基于荧光法海藻种类识别的理论及应用研究
|
摘要
海水中的浮游植物是海洋生态系统中最主要的初级生产者和能量的主
要转换者，海藻是主要的海洋浮游植物，对海洋中海藻种类进行识别，可
以估测海区内生态系统的群落结构和能量分布状态，实现海洋污染的监测
和治理。
本文在对荧光测量机理和人工神经网络理论进行分析的基础上，提出
了将荧光法与 BP网络相结合的单一海藻定性识别方法,并应用该方法进行
三种不同门类海藻的识别和检测,设计了光纤荧光海藻自动识别系统的总
体方案。主要研究内容如下：
(1) 从荧光测量的基本原理出发，研究了海藻荧光检测机理，论证了
采用荧光法实现海藻监测的可行性，为不同门类海藻的在线监测识别奠定
了理论基础。
(2) 研究了不同门类、不同浓度海藻溶液及其所含特征色素的吸收光
谱、激发光谱、荧光光谱特性，提取了海藻种类识别的特征参量，并确定
了最佳激发波长和荧光检测波长，为光纤荧光测量系统的设计提供了实验
依据。
(3) 设计了光纤荧光海藻自动识别系统的总体方案。包括消除光源不
稳定性影响的双光路双通道测量技术；高效收集荧光，避免杂散光的光纤
探头；实现背景噪声中微弱荧光信号提取的低噪声前置放大、时域平均检
测技术。
(4) 基于海藻的荧光光谱特性，设计了用于单一海藻识别的 BP 网络模
型，并将其应用于三种不同门类单一海藻的识别实验，验证了将荧光法和
人工神经网络理论相结合应用于海藻识别的可行性。
本研究将荧光法和人工神经网络模式识别理论相结合应用于光纤海藻
自动识别系统，能对三种不同门类的海藻进行自动识别，并具有数据实时
采集、处理及结果显示等功能，为海藻状态监测提供了一种新的技术手段。
Phytoplankton in the sea is the main primary producer and energy
transducer in ocean ecological system. By the recognition of algae that is the
main ocean phytoplankton, we can estimate community structure and energy
distribution in the sea area and realize monitoring and governing ocean
pollution.
Based on the analysis and synthesis of principle of fluorescence
measurement and theory of artificial neural network, this paper proposes a
method that the single algae could be recognized based on fluorescence
method and BP network, applies it to recognition of three kinds of single algae,
and designs systematic scheme which is so called optical fiber and
fluorescence system for automatic algae recognition. The main research
contents are as followings:
(1) Based on the fundamental theory of fluorescence measurement, the
principle of the algae fluorescence detection is studied and the feasibility of
fluorescence method is demonstrated in order to provide foundation for the
reasonable online recognition of different kind of algae.
(2) Not only are the absorption spectra, excitation spectra and fluorescence
emission spectra properties are analyzed by the experiments, but also the
characteristic parameters are picked-up for algae recognition and best
excitation wavelength and fluorescence detection wavelength are selected.
(3) The total scheme of optical fiber and fluorescence system for automatic
algae recognition are based on PC. This system includes the double-channels
measurement technology for weakening the influence of instability
lamp-house, the probe for effective collecting fluorescence without scattered
light, low noise preamplifier average detection technique in time field to pick
up the weak fluorescence from the measured signals with noise.
(4) Based on fluorescence spectra of algae, BP network model for
recognition of three kinds of algaes is designed, recognition experiment of
three kinds of single algae has been carried with BP network, which
demonstrates the feasibility fluorescence method and artificial neural network
II
pattern recognition theory algae recognition.
This research applies fluorescence method and artificial neural network
pattern recognition theory to optical fiber algae monitor system that can
realize recognition of three kinds of algaes. This system has real time data
collection, data processing and results displaying functions. A novel technical
means for algae state monitoring is provides by the paper.
