基于光谱技术的蚕桑相关特性数字化研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
数字农业已经成为21世纪农业信息化发展战略的支撑技术。它从农业生产、农业管理以及农业科学研究的实际出发,将各个环节准确定量并加以数字化,为全方位的农业科学管理、合理决策提供基础技术平台,为农业科学研究提供全新的定量定性研究方法。蚕桑业是我国的传统优势行业,但随着社会、经济和科技的高速发展,现正面临着科学化、信息化、高效化的挑战。光谱技术是实现数字化农业研究的重要技术,近年来在许多农作物的相关特性研究上取得了成功经验,但光谱技术在蚕、桑上的研究则未见报道。为此,本论文就光谱技术在蚕桑行业中的应用进行了一系列的研究与探索,为推进数字蚕桑奠定了一定的基础,取得的主要研究成果如下:
     (1)对所采用的SPAD502叶绿素仪在桑叶测量时不同叶位、不同品种的影响差异性做了统计分析,发现这两个因素对SPAD的测量结果有显著差异。
     (2)建立了9个品种桑叶的叶绿素含量与SPAD值之间的关系,并列出了方程。发现9个品种均呈正相关关系,说明采用SPAD仪可以用来对桑叶的营养状况做出简单判断,但是对于不同的品种,其具体数值需要进行校正。采用SPAD502研究了同一叶片、同一植株不同分支处SPAD值的分布情况,并采用ArcView3.2地理信息系统软件中的空间分析模块对其进行了形象、直观化的表达。结果表明,对于同一叶片,测量部位不同,SPAD值也不同;而对于同一植株的SPAD值则基本上遵循从低端向顶端减小的趋势。结合养蚕实际,对春、秋两季荷叶白品种确定的叶位进行了跟踪测量。分析了SPAD值在养蚕期间随生长的变化趋势。
     (3)根据几何学原理,对试验选用ASD公司的便携式可见-近红外光谱仪进行了参数分析。根据仪器高度、保存次数和扫描次数三因素下蚕种吸光度值的统计分析,发现不同的仪器高度下蚕种的吸光度有极显著差异,而保存次数和扫描次数则差异不显著,根据试验要求,选用了扫描30次,保存3次的参数,对不同蚕品种进行了鉴别,通过偏最小二乘法模型参数比较,发现该光谱仪对蚕种鉴别的高度以8cm为佳,为今后试验的开展选取最佳测试高度奠定了基础。
     (4)对采集过近红外光谱数据的蚕种样本进行孵化率分析。经过统计分析发现,经过光谱扫描后的样本孵化率与未扫描过的样本孵化率无显著差异。初步证明了近红外光对蚕种的生命力无显著性破坏,为利用该技术在蚕业生产和检验上的实际应用奠定了基础。
     (5)采用可见-近红外光谱仪对冷藏浸酸种和越年种进行了产地和品种的鉴别。从偏最小二乘法的建模参数来看,对产地的鉴别,冷藏浸酸种的校正模型相关系数在0.946以上,预测模型相关系数在0.872以上,越年种校正模型相关系数都在0.949以上,预测模型的相关系数在0.941以上,说明采用光谱技术可以鉴别蚕种的产地。同样地,对品种鉴别,冷藏浸酸种的校正模型相关系数在0.964以上,预测模型相关系数在0.938以上,而越年种的校正模型相关系数在0.966以上,预测样本模型相关系数在0.968以上,说明采用光谱技术可以鉴别家蚕蚕种的品种。这项研究结果为今后正确、无损、快速地鉴别蚕种的真伪以及是否产自优质生产基地做了基础工作。
     (6)跟踪调查了主要蚕品种在出库前不同发育阶段的近红外光谱反射特性。通过数学模型可以较准确地鉴别蚕卵的不同胚胎发育阶段,将蚕卵肉眼观察无法得到的胚胎发育状况做了数字化信息转化。对催青后的蚕卵发育情况进行日光谱跟踪测量,发现在不同的胚胎发育期,其光谱的特性也不同,可以通过数学建模加以判断,为日后检测催青胚胎是否正常发育提供了一种新方法。
     (7)初步建立了蚕、桑相关特性近红外光谱管理系统。系统分网络版和单机版,方便了科研和生产单位、个人的查询和使用,有利于推动近红外光谱技术在蚕桑行业的应用。
Digital Agriculture has become one of the supporting technologies of agricultural information development strategy in the 21st century. From the realities of agricultural production, farm management and agricultural scientific study, it quantifies and digital all aspects of agriculture accurately. It can provide a basic working platform for over all agricultural scientific management and rational decision.Sericulture is one of the traditional advantage industries in China.With the development of the society,economy and scientific technology, sericulture faces with scientific,information technology, efficient challenges.Spectroscopy technology is an importantl tool for digital agriculture research. Recently abundant successful experience has been obtained by spectroscopy technology in some crops with their relevant characteristics,but the reports about silkworm egg or mulberry have not yet. Series of studies and explor in have been done about the application of spectroscopy technology in silkwowm eggs and mulberry leaves in this thesis,and laid a foundation for the arrival of digital sericulture.The main conclusions were as follows:
     (1)Based on the statistical analysis in the SPAD502 chlorophyll meter measurement results of different position and varieties of mulberry leaves,those two factors on the SPAD value demonstrated significant differences.
