between the variables of microbial TTI response and ground pork qualities. Meat Science,2013,95 (2):362-367
[19]Fabio Z, Fabio A, Letizia F, et al. Sensors and actuators A. Physical,2010,158:169-175 [20]Foster A M, Swain M J, Barrett R. et al. Experimentalverification of analytical and CFD predictions ofinfiltration through cold store entrances. International Journal of Refrigeration.2003,26(8):918-925
[21]Ganesh M. Introduction to fuzzy sets and fuzzy logic. Prentice-hall,2008
[22]Giannakourou M C, Koutsoumanis K, Nychas G J E, et al. Field cvalution of the application of time temperature integrators for monitoring fish quality in the chill chain.International. Journal of Food Microbiology,2005,102(3):323-336
[23]Giannakourou M C, Taoukis P S. Kinetic modeling of vitamin C loss in frozen green vegetables under variable storage conditions. Food Chemisty,2003,83(1):33-41
[24]Hallie F, Christopher W. Cold chain concerns.Pharmaceutical Technology,2005,4:44
[25]Hai L, Miodrag B, Amiya N, et al. Taxonomy and challenges of the integration of RFID and wireless sensor net works. IEEE Network,2008,22:26-35
[26]James S J, James C, Evans J A. Modelling of food transportation systems-a review. Int. J. Refrig, 2006,29:947-957
[27]James S J, James C. The food cold-chain and climate change. Food Research International,2010, 43(7):1955-1956
[28]Jarimopas B, Singh S P. Measurement and analysis of truck transport vibration levels and damage to packaged tangerines during transition. Package Technology & Science,2005,18:179-188
[29]Jedermann R, Lang W. Semi-passive RFID and beyond:steps towards automated quality tracing in the food chain, Int. J. Radio Frequency Identification Technology and Applications,2007,1(3): 247-259
[30]Jedermann R, Ruiz G L, Lang W. Spatial temperature profiling by semi passive RFID loggers for perishable food transportation. Computers and Electronics in Agriculture.2009,65(2):145-154
[31]Jose M L, Sara T, Roberto B, et al. Changes in physico-chemical, microbiological, textural and sensory attributes during ripening of dry-cured foal salchichon. Meat Science,2012,90(1):194-198
[32]Kammer D C, Tinker M L. Optimal placement of triaxial accelerometers for modal vibrate tests. Mechanical Systems and Signal Processing,2004,18:29-41
[33]Kacimi R, Dhaou R, Beylot A. Using energy-efficient wireless sensor network for cold chain monitoring. Consumer Communications and Networking Conference,2009:1-5
[34]Kentaro M, Ynzuru M, Shokaku K, et al. Phaseseparable aqueous amide solutions as a thermal history indicator. Biosci Biotechnol Biochem,2008,72(12):3314-3317
[35]Kim M J, Jung S W, Park H R, et al. Selection of an optimum pH-indicator for developing lactic acid bacteria-based time-temperature integrators (TTI). Journal of Food Engineering,2012,113(3): 471-478
[36]Lin J P. Pre-warning system of coal mine based on ICA and kalman filter. Sixth International Conf. on Natural Computation,2010
[37]Li Y, Kramer M R, Beulens A J M, et al. A framework for early warning and proactive control systems in food chain networks. Computers in Industry,2010,61:852-862
[38]Lester E J. Freezing effects on food quality. New York:Marcel Dekker Inc,1995
[39]Luo H, Luo J, Liu Y, et al. Adaptive data fusion for energy efficient routing in wireless sensor networks, IEEE Trans. on Comp,2003,23(41):43-49
[40]Mai N T T, Gudjansdottir M, Lauzon H L, et al. Continuous quality and shelf life monitoring of retail-packed fresh cod loins in comparison with conventional methods. Food Control,2011,22(6): 1000-1007
[41]Ma X. Application of data fusion theory in coal gas fire prediction system.2008 International Co nference on Intelligent Computation Technology and Automation.2008
[42]Marija B, Ludvik B, Robert V. Stability of perishable goods in cold logistic chains. International Journal of Production Economics,2005,93:345-356
[43]Mertens K, Vaeson I, Loffel J. Data-based design of an intelligent control chart for the daily monitoring of the average egg weight. Computers and Electronics in Agricuture,2008,61:222-232
[44]Mixhael R. State-of-the-Art XML support in RDBMS:Mircosoft SQL server's XML fratures. IEEE Transactions on Knowledge and Data Engineering,2006
[45]Moureh J. Airflow patterns within a refrigerated truckloaded with slotted filled boxes. USA, CRC Press, Boca Raton,2009
[46]Marija B, Ludvik B, Robert V. Stability of perishable goods in cold logistic chains. International Journal of Production Economics,2005,93:345-356
[47]Nga M, Audorff H, Reichstein W, et al. Performance of a photochromic time-temperature indicator under simulated fresh fish supply chain conditions. International Journal of Food Science and Technology,2011,46:297-304
[48]Ning W, Zhang N, Wang M. Wireless sensors in agriculture and food industry-recent development and future perspective. Computers and Electronics in Agriculture,2006,50(1):1-14
[49]Nelson C, Froes P. Monitoring temperatures in the vaccine cold chain in Bolivia. Vaccine,2007, 25:433-437
[50]Nojeong H, Varshney P K. Energy-efficient deployment of intelligent mobile sensor networks. IEEE Transactions on Systems, Man and Cybernetics:Part A,2005,35(1):78-92