要转换者，海藻是主要的海洋浮游植物，对海洋中海藻种类进行识别，可
以估测海区内生态系统的群落结构和能量分布状态，实现海洋污染的监测
和治理。
本文在对荧光测量机理和人工神经网络理论进行分析的基础上，提出
了将荧光法与 BP网络相结合的单一海藻定性识别方法,并应用该方法进行
三种不同门类海藻的识别和检测,设计了光纤荧光海藻自动识别系统的总
体方案。主要研究内容如下：
(1) 从荧光测量的基本原理出发，研究了海藻荧光检测机理，论证了
采用荧光法实现海藻监测的可行性，为不同门类海藻的在线监测识别奠定
了理论基础。
(2) 研究了不同门类、不同浓度海藻溶液及其所含特征色素的吸收光
谱、激发光谱、荧光光谱特性，提取了海藻种类识别的特征参量，并确定
了最佳激发波长和荧光检测波长，为光纤荧光测量系统的设计提供了实验
依据。
(3) 设计了光纤荧光海藻自动识别系统的总体方案。包括消除光源不
稳定性影响的双光路双通道测量技术；高效收集荧光，避免杂散光的光纤
探头；实现背景噪声中微弱荧光信号提取的低噪声前置放大、时域平均检
测技术。
(4) 基于海藻的荧光光谱特性，设计了用于单一海藻识别的 BP 网络模
型，并将其应用于三种不同门类单一海藻的识别实验，验证了将荧光法和
人工神经网络理论相结合应用于海藻识别的可行性。
本研究将荧光法和人工神经网络模式识别理论相结合应用于光纤海藻
自动识别系统，能对三种不同门类的海藻进行自动识别，并具有数据实时
采集、处理及结果显示等功能，为海藻状态监测提供了一种新的技术手段。
Phytoplankton in the sea is the main primary producer and energy
transducer in ocean ecological system. By the recognition of algae that is the
main ocean phytoplankton, we can estimate community structure and energy
distribution in the sea area and realize monitoring and governing ocean
pollution.
Based on the analysis and synthesis of principle of fluorescence
measurement and theory of artificial neural network, this paper proposes a
method that the single algae could be recognized based on fluorescence
method and BP network, applies it to recognition of three kinds of single algae,
and designs systematic scheme which is so called optical fiber and
fluorescence system for automatic algae recognition. The main research
contents are as followings:
(1) Based on the fundamental theory of fluorescence measurement, the
principle of the algae fluorescence detection is studied and the feasibility of
fluorescence method is demonstrated in order to provide foundation for the
reasonable online recognition of different kind of algae.
(2) Not only are the absorption spectra, excitation spectra and fluorescence
emission spectra properties are analyzed by the experiments, but also the
characteristic parameters are picked-up for algae recognition and best
excitation wavelength and fluorescence detection wavelength are selected.
(3) The total scheme of optical fiber and fluorescence system for automatic
algae recognition are based on PC. This system includes the double-channels
measurement technology for weakening the influence of instability
lamp-house, the probe for effective collecting fluorescence without scattered
light, low noise preamplifier average detection technique in time field to pick
up the weak fluorescence from the measured signals with noise.
(4) Based on fluorescence spectra of algae, BP network model for
recognition of three kinds of algaes is designed, recognition experiment of
three kinds of single algae has been carried with BP network, which
demonstrates the feasibility fluorescence method and artificial neural network
II
pattern recognition theory algae recognition.
This research applies fluorescence method and artificial neural network
pattern recognition theory to optical fiber algae monitor system that can
realize recognition of three kinds of algaes. This system has real time data
collection, data processing and results displaying functions. A novel technical
means for algae state monitoring is provides by the paper.
引文
1 C. M. Lalli, T. R. Parsons. Biological Oceanography: An Introduction. Pergamon
Press,1993:45-50
2 高洪峰,焦念志.通过藻类色素分析估测海洋浮游植物生物量和群落组成的研究进
展.海洋科学,1997,(3):51-53
3 王 世 忠 ,李 继 刚 ,高 志 ,等 .海 水 藻 类 和 悬 浮 物 浓 度 测 量 仪 应 用 研 究 .海 洋 技
术,2001,20(1):97-103
4 张滨,乔然.赤潮.海洋预报,1994,(3):31
5 王 文 翰 ,杨 坤.保 护 海 洋 生 态 促 进 海 洋 产 业 经 济 持 续 发 展 .东 北 财 经 大 学 学
报,2001,13 (1):23-24
6 夏 达 英 , 王 振 先 , 夏 敬 芳 , 等 . 海 水 叶 绿 素 a 现 场 测 量 仪 的 研 究 . 海 洋 与 湖
沼,1997,28(4):433-439
7 王 荣 , 鲁 北 纬 , 余 家 栋 . 海 水 叶 绿 素 a 含 量 的 连 续 走 航 测 定 . 海 洋 与 湖
沼,1996,27(3):296-301
8 C. J. Lorenzen. A Method for the Continuous Measurement of in vivo Chlorophyll-a
Concentration. Deep Sea Res,1966,(13):223-227
9 夏 达 英 , 王 振 先 , 张 士 魁 . 荧 光 技 术 在 海 洋 环 境 学 上 的 应 用 研 究 . 海 洋 学
报,1999,21(3):66-71
10 U. Schreiber. Chlorophll Fluorescence Yield Changes as a Tool in Plant Physiology.
Photosynthesis Res,1984,4:361-370
11 H. K. Lichtenthaler, U. Rinderle. The Role of Chlorophyll Fluorescence in the
Detection of Stress Condition in Plants. CRC Critical Reviews in Analytical
Chemistry,1988:29-85
12 W. Bilger, U. Schreiber, O. L. Lange. Chlorophyll Fluorescence as an Indicator of
Heat Induced Limitation of Photosynthesis. In Plant Response to stress, Spriger
Verglag, Berlin,1988:391-399
13 H. K. Hongsuk. New Algae Mapping Technique by the Use of an Airborne Laser
Fluorescence. Applied Optics,1973,12(7):1454-1459
14 C. S. Yentsch, C. M. Yentsch. Fluorescence Spectral Signatures: The Characterizati-
on of Phytoplankton Populations by Theuse of Excitation and Emission Spectra. J.