     (2) Relationship about the SPAD value and chlorophyll content for 9 varieties of mulberry leaves was established, while the regression models were given.The result showed that the correlation coefflation had great difference in the 9 varieties. SPAD 502chlorophyll meter was used in this research to analyze the variability of chlorophyll content during the growth stage of mulberry leaves.SPAD502 were studied using the same leaves,different branches of the same plant. Arc View 3.2 was used to show SPAD value of the distribution in leaves of 6 varieties image.The results indicated that in a same leaf measuring different position had different SPAD value.SPAD value of one mulberry leaf from Heyebai was tracked the in spring and autumn, while the same time for silkworm werw fed with mulberry leaves.The vaule had growth trend as time went by. For practical use, the SPAD value in different varieties of mulberry leaves should have some significance to correct the real value.
     (3) According to the geometry principle, ASD Vis/NIR spectroscope equipment was analysised for its parameter.The effects of the hight, the save number and the scanning number of the equipment to silkworm eggs calibration models were investigated.With the same area of measurement size situation, three different condition had carried on the statistical analysis by ABS.Different hight had the extremely remarkable difference,but the save number and the scan number then the difference was not remarkable.According to experimented request,30 scan numbers,save 3 times of parameters were selected and carried on to get the workable height.2 varieties of silkworm eggs were carried on the comparison to the establishment model.The result showed the height taken 8cm was the fittest parameter.
     (4) Analysis Near infrared spectra of the acquisition over the silkworm eggs for hatching ration. Statisccal analysis showed that Near infrared spectra of silkworm eggs hatchability had no significant difference with the one without reflacted.It proved that the Near infrared spectra of silkworm eggs vitality no significant damage.It laid the groundwork for the use of the technology in sericulture production and testing of practical application.
     (5)Using Vis/NIR spectrometer for refrigerated silkworm eggs and Hibernated silkworm eggs to identify the place and varieties.Both of the two type of silkworm eggs had a high correlation coefficient by PLS calibration model of NIR spectra t(R=0.946,R=0.949),and the correlation coefficient of the prediction was also high(R=0.872,R=0.941).The Vis/NIR spectroscopy could identify the location of silkworm. Similarly, the identification of varieties,refrigerated types of correlation coefficientswas more than 0.964 and the predict coefficient was above 0.938.The spring-produced silkworm eggs for next spring rearing correction coefficient was over 0.966 and the forecast sample correlation coefficients was more than 0.968.It proved that Vis/NIR spectroscopy can be used to identify the species of silkworm.The result could correctly identify the authenticity of the silkworm eggs.It also could work on during the quality production for the production of silkworm nondestructive rapid identifications.
     (6) The NIRS reflectance property of the different development stages for the eggs of main hybrids before taken out the refrigerator was followed and investigated. The different embryonic development of the eggs could accurately be identified by means of the mathematical models.The embryonic development, which the naked eye could not be found, was made into the digital information transformation.The egg development after the incubation was determined with the daily tracking spectrum, from which it was found different spectral characteristics in different embryonic development stages,which could be determined by setting up the mathematical model,that gave a new technique for the inspection of the embryonic development during the incubation in the future.
     (7)The management system of silkworm eggs and mulberry correlation NIR was established. Network version and the PC version of the system could fit for the research and production units,personal inquiries.It will promote the Near Infrared Spectroscopy technology in the development of sericulture.