[51]Nopwinyuwong A, Trevanish S, Suppakul P. Development of a novel colorimetric indicator label for monitoring freshness of intermediate-moisture dessert spoilage. Talanta,2010,81(3):1126-1132
[52]Ogasawara A, Yamasaki K. A temperature-managed traceability system using RFID tags with embedded temperature sensors. NEC Technical Journal,2006,1(2):82-86
[53]Olivier S, Marc B, Anne M. Model reduction via principal component truncation truncation for the optimal design of atmospheric monitoring networks. Atmospheric Environment,2009,43(32): 4940-4950
[54]Oprea A, Courbat J, et al. Temperature, humidity and gas sensors integrated on plastic foil for low power applications. Sensors and Actuators B:Chemical,2009:227-232
[55]Pan Y, Lu X. Energy-efficient lifetime maximization and sleeping scheduling supporting data fusion and OoS in multi-sensor net. Signal Processing,2007,87(12):2949-2964
[56]Pattern S, Krishnamachari B, Govindan R. The impact of spatial correlation on routing with compression in wireless sensor networks. ACM Transactions on Sensor Networks.2008,4(4):24-35
[57]Pierce F J, Elliott T V. Regionaland on-farm wireless sensor networks for agricultural systems in Eastern Washington. Computersand Electronicsin Agriculture,2008,61(1):32-43
[58]Philippe R, Marie C, Veronique N, et al. Time-temperature profiles of chilled read-to-eat foods in school catering and probabilistic analysis of monocytogenes growth. International Journal of Food Microbiology,2004,96(1):49-59
[59]Qing S, Liu Y, Gareth P, et al. Wireless intelligent sensor networks for refrigerated vehicle. IEEE 6th CAS Symp. on Emerging Technologies:Mobile and Wireless Comm,2004,525-528
[60]Rain D N, Abraham T E. Kinetic study of a purified anionic peroxidase isolated from eupatorium odoratum and its novel application as time temperature indicator for food materials. Journal of food engineering,2006,77:594-600
[61]Ruiz G L, Barreiro P, Rodriguez B J, et al. Monitoring the intermodal, refrigerated transport of fruit using sensor networks. Spanish Journal of Agricultural Research,2007,5(2):142-156
[62]Ruiz G L, Barreiro P, Robla J I. Performance of Zigbee-based wireless sensor nodes for real-time monitoring of fruit logistics. Journal of Food Engineering,2008,87:405-415
[63]Shelby Z, Pomalaza R C, Karvonen H. Energy optimization in multihop wireless embedded and sensor networks. International Journal of Wireless Information Networks,2005,12(1):11-20
[64]Sholberg P L. Fumigation of table grapes with acetic acidto prevent post-harvest decay. Plant Disease,1996,80(12):1425-1428
[65]Soledad E D, Jesu C P, Alejandro C M, et al. A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks. Computers and Electronics in Agriculture,2011,76(2):252-265
[66]Subramanian V, Chang P C, Huang D, et al. All printed RFID tags:materials, devices and circuit implications. IEEE Computer Society, Proc. of the 19th Int Conf on VLSID06. Hyderabad, India,2006: 709-714
[67]Taoukis P, Labuza T. Applicability of time-temperature indicator as shelf life monitors of food products. Journal of food science,1989a,54:783-788
[68]Theofania T, Anastasios S, Marianna G, et al. Predictive modelling and selection of time temperature integrators for monitoring the shelf life of modified atmosphere packed gilthead seabream fillets. LWT-Food Science and Technology,2011,44(4):1156-1163
[69]Tomas S L, Daeyoung K. Wireless Sensor networks and RFID integration for context aware services. Auto-id Labs,2008
[70]Tsironi T, Dermesonlouoglou E, Giannakourou M. Shelf life modeling of frozen shrimp at variable temperature conditions. Food Science and Technology,2009,42:664-671
[71]Tsironi T, Gogou E. Application and validation of the TTI based chill chain management system SMAS on shelf life optimization of vacuum packed chilled tuna. International Journal of Food Microbiology,2008,128:108-115
[72]Tassou S A, De L G, Ge Y T. Food transport refrigeration-approaches to reduce energy consumption and environmental impacts of road transport. Applied Thermal Engineering,2009,29:1467-1477
[73]Tan K K, Huang S N, Zhang Y, et al. Distributed fault detection in industrial system based on sensor wireless network. Computer Standards & Interfaces,2009,31(3):573-578
[74]Vaikousi H, Biliaderis C G, Koutsoumains K P. Applicability of a microbial time temperature indicator(TTI) for monitoring spoilage of modified atmosphere packed minced meat. International Hournal of Food Microbiology,2009,133(3):272-278
[75]Vaikousi H, Biliaderis C G, Koutsoumains K P. Development of a microbial time/temperature indicator prototype for monitoring the microbiological qualitu of chilled foods. Applied and Environmental Microbiology,2008,74:3242-3250
[76]Vellidis G, Tucker M, Perry C, et al. A real-time wireless smart sensor array for scheduling irrigation. Computers and Electronics in Agriculture,2008,61(1):44-50
[77]Wang X, Jabbari A, Jedermann R, et al. Adaptive data sensing rate in ad-hoc sensor networks for autonomous transport application. Information Fusion.2010 13th Conference on, IEEE,2010:1-8