Mar. Res,1979,37:471-483
79
15 J. S. Cleveland, M. J. Perry. Quantum Yield, Relative Specific Absorption and Fluo-
rescence in Nitrogenlimited Chaetoceros Grasilis. Mar. Biol,1987,94:489-497
16 J. B. SooHoo, D. A. Kiefer, D. J. Collins et al. In vivo Fluorescence Excitation and
Absorption Spectra of Marine Phytoplankton: I. Taxonomic Characteristics and
Responses to Photoadaptation. J. Plankton. Res,1986,8:197-214
17 E. Sakeshaug, G. Johnsen, K. Andresen et al. Modeling of light-dependent Algal
Photosynthesis and Growth: Experiments with Barents Sea Diatoms Thalassiosira
Nordenskioedii and Chaetoceros Furcellatus. Deep Sea. Res,1991,38:415-430
18 C. S. Yentsch, D. A. Phinney. Spectral Fluorescence: an Ataxonomic Tool for Study-
ing the Structure of Phytoplankton Populations. J. Plankton. Res,1985,7:617-632
19 P. B. Oldham, E. J. Zillioux, I. M. Warner. Spectral ‘Fingerprinting’ of Phytoplankt-
on Populations by Two-dimensional Fluorescence and Fourier- transform-based
Pattern Recognition. J. mar. Res,1985,43:893-906.
20 T. J. Cowles, R. A. Desiderio, S. Neuer. In situ Characterization of Phytoplankton
from Vertical Profiles of Fluorescence Emission Spectra. Mar. Biol,1993,115:
217-222
21 L. Poryvkina, S. Babichenko, S. Kaitala et al. Spectral Fluorescence Signature in the
Characterization of Phytoplankton Community Composition. J. Plankton. Res,1994,
16:1315-1327
22 A. M. Chekalyuk, D. K. Bunin, A. V. Chechendaev et al. Lidar Mapping of chloroph-
yll-a and Organic Matter Distributions in the Russian Coastal zone of the Black Sea.
SPIE,1992,(1922):384-390
23 A. M. Chekalyuk, M. Yu. Gorbunov. Laser remote Sensing of Phytoplankton photos-
ynthetic Activity and Chlorophyll by Using a Shipboard Lidar System. SPIE,1992,
(1922):391-400
24 M. Yu. Gorbunov, A. M. Chekalyuk. Lidar in-situ Study of Sunlight Regulation of
Phytoplankton photosynthetic Activity and Chlorophyll Fluorescence. SPIE,1992,
(1922):421-427
25 B. Ruth. Monitoring the Herbicide Concentration in Ponds by Detecting the Chloro-
phyll Fluorescence of Algae with ms-Laser-Excitation. SPIE,1995,(2586):46-55
26 D. Mckee, A. Cunningham, K. Jones. An integrated submersible Fluorometer/Nephe-
lometer/Transmissometer: Design and Testing at Sea. Optics and Laser Technoloty,
1997,29(1):35-39
80
27 J. J. Cullen, A. M. ciotti, R. F. Davis. The Relationship between Near-surface Chlor-
ophyll and Solar-stimulated Fluorescence: Biological Effects. SPIE,1997,
(2963):272-277
28 K. Wild-Allen, P. Tett, D. Bowers. Observations of Diffuse Upwelling Irradiance and
Chlorophyll in Case I Waters near the Canary Islands(Spain). Optics and Laser
Technology,1997,29(1):3-8
29 M. Beutler, K. H. Wiltshire, B. Meyer et al. A fluorometric Method for the Different-
iation of Algal Populations in vivo and in situ. Photosynthesis Research,2002,72
(1):39-53
30 潘德炉.利用荧光高度遥感海洋中叶绿素 a 的浓度.海洋学报,1989,11(6):780-787
31 陈卫标,吴东,张亭禄,等.海洋表层叶绿素浓度的激光雷达测量方法和海上实验.海
洋与湖沼,1998,29(3):255-260
32 靳伟,廖延彪,张志鹏,等.导波光学传感器原理与技术.北京:科学出版社,1998:1
33 E. J. D’sa. Time Series Measurements of Chlorophyll Fluorescence in Oceanic Bott-
om Boundary Layer with Multisensor Fiber Optic Fluorometer. J. Atmos, Ocean,
Technol,1997,14(4):101-106