引文
1.曹干.现代近红外光谱分析技术在农业研究中的应用[J].广东农业科学,2004(B12):26-31
    2.褚小立,袁洪福,陆婉珍.近年来我国近红外光谱分析技术的研究与应用进展[J].分析仪器2006,2:1-10
    3.褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展,2004,16(4):528-542
    4.蔡鑫茹,刘广新,焦仁海.近红外光谱仪测定玉米子粒淀粉含量的研究[J].吉林农业科学,2006,31(6):10-11
    5.丁丽敏,计成.近红外光谱技术快速测定棉籽粕,菜籽粕真可利用氨基酸含量的研究[J].动物营养学报,2000,12(1):21-25
    6.范世福.光学分析仪器技术的若干新进展[J].分析仪器,1994(2):1-5
    7.付三玲,张伏,李建昌,等.几种物理技术在农业中的应用及展望[J].农机化研究,2006(11):36-38
    8.国家商务部,农业部.全国桑蚕种桑蚕茧桑蚕丝生产指导性计划的通知[Z].北京:2007
    9.高文淑,景茂,严衍禄.付里叶变换近红外漫反射光谱法测定谷子、玉米中多种氨基酸含量[J].北京农业大学学报,1990,16(增刊):88-93
    10.黄昊,曾德森.常见光学分析方法在医学分析仪器中的应用[J].医疗卫生装备,2004,25(1):25-26,29
    11.黄君霆,朱万民,夏建国,等.中国蚕丝大全[M].四川科学技术出版社,成都,1996:1-2
    12.胡满江.石化科学仪器仪表的发展与应用[J].石油仪器,2001,15(4):39-42
    13.何勇.精细农业[M].浙江大学出版社,杭州,2003:1-11
    14.何勇,李晓丽,邵咏妮.基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究[J].光谱学与光谱分析,2006,26(5):850-853
    15.何勇,李晓丽.用近红外光谱鉴别杨梅品种的研究[J].红外与毫米波学报,2006,25(3):192-194,212
    16.金同铭,潘沈元.蚕茧性别近红外光谱(NIRs)的模式识别[J].分析测试学报,1997,16(1):32-37
    17.景茂,严衍禄,刘广田.付里叶变换近红外漫反射光谱法测定小麦单籽粒中蛋白质含量[J].光谱学与光谱分析,1991,11(3):20-22
    18.孔源,韩鲁佳,贾贵儒,等.近红外技术快速测定肉鸡粪便主要肥料成分含量的研究[J].农业工程学报,2004,20(6):251-254
    19.李宏,莫放,JerryStuth,等.应用绵羊粪便的近红外光谱方程评定其日粮的可消化有机物[J].中国畜牧杂志,2004,40(6):22-24
    20.刘建学,吴守一.近红外光谱法快速检测大米蛋白质含量[J].农业机械学报,2001,32(3):68-70
    21.李君霞,张洪亮,严衍禄,等.水稻蛋白质近红外定量模型的创建及在育种中的应用[J].中国农业科学,2006,39(4):836-841
    22.刘景艳,鲍峰伟.近红外光谱分析技术在食品工业中的应用[J].中国食品添加剂,2006,10:172-175
    23.李静雅,索雪松,张志鹏,等.基于WEB的小麦玉米生产管理专家系统[J].农机化研究,2005(6):128-131
    24.刘荔荔,李力,邢旺兴,等.不同种丹参药材的近红外漫反射光谱模式识别法鉴别[J].2002,2(1):23-25
    25.刘荔荔,相秉仁,盛龙生,等.近红外漫反射光谱可视化褶合指纹谱对中药材的定性鉴别[J].中国药科大学学报,2003,34(2):105-108
    26.李民赞.光谱分析技术及其应用[M].北京:科学技术出版社,2006
    27.李庆波,汪燕,徐可欣,等.牛奶主要成分含量近红外光谱快速测量法[J].食品科学,2002,23(6):121-124
    28.刘炜,俞湘麟,孙东东,等.立叶变换近红外光谱法快速检测鲜猪肉中肌内脂肪、蛋白质和水分含量[J].养猪,2005,3:47-50
    29.陆婉珍.现代近红外光谱分析技术[M].北京:中国石化出版社,2007
    30.陆婉珍,袁洪福,徐广通,等.现代近红外光谱分析技术[M].北京:中国石化出版社,2000(1)
    31.李晓丽,胡兴越,何勇.基于主成分和多类判别分析的可见-红外光谱水蜜桃品种鉴别新方法[J].红外与毫米波学报,2006,25(6):417-420
    32.刘小敏,杨林,陈漩,等.NIRS501型近红外分析仪在鱼粉质检中的应用[J].粮食与饲料工业,1996,9:23-27
    33.刘杏梅,张蔚文.中尺度上水稻田质量与精确农业[J].浙江大学学报:农业与生命科学版,2005,31(6):745-749.