[78]Wang H, Yang Y H, Ma M D, et al. Network lifetime maximization with cross-layer design in wireless sensor networks. IEEE Transactions on Wreless Communications,2008,7(10):3759-3768
[79]Wendie L, Smout C, Van L, et al. From time temperature integrator kinetics to time temperature integrator tolerance levels:Heat-treated milk. American Chemical Society,2004,1:1-12
[80]Xing S, Fu Z, Lorena D, et al. Shelf life modeling of tilapia in the cold chain. Journal of Food, Agriculture & Environment,2012,10 (3&4):257-260
[81]Yan B, Lee D. Application of RFID in cold chain temperature monitoring system. Computing, Communication, Control and Management,2009:258-261
[82]Yahia E M. Modified and controlled atmosphere for the storage, transportation, and packaging of horticultural commodities. Boca Raton:CRC Press,2009
[83]Yan S, Huawei C, Limin Z, et al. Development and characterization of a new amylase type time-temperature indicator. Food control,2008,19,315-319
[84]Zhang J, Yan T, Son S H. Deployment strategies for differentiated detection in wireless sensor networks. Proc. of the 3rd Annual IEEE International Conference on Sensor Mesh and Ad Hoc Communications and Networks,2006
[85]鲍新中，杨宜,基于聚类-粗糙集-神经网络的企业财务危机预警.系统管理学报,2013,22(3)：358-365
[86]下梅.国内外农产品冷链物流发展比较研究.物流工程与管理,2011,33,11：33-35
[87]蔡振江,康健一,张青,等.数据融合技术在温室温度检测中的应用.农业机械学报,2006,37(10)：101-103
[88]程伟,下贵成,梁萍.基于多变量灰色模型的磨削参数预测研究.现代制造工程,2013,5：26-28
[89]陈红丽,陆华.冷链物流服务过程的质量评价.中国流通经济,2013,1：34-39
[90]陈超.车载自组织网络数据传输技术研究：[硕士学位论文].长沙：湖南大学,2012
[91]邓聚龙.灰预测与灰决策.武汉：华中科技大学出版社,2002
[92]邓聚龙.灰色控制系统.武汉：华中工学院出版社,1985
[93]董丁稳.基于安全监控系统实测数据的瓦斯浓度预测预警研究：[博士学位论文].西安：西安科技大学,2012
[94]傅泽田,邢少华,张小栓.食品质量安全可追溯关键技术发展研究.农业机械学报,2013,7：144-153
[95]付华,王宏云.基于特征分析模型的瓦斯监测传感器优化布点.传感器’与微系统,2009,28(11)：281-30
[96]郭凤仪,郭长娜,王洋洋.煤矿涌水量的灰色RBF网络预测模型.计算机测量与控制,2012,20(2)：300-310
[97]葛毅强,叶强,张维一.鲜食葡萄采后的SO2熏蒸贮运保鲜.中国果树,1998(1)：47-49
[98]郭全友,王锡吕,杨究时,等.不同贮藏温度下养殖大黄鱼货架期预测模型的构建.农业工程学报,2012,10：267-273
[99]郭文川,程寒杰,李瑞明,等.基于无线传感器网络的温室环境信息监测系统.农业机械学报,2010,41(7)：181-185
[100]郭斌, 钱建平, 张太红,等.基于ZigBee的果蔬冷链配送环境信息采集系统.农业工程学报,2011,27(6)：208-213
[101]顾延涛,刘成忠,徐纬芳.基于ZigBee和GSM的农产品物流信息采集系统设计.湖南农业科学,2012(1)：126-128
[102]谷雪莲,杜巍,华泽钊,等.预测牛乳货架期的时间-温度指示器的研制.农业工程学报,2005,21：142-146
[103]韩华峰,杜克明,孙忠富,等.基于ZigBee网络的温室环境远程监控系统设计与应用.农业工程学报,2009,25(7)：158-163
[104]韩谞,吕恩利,陆华忠,等.液氮充注气调保鲜运输厢内环境因素间耦合关系.农业工程 学报,2012,28(17)：275-280
[10j5]韩芝伙.基于GPRS和ZigBee的农旧信息监控系统设计.机械与电子,2011(12)：49-52.[106]何东健,邹志勇,周曼.果园环境参数远程检测WSN网关节点设计.农业机械学报,2010,41(6)：182-186
[107]何友, 王国宏,陆大黔,等.多传感器信息融合及应用.北京：电子工业出版社，2000
[108]侯雅文,王斌会.空气质量指数监控与预测的控制图方法.统计与信息论坛,2012,27(8)：86-89
[109]任颖.RFID冷链无线温度监控.射频世界，2008(6)：28-29
[110]蒋维均.化工原理.清华大学出版社,1992
[111]李骥平.商空间理论在煤矿预警中的应用：[硕士学位论文].太原：太原理工大学,2010
[112]李琛,刘颖,翁桢,等.贮运环境对葡萄品质的影响.现代食品科技,2013,29(2)：230-235
[113]李文生,王宝刚,杨军军,等.不同剂型二氧化硫对葡萄贮藏效果的影响.食品科技,2013,38(3)：40-42
[114]李志文,张平,黄艳凤,等.贮藏保鲜中SO伤害对红提葡萄香气组分的影.西北植物学报,2011,31(2)：385-392
[115]李家科,李亚娇,李怀恩,等.非点源污染负荷预测的多变量灰色神经网络模型.西北农林科技大学学报,2011,39(3)：229-234
[116]李志文,张平,刘翔,等.1-MCP结合冰温贮藏对葡萄采后品质及相关生理代谢的调控.食品科学,2011,32(20)：300-305
[117]李有平.条形码自动识别技术及其在设备巡检中的应用.水电自动化与大坝监测,2003(6)：32-34
[118]刘广海,孙永才,谢如鹤,等.冷藏集装箱渗透漏气量动态性能研究.第七届全国食品冷藏链大会论文集,2010
[119]刘鹏,王曙钊,白剑林.一种空间信息融合的DS算法研究.系统仿真学报.2008,20(1)：242-244
[120]刘浩荣,谢如鹤,刘广海,等.多温区冷藏车行驶调温性能试验研究.制冷,2012,31(1)：1-7
[121]刘卉,孟志军,徐敏,等.基于规则网格的农田环境监测传感器节点部署方法.农业工程学报,2011,27(8)：265-270
[122]刘璐.水产品冷链决策支持模型的构建：[博士学位论文].北京：中国农业大学,201086
[123]刘娜.鲜食葡萄物流的成本收益评价：[硕士学位论文].北京：中国农业大学,2012
[124]刘思峰,党耀国,方志耕.灰色系统理论及其应用.北京：科学出版社，2010
[125]刘伟荣,袁群.基于时间Petri网的生鲜肉冷链物流流程建模研究.物流技术,2012,31(9)：289-292
[126]刘卓军,李晓明.基于时间序列建模和控制图的异常交易检测方法.数学的实践与认识,2013,43(10)：89-95
[127]刘红斌.影响红提葡萄贮运保鲜质量的若干因素.天津农业科学,2007,13(1)：55-56
[128]梁丽雅,郝利平,闫师杰.保鲜剂对红地球和巨峰葡萄呼吸强度和贮藏品质的影响.农业工程学报,2003,19(4)：205-208
[129]吕广鹏.基于Web的医用冷链实时监测系统设计与实现：[硕士学位论文].中国海洋大学.2009
[130]罗大庸,张远.多传感器信息时空融合模型及算法研究.系统工程与电子技术,2004,26(1)：36-39
[131]卢立新,黄祥飞,华岩.基于模拟运输条件的梨果实包装振动损伤研究.农业工程学报,2009.25(6)：110-114
[132]马骏,杨小玲,李春媛,等.不同保鲜方法对新疆木纳格葡萄贮藏效果的影响.保鲜与加工2013,13(3)：24-27
[133]潘迪夫,赵杰.基于GPS/GSM/GIS技术的铁路冷藏车运行状态监测与信息管理系统.铁道科学与工程学报,2005,2(4)：92-96
[134]彭力.信息融合关键技术及其应用.北京：冶金工业出版社,2010
[135]乔磊,卢立新,唐亚丽,等.酶型时间温度指示器监测冷鲜猪肉贮藏货架期.农业工程学报,2013,29,13：263-269
[136]齐林,田东,张健,等.基于SPC的农产品冷链物流感知数据压缩方法.农业机械学报,2011,42(10)：129-134
[137]齐林,韩玉冰,张小栓,等.基于WSN的水产品冷链物流实时监测系统.农业机械学报,2012,43(8)：134-140
[138]阮萍,雷镇,王华.基于灰色系统和人工神经网络的中长期电力负荷预测.计算机应用.2004,24(6)：285-286
[139]宋岁岁,李雪艳.吐鲁番地区葡萄物流现状及对策研究.边疆经济与文化,2008,48(12)：8-9
[140]孙利民，李建中，陈渝,等.无线传感器网络.北京：清华大学出版社,2005
[141]孙春华.我国生鲜农产品冷链物流现状及发展对策分析.江苏农业科学,2013,41(1)：395-399
[142]孙家庆,王权,刘正武.设备热平衡.上海机械学院,1980
[143]孙克雷,秦汝祥.基于自适应分批估计的瓦斯监测多传感器数据融合研究.传感器与微系统,2011，30(10)：47-49
[144]苏光秋.葡萄采后生理研究综述.果树,2013,4：284-287
[145]王佳敏,王凤丽，张建喜.冷藏车车厢微环境信息感知系统设计.物联网技术,2013,2：21-24
[146]汪庭满,张小栓,陈炜,等.基于无线射频识别技术的罗非鱼冷链物流温度监控系统.农业工程学报,2011,27(9)：141-146
[147]万树平.多传感器数据的聚类融合方法.系统工程理论与实践,2008,5：131-135
[148]王盛,翟青,白雨晨.上海房地产市场预警的理实证分析.华东师范大学学报,2013,3：137-144.
[149]王翥,郝晓强,魏德宝.基于WSN和GPRS网络的远程水质监测系统.仪表技术与传感器，2010(1)：48-49
[150]王以忠,张锐,黄华芳,等.农产品质量溯源用RFID温度测量记录系统的研究.湖北农业科学,2008,47(3)：346-347
[151]王广海，吕恩利,陆华忠,等.保鲜运输用液氮充注气调控制系统的设计与试验.农业工程学报,2012,28(1)：255-259
[152]王军,苏剑波,席裕庚.多传感器融合与集成概述.机器人,2001,23(2)：183-186.
[153]翁桢,李琛,刘颖.葡萄贮运保鲜包装结构优化设计.森林工程,2012,28(4)：93-96
[154]吴有梅.葡萄采后桨果脱落及保鲜贮藏.植物生理学报,1992,18(3)：267-272
[155]吴颖.气调冷藏对葡萄品质及落粒的影响：[硕士学位论文].上海：上海交通大学,2006
[156]吴玉康,邓世建,袁刚强,等.SHTll数字式温湿度传感器的应用.工况自动化,2010,4：99-100
[157]毋庆刚.我国冷链物流发展现状与对策研究.中国流通经济,2011,2：24-28
[158]向敏.无线传感器网络节点数据管理与能耗研究：[博士学位论文].重庆：重庆大学,2009
[159]熊迎军,沈明霞,陆明洲,等.温室无线传感器网络系统实时数据融合算法.农业工程学报,2012,28(23)：160-166
[160]徐丽敏,马万太,朱银龙.基于物联网的冷鲜肉冷链物流信息采集及监控系统.电子产品世界,2013,6：45-47
[161]徐明德,陈武.模糊物元法在优选大气监测点中的应用.测试技术学报,2007,21(5)：391-395
[162]许丽忠,张江山,王菲凤.熵权多目标决策环境监测优化布点模刑及应用.环境工程,2007,25(1)：61-64
[163]徐倩,谢晶.多温区冷藏车厢体内温度场和速度场的数值模拟及优化设计.食品与机械,2008,24(6)：88-101
[164]徐倩,谢晶.食品安全与食品低温流通中的温度控制.上海水产大学学报,2007,16(2)：2-5
[165]邢少华.水产品冷链物流安全评价方法研究：[博十学位论文].北京：中国农业大学,2013
[166]杨斌,田勇,檀僳锰,等.基于时间序列预测的延迟容忍网络路由算法.北京邮电大学学报,2011,34(6)：91-94
[167]杨祯山,邵诚.基于灰色RBF神经网络模型的电梯层间交通分布预测.信息与控制,2008,37(6)：690-696
[168]杨寿发,徐锡春.实施产地预冷完善鲜食葡萄低温物流冷链.中外葡萄与葡萄酒,2011,3：47-50
[169]易冬梅.新疆葡萄运输中的冷藏问题及对策.冷饮与速冻食品工业,2005,11(4)：33-35
[170]袁景凌,钟珞,李小燕.灰色神经网络的研究及发展.武汉理工大学学报,2009,31(3)：91-93
[171]张荣标,谷国栋,冯友兵,等.基于IEEE 802.15.4的温室无线监控系统的通信实现.农业机械学报,2008,39(8)：119-122
[172]翟翌立,戴逸松.多传感器数据自适应加权融合估计算法的研究.计量学报,1998,19(1)：69-75.
[173]张佳薇,曹军.基于分布图与分批估计的自校准层融合算法研究.森林工程,2012,26(6)：26-29
[174]张捍东,孙成慧,岑豫皖.分布式多传感器结构中的数据融合方法.华中科技大学学报,2008,36(6)：37-39
[175]张有林,苏东华.轻工科技兴农.北京：中国轻工业出版社,2000.
[176]张则增.全国乡镇企业经济效益的灰色评估.系统工程理论方法应用,1994,2：67-74.
[177]张世强.基于信息再利用的灰色系统GM(1,1)模型建模方法及应用.数学的实践与认识,2009,39(13)：97-103
[178]张连振,黄侨,王潮海.基于多目标遗传算法的传感器优化布点研究.工程力学,2007,4：168-172
[179]张敬磊,王晓原.交通流灰色RBF网络非线性组合预测方法.数学的实践与认识,2011,41(19)：1-7
[180]张平,王莉,朱志强,等.鲜食葡萄贮运保鲜技术与现代低温物流技术体系.保鲜与加工2011,11(6)：1-5
[181]张俊巧.日本水果流通保鲜技术的发展及状况.广西园艺,2007,18(2)：32-33
[182]张建军,杨艳玲.我国农产品冷链物流发展现状及发展趋势研究.物流科技,2013,2：102-105
[183]张雷.基于多传感器集成的葡萄冷链物流监测系统研究：[硕十学位论文].北京：中国农业大学,2012
[184]张平,田海龙,崔亚东,等.塑料薄膜和箱式气调对巨峰葡萄贮藏品质和采后生理影响的研究.保鲜与加工,2012,12(2)：14-19
[185]张连文,杨传民,王勇.樱桃侨茄运输包装件振动冲击性能试验.农业机械学报,2011,42(3)：125-130
[186]赵淑娥.硫代巴比妥酸法(TBA模型)预测鱼糜制品保藏货架期研究.食品科技,2012,2：26-27
[187]张旭.鲜食葡萄物流能耗评估方法与实证研究：[硕士学位论文].北京：中国农业大学,2012
[188]赵长青,傅泽田,刘雪,等.食品冷链运输中温度监控与预警系统.微计算机信息，2010,26(6)：27-28
[189]赵英霞.中国农产品冷链物流发展对策探讨.哈尔滨工业大学学报,2010,(2)：44-48
[190]赵秋艳,汪洋,乔明武,等.有机RFID标签在动物食品溯源中的应用前景.农业工程学报,2012,8：154-158
[191]中国冷链物流网.中国冷链物流行业数据及现状
[192]袁景凌,钟珞,李小燕.灰色神经网络的研究及发展.武汉理工大学学报,2009,31(3)：91-93
[193]周然,李云飞.不同强度的运输振动对黄花梨的机械损伤及贮藏品质的影响.农业工程学报,2007,23(11)：255-259
[194]郑海浪.冷冻冷藏业如何优化冷链物流.商品储运与养护,2004,2(4)：8-10
[195]邹晓红.基于短信业务的冷藏车温度监测系统的研究.信息技术,2005,10：112-116
[196]朱志强,高小生,张平,等.不同保鲜剂结合冰温对玫瑰香葡萄贮藏品质和生理生化的影响.食品工业科技,2013,34(5)：333-337
[197]朱琼瑶,张光新,冯天恒,等.基于粗糙集和证据理论的水质分析预警技术研究.浙江大学学报,2012,38(6)：747-754
[198]朱耀强,龚婷,赵思明,等.生鲜鲩鱼片货架期预测模型的建立与评价.食品工业科技,2012,2：380-382
[2]Arici T, Altunbasak Y. Adaptive sensing for environment monitoring using wireless sensor networks. IEEE WCNC,2004,5(1):2350-2355
[3]Baranyi J, Roberts T A. A dynamic approach to predicting microbial growth in food.International journal of food microbiology,1994,23:277-294
[4]Baucer B A, Knorr D. The impact of pressure,temperature and treatment time on starches: pressure-induced starch gelatinization as pressure time temperature indicator for high hydrostatic pressure processing. Journal of food engineering,2005,68(3):329-334
[5]Becker M, Yuan S, Jedermann R, et al. Challenges of aplying wireless sensor networks in logistics. In:CEWIT 2009. Wireless and IT driving Healthcare, Energy and Infrastructure Transformation,2009
[6]Bobelyn E, Hertog M L A T M, Nicolai B M. Applicability of an enzymatic time temperature integrator as a quality indicator for mushrooms in the distributrion chain. Postharvest biology and technology,2006,42(1):104-114
[7]Carol C. Safety Stars with Temperature Control. Food Logistics,2007,11:16-20
[8]Chen J, Pang Z, Zhang Z, et al. A novel acceleration data compression scheme for wireless sensor network application in fresh food tracking system. ICEMI'09,2009
[9]Christian C, Thomas B, Mandenius C F. Integration of distributed multi-analyzer monitoring and control in bioprogressing based on a real-time expert system. Journal of Biotechnology,2003,103: 237-248
[10]Chakrabarty K, Iyengar S S, Qi H, et al. Grid coverage for surveillance and target location in distributed sensor networks. IEEE transactions on computers,2002,51:1448-1453
[11]Chen P Y, Chen W T. A group tourguide system with RFIDs and wireless sensor networks. IPSN, 2007:561-562
[12]Chunbae L, Wen W, Sangsik Y, et al. Development of SAW-based multi-gas sensor for simultaneous detection of CO2 and NO2. Sensors and Actuators B:Chemical,2011,154(1):6-16
[13]Costa C, Lucera A, Conte A, et al. Effects of passive and active modified atmosphere packaging conditions on ready-to-eat table grape. Journal of Food Engineering,2011,102:115-121
[14]Deligiannakis A, Kotidis Y, RoussopoulosN. Compressing Historical Informafion in Sensor Networks. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, USA,2004:527-538
[15]Deng Y, Wu Y, Li Y, et al. Studies of postharvest berry abscission of 'Kyoho' table grapes during cold storage and high oxygen atmospheres. Postharvest Biology and Technology,2007,43:95-101
[16]Elisabeth I Z, Zsolt K, Fred V B, et al. A survey of applications and requirements of unique identification systems and RFID techniques. Computers in Industry,2011,62(3):227-252