    34.梁晓艳,吉海彦.近红外光谱技术在农作物品质分析方面的应用[J].中国农学通报,2006,22(1):366-371
    35.刘燕德,应义斌,傅霞萍.近红外漫反射用于检测苹果糖度及有效酸度的研究[J].光谱学与光谱分析,2005,25(11):1793-1796
    36.卢艳丽,李少昆,王纪华,等.冬小麦不同株型品种光谱响应及株型识别方法研究[J].作物学报,2005,31(10):1333-1339
    37.潘沈元.蚕茧近红外反射(NIR)光谱的模式识别:Ⅱ.光谱识别中特征值选取方法的探讨[J].生物物理学报,1998,14(2):252-256
    38.潘沈元,陶鸣.雌雄蚕蛹近红外反射光谱的差异及其模式识别[J].昆虫学报,1996,39(4):360-365
    39.潘沈元,金同铭.蚕茧近红外反射(NIR)光谱的模式识别:Ⅰ.对雌雄鲜茧,死笼茧的非破坏性识别[J].生物物理学报,1995,11(1):53-59
    40.乔欣,马旭,梁留锁.高光谱技术在农作物营养信息诊断中的应用[J].农机化研究,2005(6):195-197
    41.史春香,杨悦武,郭治昕,等.近红外技术鉴别黄芪产地[J].天津药学,2006,18(1):19-21.
    42.宋海燕.基于光谱技术的土壤、作物信息获取及其相互关系的研究[D].浙江大学,2006
    43.沈漪,潘颖,刘全,等.近红外漫反射光谱法对阿莫西林胶囊的定性及定量分析[J].药物分析杂志,2005,25(4):385-389
    44.田高友,褚小立,袁洪福,等.近红外光谱仪器主要技术指标与评价方法概述[J].现代科学仪器,2005(4):17-20
    45.吴建国,石春海,张小明,等.用近红外反射光谱法分析稻米3种必需氨基酸含量的研究[J].作物学报,2003,29(5):688-692
    46.魏良明,严衍禄,戴景瑞.近红外反射光谱测定玉米完整籽粒蛋白质和淀粉含量的研究[J].中国农业科学,2004,37(5):630-633
    47.魏少华,杨莉,吴建国,等.近红外反射光谱(NIRS)技术分析奶粉品质的研究[J].乳业科学与技术,2003,2:57-61
    48.王思宏,尹起范,范艳玲,等.长白山地区几种红景天品种的傅里叶变换红外光谱法鉴别研究[J].光谱学与光谱分析,2004,24(8):957-959
    49.袁洪福,陆婉珍.现代光谱分析中常用的化学计量学方法[J].现代科学仪器,1998(5):6-9
    50.袁洪福,陆婉珍.近红外光谱分析技术正在快速进入石油化工领域[J].石油炼制与化工,1998,29(9):47-50
    51.俞海红,何勇.数字农业及其发展现状[J].农机化研究,2006(2):14-15,19
    52.应义斌.农产品无损检测技术[M].北京:化工出版社,2005
    53.严衍禄,赵龙莲.现代近红外光谱分析的信息处理技术[J].光谱与光谱分析:2000,(20),6:777-780
    54.严衍禄,赵龙莲,韩东海,等.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005
    55.朱尔一,杨芃原.化学计量学技术及应用[M].北京:科学出版社,2001
    56.中国丝绸协会.中国统计年鉴(2000~2005年)[M].北京:丝绸杂志社,2000~2005
    57.张瑞美,彭世彰,徐俊增.光谱技术在农业领域的应用与展望[J].节水灌溉,2006(5):1-5
    58.赵雅欣,王红英.近红外光谱分析技术在饲料工业中的应用进展[J].饲料工业:2005,(26),21:37-41
    59.Ben-Dor E.,Inbar Y.,Chen Y.The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process[J].Remote Sensing of Environment,1997,61(1):1-15
    60.Cleve E.,Bach E.,Schollmeyer E.Using chemometric methods and NIR spectrophotometry in the textile industry [J].Analytica Chimica Acta,2000,420(2):163-167
    61.Cozzolino D., Murray I.Identification of animal meat muscles by visible and near infrared reflectance spectroscopy [J].Lebensmittel-Wissenschaft und-Technologie,2004, 37(4):447-452
    62.Delwiche S.R.Classification of scab-and other mold-damaged wheat kernels by near-infrared reflectance spectroscopy [J].Transctions of the ASAE,2003,46 (3): 731-738
    63.Delwiche S.R.Protein Content of Single Kernels of Wheat by Near-Infrared Reflectance Spectroscopy [J].Journal of Cereal Science,1998,27(3):241-254
    64.Delwiche S.R.,Graybosch R.A.Examination of spectral pretreatments for partial least-squares calibrations for chemical and physical properties of wheat [J].Applied Spectroscopy,2003,57(12):1517-1527
    65.Delwiche S.R.,Graybosch R.A.Identification of Waxy Wheat by Near-infrared Reflectance Spectroscopy [J]Journal of Cereal Science,2002,35(1):29-38
    66.Delwiche S.R.,Graybosch R.A.,Hansen L.E.,et al.Single kernel near-infrared analysis of tetraploid (Durum) wheat for classification of the waxy condition [J].Cereal Chemistry, 2006,83(3):287-292