[17]Ellouze M, Augustin J C. Applicability of biological time temperature integrators as quality and safety indicators for meat products. International Journal of Food Microbiology,2010,138:119-129
[18]Eunji K, Dong Y C, Hyun C K, et al. Calibrations between the variables of microbial TTI response and ground pork qualities. Meat Science,2013,95 (2):362-367
[19]Fabio Z, Fabio A, Letizia F, et al. Sensors and actuators A. Physical,2010,158:169-175 [20]Foster A M, Swain M J, Barrett R. et al. Experimentalverification of analytical and CFD predictions ofinfiltration through cold store entrances. International Journal of Refrigeration.2003,26(8):918-925
[21]Ganesh M. Introduction to fuzzy sets and fuzzy logic. Prentice-hall,2008
[22]Giannakourou M C, Koutsoumanis K, Nychas G J E, et al. Field cvalution of the application of time temperature integrators for monitoring fish quality in the chill chain.International. Journal of Food Microbiology,2005,102(3):323-336
[23]Giannakourou M C, Taoukis P S. Kinetic modeling of vitamin C loss in frozen green vegetables under variable storage conditions. Food Chemisty,2003,83(1):33-41
[24]Hallie F, Christopher W. Cold chain concerns.Pharmaceutical Technology,2005,4:44
[25]Hai L, Miodrag B, Amiya N, et al. Taxonomy and challenges of the integration of RFID and wireless sensor net works. IEEE Network,2008,22:26-35
[26]James S J, James C, Evans J A. Modelling of food transportation systems-a review. Int. J. Refrig, 2006,29:947-957
[27]James S J, James C. The food cold-chain and climate change. Food Research International,2010, 43(7):1955-1956
[28]Jarimopas B, Singh S P. Measurement and analysis of truck transport vibration levels and damage to packaged tangerines during transition. Package Technology & Science,2005,18:179-188
[29]Jedermann R, Lang W. Semi-passive RFID and beyond:steps towards automated quality tracing in the food chain, Int. J. Radio Frequency Identification Technology and Applications,2007,1(3): 247-259
[30]Jedermann R, Ruiz G L, Lang W. Spatial temperature profiling by semi passive RFID loggers for perishable food transportation. Computers and Electronics in Agriculture.2009,65(2):145-154
[31]Jose M L, Sara T, Roberto B, et al. Changes in physico-chemical, microbiological, textural and sensory attributes during ripening of dry-cured foal salchichon. Meat Science,2012,90(1):194-198
[32]Kammer D C, Tinker M L. Optimal placement of triaxial accelerometers for modal vibrate tests. Mechanical Systems and Signal Processing,2004,18:29-41
[33]Kacimi R, Dhaou R, Beylot A. Using energy-efficient wireless sensor network for cold chain monitoring. Consumer Communications and Networking Conference,2009:1-5
[34]Kentaro M, Ynzuru M, Shokaku K, et al. Phaseseparable aqueous amide solutions as a thermal history indicator. Biosci Biotechnol Biochem,2008,72(12):3314-3317
[35]Kim M J, Jung S W, Park H R, et al. Selection of an optimum pH-indicator for developing lactic acid bacteria-based time-temperature integrators (TTI). Journal of Food Engineering,2012,113(3): 471-478
[36]Lin J P. Pre-warning system of coal mine based on ICA and kalman filter. Sixth International Conf. on Natural Computation,2010
[37]Li Y, Kramer M R, Beulens A J M, et al. A framework for early warning and proactive control systems in food chain networks. Computers in Industry,2010,61:852-862
[38]Lester E J. Freezing effects on food quality. New York:Marcel Dekker Inc,1995
[39]Luo H, Luo J, Liu Y, et al. Adaptive data fusion for energy efficient routing in wireless sensor networks, IEEE Trans. on Comp,2003,23(41):43-49
[40]Mai N T T, Gudjansdottir M, Lauzon H L, et al. Continuous quality and shelf life monitoring of retail-packed fresh cod loins in comparison with conventional methods. Food Control,2011,22(6): 1000-1007
[41]Ma X. Application of data fusion theory in coal gas fire prediction system.2008 International Co nference on Intelligent Computation Technology and Automation.2008
[42]Marija B, Ludvik B, Robert V. Stability of perishable goods in cold logistic chains. International Journal of Production Economics,2005,93:345-356
[43]Mertens K, Vaeson I, Loffel J. Data-based design of an intelligent control chart for the daily monitoring of the average egg weight. Computers and Electronics in Agricuture,2008,61:222-232
[44]Mixhael R. State-of-the-Art XML support in RDBMS:Mircosoft SQL server's XML fratures. IEEE Transactions on Knowledge and Data Engineering,2006
[45]Moureh J. Airflow patterns within a refrigerated truckloaded with slotted filled boxes. USA, CRC Press, Boca Raton,2009
[46]Marija B, Ludvik B, Robert V. Stability of perishable goods in cold logistic chains. International Journal of Production Economics,2005,93:345-356
[47]Nga M, Audorff H, Reichstein W, et al. Performance of a photochromic time-temperature indicator under simulated fresh fish supply chain conditions. International Journal of Food Science and Technology,2011,46:297-304