    67.Delwiche S.R.,Graybosch R.A.,Peterson CJ.Predicting protein composition, biochemical properties, and dough-handling properties of hard red winter wheat flour by near-infrared reflectance [J].Cereal Chemistry,1998,75(4):412-416
    68.Delwiche S.R.,Hruschka W.R.Protein content of bulk wheat from near-infrared reflectance of individual kernels [J].Cereal Chemistry,2000,77(1):86-88
    69.Delwiche S.R.,Reeves J.B.The effect of spectral pre-treatments on the partial least squares modelling of agricultural products [J].Journal of Near Infrared Spectroscopy,
    2004,12(3):177-182
    70.Esteban D.I.,Gonzalez-Saiz J.M.,Pizarro C. An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS [J].Analytica Chimica Acta,2004,514(1):57-67
    71. Esteban D.I.,Gonzalez-Saiz J.M.,Saenz-Gonzalez C. et al. Coffee varietal differentiation based on near infrared spectroscopy [J].Talanta,2007,71(1,15):221-229
    72. Herschel W. Experiments on the Refrangibility of the Invisible Rays of the Sun [J].Philoso phical Transactions of the Royal Society of London,1800,90:225
    73.He Y.,Li X.L.,Deng X.F.Discrimination of Varieties of Tea Using Near Infrared Spectroscopy by Principal Component Analysis and BP Model[J]Journal of Food Engineering,2007,79 (4):1238-1242
    74.He Y.,Li X.L.Shao Y.N.Quantitative analysis of the varieties of apple using near infrared spectroscopy by principal component analysis and BP model[J].LNCS, 2005,3809:1053-1056
    75.Kwon Y K, Cho R K.Identification of rice variety using near infrared spectroscopy [J]. Journal of Near Infrared Spectroscope,1998,6:67-73
    76.Kallenbach R L, Roberts C A, Teuber L R, et al. Estimation of fall dormancy in alfalfa by near infrared reflectance spectroscopy [J].Crop Science,2001,41(5):774-777
    77. Lachman J.,Kolihova'D.,Miholova'D.,et al.Analysis of minority honey components: Possible use for the evaluation of honey quality [J].Food Chemistry,2007,101(3): 973-979
    78. Liu, Lyon B.G.,Windham W.R. et al. Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study [J].Meat Science,2003,65(3):1107-1115
    79. Luypaert J.,Massart D.L.,Vander Heyden YNear-infrared spectroscopy applications in pharmaceutical analysis [J].Talanta, In Press,2006:Available online
    80.Lehr H-P, Wickramasinghe Y. New prototype NIRS to investigate multi-regional cerebral blood and tissue oxygenation and homodynamic. Medical & Biological Engineering & Computing,2000,38:281-286
    81.McClure W.F.204 years of near infrared technology:1800-2003 [J].Journal of Near Infrared Spectroscopy.2003,11(6):487-518
    82. Millmier A.J.,Lorimor C,Hurburgh J.,et al.Near-infrared sensing ofmanure nutrients [J].Trans ASAE,2000,43:903-908
    83.Blanco M., Villarroya I. NIR spectroscopy:a rapid-response analytical tool [J].TrAC
    Trends in Analytical Chemistry,2002,21:240-250
    84.Narayanaswamy R.Current developments in optical biochemical sensors [J].Biosensors and Bioelectronics,1991,6(6):467-475
    85.Norris K.H.,Barnes R.F.,Moore J.E.,et al. Predicting forage quality by infrared reflectance spectroscopy [J].J.Anim.Sci.1976,43:889-897
    86.Nguyen Q.D.,Michel J.The Raman laser fiber optics (RLFO) method and its applications [J].Sensors and Actuators B:Chemical,1993:11(1-3):147-160
    87. Norris K.H, Roman J.D.Qualitative Applications of Near-infrared Reflectance Spectroscopy [J].Agricultural Engineering.1962,43:154
    88.Osborne B.G, Mertens B.,Thompson M, ET al.The authentication of Basmati rice using near infrared spectroscopy [J]. Journal of Near Infrared Spectroscope,1993,1: 77-83
    89. Stchur P.,Yang K.X.,Hou X.D.,et al.Laser excited atomic fluorescence spectrometry-a review [J].Spectrochimica Acta Part B:Atomic Spectroscopy,2001,56(9):1565-1592
    90.Romdhane K.,Mouazena A.M.,Herman Ramona.et al.Feasibility study of discriminating the manufacturing process and sampling zone in ripened soft cheeses using attenuated total reflectance MIR and fiber optic diffuse reflectance VIS-NIR spectroscopy [J].Food Research International,2006,39(5),:588-597
    91.Karoui R.,Baerdemaeker J.D.A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products [J].Food Chemistry,2007,102(3):621-640
    92. Reeves J. B.,Blosser T.H.Near Infrared Reflectance Spectroscopy for analyzing undried silage [J].J.of Dairy Sei.1989,72:79-88
    93.Schmoldt D. L.Precision Agriculture and information technology [J].Computers and Electronics in Agriculture,2001,30(1-3):5-7
    94.Sdndor T.,Arpad I.,T6th,et al.Multivariate classification of different soybean varieties [J].Journal of Near Infrared Spectroscope,1998,6:183-187
    95.Schrader B.,Schulz H.,Andreev G N.,et al.Non-destructive NIR-FT-Raman spectroscopy of plant and animal tissues, of food and works of art [J].Talanta,2000, 53(1,2):35-45
    96. Tzanavaras P.D.,Themelis D.GReview of recent applications of flow injection spectrophotometry to pharmaceutical analysis [J].Analytica Chimica Acta,2007, 588(1,4):1-9
    97.Velasco L.,Moilers C.Estimation of.seed weight,oil content and fatty acid composition
    in intact single seeds of rapeseed (Brassica napus L.)by near-infrared reflectance spectroscopy[J].Euphytica,1999,106(1):79-85
    98.Velasco L, Moilers C.Nondestructive assessment of protein content in single.seeds of rapeseed(Brassica napus L.)by near infrared reflectance spectroscopy [J].Euphytica, 2002,123(1):89-93
    99.Velasco L, Gruneberg WJ.Analysis of dry matter and protein contents in fresh yam bean tubers by near-infrared reflectance spectroscopy[J].Communications in soil science adn plant analysis,1999,30(13-14):1797-1805
    100. Velasco L,Perez-Vich B,Fernandez-Martinez JM.Nondestmctive screening for oleic and linoleic acid in singlesunflower achenes by near-infrared reflectance spectroscopy [J].Crop Science,1999,39(1):219-222