[48]Ning W, Zhang N, Wang M. Wireless sensors in agriculture and food industry-recent development and future perspective. Computers and Electronics in Agriculture,2006,50(1):1-14
[49]Nelson C, Froes P. Monitoring temperatures in the vaccine cold chain in Bolivia. Vaccine,2007, 25:433-437
[50]Nojeong H, Varshney P K. Energy-efficient deployment of intelligent mobile sensor networks. IEEE Transactions on Systems, Man and Cybernetics:Part A,2005,35(1):78-92
[51]Nopwinyuwong A, Trevanish S, Suppakul P. Development of a novel colorimetric indicator label for monitoring freshness of intermediate-moisture dessert spoilage. Talanta,2010,81(3):1126-1132
[52]Ogasawara A, Yamasaki K. A temperature-managed traceability system using RFID tags with embedded temperature sensors. NEC Technical Journal,2006,1(2):82-86
[53]Olivier S, Marc B, Anne M. Model reduction via principal component truncation truncation for the optimal design of atmospheric monitoring networks. Atmospheric Environment,2009,43(32): 4940-4950
[54]Oprea A, Courbat J, et al. Temperature, humidity and gas sensors integrated on plastic foil for low power applications. Sensors and Actuators B:Chemical,2009:227-232
[55]Pan Y, Lu X. Energy-efficient lifetime maximization and sleeping scheduling supporting data fusion and OoS in multi-sensor net. Signal Processing,2007,87(12):2949-2964
[56]Pattern S, Krishnamachari B, Govindan R. The impact of spatial correlation on routing with compression in wireless sensor networks. ACM Transactions on Sensor Networks.2008,4(4):24-35
[57]Pierce F J, Elliott T V. Regionaland on-farm wireless sensor networks for agricultural systems in Eastern Washington. Computersand Electronicsin Agriculture,2008,61(1):32-43
[58]Philippe R, Marie C, Veronique N, et al. Time-temperature profiles of chilled read-to-eat foods in school catering and probabilistic analysis of monocytogenes growth. International Journal of Food Microbiology,2004,96(1):49-59
[59]Qing S, Liu Y, Gareth P, et al. Wireless intelligent sensor networks for refrigerated vehicle. IEEE 6th CAS Symp. on Emerging Technologies:Mobile and Wireless Comm,2004,525-528
[60]Rain D N, Abraham T E. Kinetic study of a purified anionic peroxidase isolated from eupatorium odoratum and its novel application as time temperature indicator for food materials. Journal of food engineering,2006,77:594-600
[61]Ruiz G L, Barreiro P, Rodriguez B J, et al. Monitoring the intermodal, refrigerated transport of fruit using sensor networks. Spanish Journal of Agricultural Research,2007,5(2):142-156
[62]Ruiz G L, Barreiro P, Robla J I. Performance of Zigbee-based wireless sensor nodes for real-time monitoring of fruit logistics. Journal of Food Engineering,2008,87:405-415
[63]Shelby Z, Pomalaza R C, Karvonen H. Energy optimization in multihop wireless embedded and sensor networks. International Journal of Wireless Information Networks,2005,12(1):11-20
[64]Sholberg P L. Fumigation of table grapes with acetic acidto prevent post-harvest decay. Plant Disease,1996,80(12):1425-1428
[65]Soledad E D, Jesu C P, Alejandro C M, et al. A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks. Computers and Electronics in Agriculture,2011,76(2):252-265
[66]Subramanian V, Chang P C, Huang D, et al. All printed RFID tags:materials, devices and circuit implications. IEEE Computer Society, Proc. of the 19th Int Conf on VLSID06. Hyderabad, India,2006: 709-714
[67]Taoukis P, Labuza T. Applicability of time-temperature indicator as shelf life monitors of food products. Journal of food science,1989a,54:783-788
[68]Theofania T, Anastasios S, Marianna G, et al. Predictive modelling and selection of time temperature integrators for monitoring the shelf life of modified atmosphere packed gilthead seabream fillets. LWT-Food Science and Technology,2011,44(4):1156-1163
[69]Tomas S L, Daeyoung K. Wireless Sensor networks and RFID integration for context aware services. Auto-id Labs,2008
[70]Tsironi T, Dermesonlouoglou E, Giannakourou M. Shelf life modeling of frozen shrimp at variable temperature conditions. Food Science and Technology,2009,42:664-671
[71]Tsironi T, Gogou E. Application and validation of the TTI based chill chain management system SMAS on shelf life optimization of vacuum packed chilled tuna. International Journal of Food Microbiology,2008,128:108-115
[72]Tassou S A, De L G, Ge Y T. Food transport refrigeration-approaches to reduce energy consumption and environmental impacts of road transport. Applied Thermal Engineering,2009,29:1467-1477
[73]Tan K K, Huang S N, Zhang Y, et al. Distributed fault detection in industrial system based on sensor wireless network. Computer Standards & Interfaces,2009,31(3):573-578
[74]Vaikousi H, Biliaderis C G, Koutsoumains K P. Applicability of a microbial time temperature indicator(TTI) for monitoring spoilage of modified atmosphere packed minced meat. International Hournal of Food Microbiology,2009,133(3):272-278
[75]Vaikousi H, Biliaderis C G, Koutsoumains K P. Development of a microbial time/temperature indicator prototype for monitoring the microbiological qualitu of chilled foods. Applied and Environmental Microbiology,2008,74:3242-3250
[76]Vellidis G, Tucker M, Perry C, et al. A real-time wireless smart sensor array for scheduling irrigation. Computers and Electronics in Agriculture,2008,61(1):44-50
[77]Wang X, Jabbari A, Jedermann R, et al. Adaptive data sensing rate in ad-hoc sensor networks for autonomous transport application. Information Fusion.2010 13th Conference on, IEEE,2010:1-8
[78]Wang H, Yang Y H, Ma M D, et al. Network lifetime maximization with cross-layer design in wireless sensor networks. IEEE Transactions on Wreless Communications,2008,7(10):3759-3768
[79]Wendie L, Smout C, Van L, et al. From time temperature integrator kinetics to time temperature integrator tolerance levels:Heat-treated milk. American Chemical Society,2004,1:1-12
[80]Xing S, Fu Z, Lorena D, et al. Shelf life modeling of tilapia in the cold chain. Journal of Food, Agriculture & Environment,2012,10 (3&4):257-260
[81]Yan B, Lee D. Application of RFID in cold chain temperature monitoring system. Computing, Communication, Control and Management,2009:258-261
[82]Yahia E M. Modified and controlled atmosphere for the storage, transportation, and packaging of horticultural commodities. Boca Raton:CRC Press,2009
[83]Yan S, Huawei C, Limin Z, et al. Development and characterization of a new amylase type time-temperature indicator. Food control,2008,19,315-319
[84]Zhang J, Yan T, Son S H. Deployment strategies for differentiated detection in wireless sensor networks. Proc. of the 3rd Annual IEEE International Conference on Sensor Mesh and Ad Hoc Communications and Networks,2006
[85]鲍新中，杨宜,基于聚类-粗糙集-神经网络的企业财务危机预警.系统管理学报,2013,22(3)：358-365
[86]下梅.国内外农产品冷链物流发展比较研究.物流工程与管理,2011,33,11：33-35
[87]蔡振江,康健一,张青,等.数据融合技术在温室温度检测中的应用.农业机械学报,2006,37(10)：101-103
[88]程伟,下贵成,梁萍.基于多变量灰色模型的磨削参数预测研究.现代制造工程,2013,5：26-28
[89]陈红丽,陆华.冷链物流服务过程的质量评价.中国流通经济,2013,1：34-39
[90]陈超.车载自组织网络数据传输技术研究：[硕士学位论文].长沙：湖南大学,2012
[91]邓聚龙.灰预测与灰决策.武汉：华中科技大学出版社,2002
[92]邓聚龙.灰色控制系统.武汉：华中工学院出版社,1985
[93]董丁稳.基于安全监控系统实测数据的瓦斯浓度预测预警研究：[博士学位论文].西安：西安科技大学,2012
[94]傅泽田,邢少华,张小栓.食品质量安全可追溯关键技术发展研究.农业机械学报,2013,7：144-153
[95]付华,王宏云.基于特征分析模型的瓦斯监测传感器优化布点.传感器’与微系统,2009,28(11)：281-30
[96]郭凤仪,郭长娜,王洋洋.煤矿涌水量的灰色RBF网络预测模型.计算机测量与控制,2012,20(2)：300-310
[97]葛毅强,叶强,张维一.鲜食葡萄采后的SO2熏蒸贮运保鲜.中国果树,1998(1)：47-49
[98]郭全友,王锡吕,杨究时,等.不同贮藏温度下养殖大黄鱼货架期预测模型的构建.农业工程学报,2012,10：267-273
[99]郭文川,程寒杰,李瑞明,等.基于无线传感器网络的温室环境信息监测系统.农业机械学报,2010,41(7)：181-185
[100]郭斌, 钱建平, 张太红,等.基于ZigBee的果蔬冷链配送环境信息采集系统.农业工程学报,2011,27(6)：208-213
[101]顾延涛,刘成忠,徐纬芳.基于ZigBee和GSM的农产品物流信息采集系统设计.湖南农业科学,2012(1)：126-128
[102]谷雪莲,杜巍,华泽钊,等.预测牛乳货架期的时间-温度指示器的研制.农业工程学报,2005,21：142-146
[103]韩华峰,杜克明,孙忠富,等.基于ZigBee网络的温室环境远程监控系统设计与应用.农业工程学报,2009,25(7)：158-163
[104]韩谞,吕恩利,陆华忠,等.液氮充注气调保鲜运输厢内环境因素间耦合关系.农业工程 学报,2012,28(17)：275-280
[10j5]韩芝伙.基于GPRS和ZigBee的农旧信息监控系统设计.机械与电子,2011(12)：49-52.[106]何东健,邹志勇,周曼.果园环境参数远程检测WSN网关节点设计.农业机械学报,2010,41(6)：182-186
[107]何友, 王国宏,陆大黔,等.多传感器信息融合及应用.北京：电子工业出版社，2000
[108]侯雅文,王斌会.空气质量指数监控与预测的控制图方法.统计与信息论坛,2012,27(8)：86-89
[109]任颖.RFID冷链无线温度监控.射频世界，2008(6)：28-29
[110]蒋维均.化工原理.清华大学出版社,1992
[111]李骥平.商空间理论在煤矿预警中的应用：[硕士学位论文].太原：太原理工大学,2010
[112]李琛,刘颖,翁桢,等.贮运环境对葡萄品质的影响.现代食品科技,2013,29(2)：230-235
[113]李文生,王宝刚,杨军军,等.不同剂型二氧化硫对葡萄贮藏效果的影响.食品科技,2013,38(3)：40-42
[114]李志文,张平,黄艳凤,等.贮藏保鲜中SO伤害对红提葡萄香气组分的影.西北植物学报,2011,31(2)：385-392
[115]李家科,李亚娇,李怀恩,等.非点源污染负荷预测的多变量灰色神经网络模型.西北农林科技大学学报,2011,39(3)：229-234
[116]李志文,张平,刘翔,等.1-MCP结合冰温贮藏对葡萄采后品质及相关生理代谢的调控.食品科学,2011,32(20)：300-305
[117]李有平.条形码自动识别技术及其在设备巡检中的应用.水电自动化与大坝监测,2003(6)：32-34
[118]刘广海,孙永才,谢如鹤,等.冷藏集装箱渗透漏气量动态性能研究.第七届全国食品冷藏链大会论文集,2010
[119]刘鹏,王曙钊,白剑林.一种空间信息融合的DS算法研究.系统仿真学报.2008,20(1)：242-244
[120]刘浩荣,谢如鹤,刘广海,等.多温区冷藏车行驶调温性能试验研究.制冷,2012,31(1)：1-7
[121]刘卉,孟志军,徐敏,等.基于规则网格的农田环境监测传感器节点部署方法.农业工程学报,2011,27(8)：265-270
[122]刘璐.水产品冷链决策支持模型的构建：[博士学位论文].北京：中国农业大学,201086
[123]刘娜.鲜食葡萄物流的成本收益评价：[硕士学位论文].北京：中国农业大学,2012
[124]刘思峰,党耀国,方志耕.灰色系统理论及其应用.北京：科学出版社，2010
[125]刘伟荣,袁群.基于时间Petri网的生鲜肉冷链物流流程建模研究.物流技术,2012,31(9)：289-292
[126]刘卓军,李晓明.基于时间序列建模和控制图的异常交易检测方法.数学的实践与认识,2013,43(10)：89-95
[127]刘红斌.影响红提葡萄贮运保鲜质量的若干因素.天津农业科学,2007,13(1)：55-56
[128]梁丽雅,郝利平,闫师杰.保鲜剂对红地球和巨峰葡萄呼吸强度和贮藏品质的影响.农业工程学报,2003,19(4)：205-208
[129]吕广鹏.基于Web的医用冷链实时监测系统设计与实现：[硕士学位论文].中国海洋大学.2009
[130]罗大庸,张远.多传感器信息时空融合模型及算法研究.系统工程与电子技术,2004,26(1)：36-39
[131]卢立新,黄祥飞,华岩.基于模拟运输条件的梨果实包装振动损伤研究.农业工程学报,2009.25(6)：110-114
[132]马骏,杨小玲,李春媛,等.不同保鲜方法对新疆木纳格葡萄贮藏效果的影响.保鲜与加工2013,13(3)：24-27
[133]潘迪夫,赵杰.基于GPS/GSM/GIS技术的铁路冷藏车运行状态监测与信息管理系统.铁道科学与工程学报,2005,2(4)：92-96
[134]彭力.信息融合关键技术及其应用.北京：冶金工业出版社,2010
[135]乔磊,卢立新,唐亚丽,等.酶型时间温度指示器监测冷鲜猪肉贮藏货架期.农业工程学报,2013,29,13：263-269
[136]齐林,田东,张健,等.基于SPC的农产品冷链物流感知数据压缩方法.农业机械学报,2011,42(10)：129-134
[137]齐林,韩玉冰,张小栓,等.基于WSN的水产品冷链物流实时监测系统.农业机械学报,2012,43(8)：134-140
[138]阮萍,雷镇,王华.基于灰色系统和人工神经网络的中长期电力负荷预测.计算机应用.2004,24(6)：285-286
[139]宋岁岁,李雪艳.吐鲁番地区葡萄物流现状及对策研究.边疆经济与文化,2008,48(12)：8-9
[140]孙利民，李建中，陈渝,等.无线传感器网络.北京：清华大学出版社,2005
[141]孙春华.我国生鲜农产品冷链物流现状及发展对策分析.江苏农业科学,2013,41(1)：395-399
[142]孙家庆,王权,刘正武.设备热平衡.上海机械学院,1980
[143]孙克雷,秦汝祥.基于自适应分批估计的瓦斯监测多传感器数据融合研究.传感器与微系统,2011，30(10)：47-49
[144]苏光秋.葡萄采后生理研究综述.果树,2013,4：284-287
[145]王佳敏,王凤丽，张建喜.冷藏车车厢微环境信息感知系统设计.物联网技术,2013,2：21-24
[146]汪庭满,张小栓,陈炜,等.基于无线射频识别技术的罗非鱼冷链物流温度监控系统.农业工程学报,2011,27(9)：141-146
[147]万树平.多传感器数据的聚类融合方法.系统工程理论与实践,2008,5：131-135
[148]王盛,翟青,白雨晨.上海房地产市场预警的理实证分析.华东师范大学学报,2013,3：137-144.