    101.Williams P.C. Near-infrared reflectance analysis:food industry applications [J].Trends in Food Science & Technology,1990(1):44-48
    102. Zsolt S.,Tamas D.,Gyorgy D.B.Distinguishing melon genotypes using NIR spectroscopy [J].Chemometrics and intelligent Laboratory System,2004,72(2): 195-203
    103.Wen Z.Q.,Tao Y. Fuzzy-based determination of model and parameters of dual-wavelength vision system for on-line apple sorting [J].Opt Eng.,1998, 37(1):293-299
    1.陈防,鲁剑巍.SPAD-502叶绿素仪在作物营养快速诊断上的应用初探[J].湖北农业科学,1996(2):31-34
    2.冯家新.浙江省1949-2000年的蚕种及蚕品种概况[J].蚕桑通报,2005,36(1):1-5
    3.刘德金.农业试验设计与分析[M].北京:中国农业科学技术出版社,2005
    4.李民赞.光谱分析技术及其应用[M].北京:科学技术出版社,2006
    5.宋海燕.基于光谱技术的土壤、作物信息获取及其相互关系的研究[D]浙江大学,2006
    6.唐启义.通用统计软件DPS研制与应用[J].科学中国人,2004(9):56-56
    7.唐启义,冯明光.实用统计分析及其DPS数据处理系统[M].北京:科学技术出版社,2002
    8.王康,沈荣开,唐友生.用叶绿素测值(SPAD)评估夏玉米氮素状况的实验研究[J].灌溉排水,2002,21(4):1-3,12
    9.王正林,刘明.精通MATLAB7[M].北京:电子工业出版社,2007
    10. http://www.camo.com
    11.中国农业科学院蚕业研究所.中国桑树品种志[M],北京:农业出版社,1993
    1.陆婉珍,袁洪福,徐广通,等.现代近红外光谱分析技术[M].北京:中国石化出版社,2000
    2.刘燕德.水果糖度和酸度的近红外光谱检测研究[D].浙江大学,2006
    3.李民赞.光谱分析技术及其应用[M].北京:科学技术出版社,2006
    4.贾良良,陈新平等.作物氮营养诊断的无损测试技术[J].世界农业,2001(6):36-37
    5.张爱梅,张登武.近红外线扫描诊断1098例乳腺疾病的分析[J].中国疗养医学,2003,12(1):45-46
    6. Kawamura Shuso, Tsukahara Maki, Natsuga Motoyasu,et al.On-line Near Infrared Spectroscopic Sensing Technique for Assessing Milk Quality during Milking[J]. ASAE Annual Meeting:2003,NO.033026
    1.陈防,鲁剑巍,SPAD-502叶绿素仪在作物营养快速诊断上的应用初探[J].湖北农业科学,1996,(2):31-34
    2.范银贵.空间插值方法在绘制降水量等值线中的应用[J].水利水电科技进展,2002,22(3):48-50
    3.雷泽湘,艾天成等.草莓叶片叶绿素含量、含氮量与SPAD值间的关系[J].湖北农学院学报,2001,21(2):138-140.
    4.铃木诚,王林用叶绿素仪对桑树进行简易营养诊断[J].国外农学:蚕业,1994(1):50-53
    5.林水中,王明霞,束秀玉,等.桑树缺素症症状调查与分析[J].江苏蚕业,2004,26(4):54-55.
    6.王秀珍,黄敬峰,李云梅,等.高光谱数据与水稻农学参数之间的相关分析[J].浙江大学学报(农业与生命科学版),2002,(3):13-16
    7.王娟,韩登武,任岗,等.SPAD值与棉花叶绿素和含氮量关系的研究[J].新疆农业科学,2006,43(3):167-170
    8.王康,沈荣开,等用叶绿素测值(SPAD)评估夏玉米氮素状况的实验研究[J].灌溉排水,2002,21(4):1-3,12.
    9.余荣峰,方树友,方正,等.浅析优质蚕茧基地建设的制约因素及对策[J].中国蚕业,2005,26(4):9-11
    10.杨贵明,薛秋生等.不同桑树品种各器官氮磷含量及其累积量研究[J].湖北农业科学,2001(5):63-65
    11.中国农业科学研究院蚕业研究所.中国桑树栽培学[M]上海:上海科学技术出版社,1985
    12.张文安.SPAD-501型叶绿素仪在测定水稻叶绿素含量中的应用[J].贵州农业科学,1991(4):37-40.
    13.赵春江.数字农业信息标准研究-作物卷[M].北京:中国农业出版社,2005:409-411
    14.朱求安,张万昌,余钧辉.基于GIS的空间插值方法研究[J].江西师范大学学报:自然科学版,2004,28(2):183-188
    15.Abrol Y P., Chatterjee S.R.,Kumar P.A., et al, Improvement in nitrogen use efficiency: physiological and molecular approaches [J].Current. Science,1999,76:1357-1364
    16. Boochs R,Kupfer G,D.,Shape of the red edge as vitality indicator for plants [J].Internal Journal of Remote Sensing,1990.11(12):1741-1753
    17. Bernt O. H.,Knut A.S.Effect of Irradiance on Chlorophyll Estimation with the Minolta SPAD-502 Leaf Chlorophyll Meter. Annals of Botany,1998,82 (3):389-392
    18. Carlos C,Lianne M D.,Pierre D.Inter-relationships of applied nitrogen, SPAD, and yield of leafy and non-leafy maize genotypes[J].Journal of plant nutrition,2001,24 (8): 1173-1194.