[149]王翥,郝晓强,魏德宝.基于WSN和GPRS网络的远程水质监测系统.仪表技术与传感器，2010(1)：48-49
[150]王以忠,张锐,黄华芳,等.农产品质量溯源用RFID温度测量记录系统的研究.湖北农业科学,2008,47(3)：346-347
[151]王广海，吕恩利,陆华忠,等.保鲜运输用液氮充注气调控制系统的设计与试验.农业工程学报,2012,28(1)：255-259
[152]王军,苏剑波,席裕庚.多传感器融合与集成概述.机器人,2001,23(2)：183-186.
[153]翁桢,李琛,刘颖.葡萄贮运保鲜包装结构优化设计.森林工程,2012,28(4)：93-96
[154]吴有梅.葡萄采后桨果脱落及保鲜贮藏.植物生理学报,1992,18(3)：267-272
[155]吴颖.气调冷藏对葡萄品质及落粒的影响：[硕士学位论文].上海：上海交通大学,2006
[156]吴玉康,邓世建,袁刚强,等.SHTll数字式温湿度传感器的应用.工况自动化,2010,4：99-100
[157]毋庆刚.我国冷链物流发展现状与对策研究.中国流通经济,2011,2：24-28
[158]向敏.无线传感器网络节点数据管理与能耗研究：[博士学位论文].重庆：重庆大学,2009
[159]熊迎军,沈明霞,陆明洲,等.温室无线传感器网络系统实时数据融合算法.农业工程学报,2012,28(23)：160-166
[160]徐丽敏,马万太,朱银龙.基于物联网的冷鲜肉冷链物流信息采集及监控系统.电子产品世界,2013,6：45-47
[161]徐明德,陈武.模糊物元法在优选大气监测点中的应用.测试技术学报,2007,21(5)：391-395
[162]许丽忠,张江山,王菲凤.熵权多目标决策环境监测优化布点模刑及应用.环境工程,2007,25(1)：61-64
[163]徐倩,谢晶.多温区冷藏车厢体内温度场和速度场的数值模拟及优化设计.食品与机械,2008,24(6)：88-101
[164]徐倩,谢晶.食品安全与食品低温流通中的温度控制.上海水产大学学报,2007,16(2)：2-5
[165]邢少华.水产品冷链物流安全评价方法研究：[博十学位论文].北京：中国农业大学,2013
[166]杨斌,田勇,檀僳锰,等.基于时间序列预测的延迟容忍网络路由算法.北京邮电大学学报,2011,34(6)：91-94
[167]杨祯山,邵诚.基于灰色RBF神经网络模型的电梯层间交通分布预测.信息与控制,2008,37(6)：690-696
[168]杨寿发,徐锡春.实施产地预冷完善鲜食葡萄低温物流冷链.中外葡萄与葡萄酒,2011,3：47-50
[169]易冬梅.新疆葡萄运输中的冷藏问题及对策.冷饮与速冻食品工业,2005,11(4)：33-35
[170]袁景凌,钟珞,李小燕.灰色神经网络的研究及发展.武汉理工大学学报,2009,31(3)：91-93
[171]张荣标,谷国栋,冯友兵,等.基于IEEE 802.15.4的温室无线监控系统的通信实现.农业机械学报,2008,39(8)：119-122
[172]翟翌立,戴逸松.多传感器数据自适应加权融合估计算法的研究.计量学报,1998,19(1)：69-75.
[173]张佳薇,曹军.基于分布图与分批估计的自校准层融合算法研究.森林工程,2012,26(6)：26-29
[174]张捍东,孙成慧,岑豫皖.分布式多传感器结构中的数据融合方法.华中科技大学学报,2008,36(6)：37-39
[175]张有林,苏东华.轻工科技兴农.北京：中国轻工业出版社,2000.
[176]张则增.全国乡镇企业经济效益的灰色评估.系统工程理论方法应用,1994,2：67-74.
[177]张世强.基于信息再利用的灰色系统GM(1,1)模型建模方法及应用.数学的实践与认识,2009,39(13)：97-103
[178]张连振,黄侨,王潮海.基于多目标遗传算法的传感器优化布点研究.工程力学,2007,4：168-172
[179]张敬磊,王晓原.交通流灰色RBF网络非线性组合预测方法.数学的实践与认识,2011,41(19)：1-7
[180]张平,王莉,朱志强,等.鲜食葡萄贮运保鲜技术与现代低温物流技术体系.保鲜与加工2011,11(6)：1-5
[181]张俊巧.日本水果流通保鲜技术的发展及状况.广西园艺,2007,18(2)：32-33
[182]张建军,杨艳玲.我国农产品冷链物流发展现状及发展趋势研究.物流科技,2013,2：102-105
[183]张雷.基于多传感器集成的葡萄冷链物流监测系统研究：[硕十学位论文].北京：中国农业大学,2012
[184]张平,田海龙,崔亚东,等.塑料薄膜和箱式气调对巨峰葡萄贮藏品质和采后生理影响的研究.保鲜与加工,2012,12(2)：14-19
[185]张连文,杨传民,王勇.樱桃侨茄运输包装件振动冲击性能试验.农业机械学报,2011,42(3)：125-130
[186]赵淑娥.硫代巴比妥酸法(TBA模型)预测鱼糜制品保藏货架期研究.食品科技,2012,2：26-27
[187]张旭.鲜食葡萄物流能耗评估方法与实证研究：[硕士学位论文].北京：中国农业大学,2012
[188]赵长青,傅泽田,刘雪,等.食品冷链运输中温度监控与预警系统.微计算机信息，2010,26(6)：27-28
[189]赵英霞.中国农产品冷链物流发展对策探讨.哈尔滨工业大学学报,2010,(2)：44-48
[190]赵秋艳,汪洋,乔明武,等.有机RFID标签在动物食品溯源中的应用前景.农业工程学报,2012,8：154-158
[191]中国冷链物流网.中国冷链物流行业数据及现状
[192]袁景凌,钟珞,李小燕.灰色神经网络的研究及发展.武汉理工大学学报,2009,31(3)：91-93
[193]周然,李云飞.不同强度的运输振动对黄花梨的机械损伤及贮藏品质的影响.农业工程学报,2007,23(11)：255-259
[194]郑海浪.冷冻冷藏业如何优化冷链物流.商品储运与养护,2004,2(4)：8-10
[195]邹晓红.基于短信业务的冷藏车温度监测系统的研究.信息技术,2005,10：112-116
[196]朱志强,高小生,张平,等.不同保鲜剂结合冰温对玫瑰香葡萄贮藏品质和生理生化的影响.食品工业科技,2013,34(5)：333-337
[197]朱琼瑶,张光新,冯天恒,等.基于粗糙集和证据理论的水质分析预警技术研究.浙江大学学报,2012,38(6)：747-754
[198]朱耀强,龚婷,赵思明,等.生鲜鲩鱼片货架期预测模型的建立与评价.食品工业科技,2012,2：380-382
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