    19. Caruso C, Quarta F.Interpolation methods comparison [J].Computers & Mathematics with Applications,1998,35(12):109-126
    20. Carreres R, Sendra J.,Ballesteros R.Effects of preflood nitrogen rate and midseason nitrogen timing on flooded rice [J].Journal of Agricultural science,2000,134:379-390.
    21. Filella I, Penuelas J. The red edge position and shape as indicators of plant chlorophyll content, bio-mass and hydric sta-tus[J].International Jourrnal of Remote Sensing,1994,15:1459-1 470.
    22. Minolta co. Ltd. Chlorophyll SPAD-502 instruction manual [M],Tokyo,radiometric instruments operations,1998
    23.Minolta co. Ltd. Chlorophyll SPAD-502 instruction manual [M].Osaka, Japan:Radiometric Instruments Operations,1989:17-21
    24. Van den Berg A. K.,Perkins T. D. Evaluation of a portable chlorophyll meter to estimate chlorophyll and nitrogen contents in sugar maple (Acer saccharum Marsh.)leaves.Forest Ecology and Management [J],2004,200(1-3):113-117
    1.冯家新.浙江蚕品种[M].杭州:浙江科学技术出版社,1993:11-14
    2.王连珍,郭军.DNA指纹分析在蚕业上的应用[J].辽宁丝绸,2003(3):31-32
    3.中国农业科学院蚕业研究所.中国养蚕学[M].上海:上海科学技术出版社,1990
    4.何家禄,易文仲.家蚕不同品种血液酯酶图谱的研究[J].蚕业科学,1983,9(2):103-106;
    5.罗英,刘春,陈萍,等.家蚕SOD的品种间差异研究[J].蚕学通讯,2002,22(2):1-7
    6.程道军,周泽扬,鲁成,等.RFLP技术构建家蚕现行品种DNA指纹图谱的研究[J].西南农业大学学报,2000,22(6):484-486
    7.夏庆友,周泽扬,鲁成,等.家蚕不同地理品种分子系统学研究[J].昆虫学报,1998,41(1):32-40
    8.张金卫,钟伯雄,丁农,等.应用AFLP分子标记对6个家蚕品种的鉴定[J].蚕业科学,2004,30(2):137-142
    9. Li X.L.,He Y., Wu C.Q.,et al.Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy[J].Journal of Food Engineering,2007,:82(3):316-323
    10. Fernandez-Cabands V.M., Garrido-Varo A., Garcia O.J.,et al.Optimisation of the spectral pre-treatments used for Iberian pig fat NIR calibrations[J].Chemometrics and Intelligent Laboratory Systems,2007,87,(1):130-138
    1.李军会,赵龙莲.农业近红外分析技术软件及网络系统的研制[J].现代仪器,2000(6):11-13
    2.袁鹏飞.SQL Server 2000中文版设计实务[M].北京:人民邮电出版社,2001
    3.李军会,赵龙莲,劳彩莲,等.用近红外光谱构建现代农产品品质分析技术[J].现代科学仪器,2005(1):17-19
    4.赵斌,张滨义,董清波.ASP.NET从入门到精通[M].北京:人民邮电出版社,2002
    5.曾静,李陶深.基于ASP.NET技术的信息库管理系统的设计与实现[J].广西科学院学报,2006,22(4):314-316
    6.张江霞,宁成军.基于VB6.0和SQLServer2000的高校学生信息管理系统设计与实现[J].机械管理开发,2006(6):106-107
    7. Chrles R Hurburgh. Performance of a network of whole grain NIRS Units for Coarse Grains[C].Proc of the 9th International NIRS Conf,2000, P16 AGRO
    8.Peter Tillman,Christian Paul. Networking of NIRS instruments for rapeseed analysis using different procedures[C].Proc of the 9th International NIRS Conf,2000, P38 AGRO
    9.黄凌霞,黄敏,金佩华,等.基于光谱技术和模式快速鉴别家蚕品种的初探[J].蚕业科学,2006,32(3):436-438,447