34 M. Lehaitre. Optical Fiber Spectrphotometry for in situ Seawater Biological and
Chemical Analysis. J. Mar. Environ. Eng,1997,4(1):89-97
35 P. R. Coulet. Luminescence-based Fiber Optic Probes. Sensors and Actuators,1993,
11(3):45-52
36 S. E. Lohrenz. Development of a multi-Sensor in situ Fiber Optic Fluorometer. Prog-
ress Report,1992,(1):101-106
37 张季熊,李郁章,王海波.荧光光纤传感器.仪表技术与传感器,1998,(7):6-9
38 孙即祥.现代模式识别.北京:国防科技大学出版社,2002:2-4
39 D. O. Hebb. The Organization of Behavior. New York: Wiley,1949:1-335
40 F. Rosenblatt. The Perceptron: A Probabilistic Model for Information Storage and
Organization in the Brain. Cornell Aeronautical Laboratory: Psychological
Review,1958,65(6):386-408
41 B. Widrow, M. E. Hoff. Adaptive Switching Circuits. New York: IRE WESCON
Convention Record,1960:96-104
42 M. Minsky, S. Papert. Perceptron: An Introduction to Computational Geometry.
Cambridge, MA: MIT Press,1969:1-98
81
43 J. L. McClelland, D. E. Rumelhart, the PDP Research Group. Parallel Distributed
Processing: Psychological and Biological Models. Cambridge, MA: MIT Press,1986:
1-109
44 R. Miikkulainen. Dyslexic and category-specific aphasic impairments in a self-orga-
nizing feature map models of the lexicon. Brain and Language,1997,59:334-366
45 S. Grossberg. The Complementary Brain: Unifying Brain Dynamics and Modularity.
Trends in Cognitive Science,2000,4(5):233-246
46 E. Oja, M. Kuusela. The ALSM algorithm-an improved subspace method of classif-
ication. Pattern Recognition,1983,16(4):421-427
47 L. Xu, A. Krzyzak, E. Oja. A Neural Net for Dual Subspace Pattern Recognition Me-
thods. International Journal of Neural Systems,1991,12(3):169-184
48 J. J. Hopfield. Neural Networks and Physical Systems with Emergent Collective
Computational Abilities. Proceedings of the National Academy of Sciences, USA,
1982,79:2554-2558
49 R. Hecht-Nielsen. Counterpropagation Networks. Applied Optics,1987,26:4979-4984
50 L. O. Chua, L. Yang. Cellular Neural Networks: Theory. IEEE Transactions on Circ-
uits and Systems,1988,35(10):1257-1272
51 L. O. Chua, L. Yang. Cellular Neural Networks: Applications. IEEE Transactions on
Circuits and Systems,1988,35(10):1273-1290
52 王永骥,涂健.神经元网络控制.北京:机械工业出版社,1998:6-7
53 A. Sharma, S. G. Schulman. Introduction to Fluorescence Spectroscopy. New York:
John Wiley and Sons, Inc,1999:22-23
54 C. L. Catharina. Fluorescence Quenching in Four Unicellar Algae with Diffenert
Light-harvesting and Xanthophylls-cycle Pigments. Photosynthesis Research,1998,
56:277-289
55 M. Balode. The Use of spectral Fluorescence Methods to Detect Changes in the
Phytoplankton Community. Hydrobiologia,1998,363:207-217
56 陈峰,姜锐.微藻生物技术.北京:中国轻工业出版社,1999:1-36
57 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Ch
lorophyll%20Analysis.pdf
58 http://www.biologie.uni-hamburg.de/ b-online/ge24/03.htm
59 温 少 红 , 徐 春 野 , 鞠 宝 , 等 . 紫 球 藻 藻 红 蛋 白 的 分 离 纯 化 及 光 谱 特 性 . 海 洋 通
82
报,2000,19(6):90-94
60 王勇,钱凯先,董强.高纯度藻蓝蛋白分离纯化及光谱特性研究.生物化学与生物物
理进展,1999,26(5):457-460
61 R. Aguirre-Gomez, S. R. Boxall, A. R. Weeks. Detecting photosynthetic Algal Pigm-
ents in natural Populations Using a high-spectral-resolution Spectroradiometer.
International Journal of Remote Sensing,2001,22(15):2867-2884
62 邱 丽 氚 , 凌 元 洁 , 谢 树 莲 . 大 螺 旋 藻 一 些 生 理 特 性 的 研 究 . 木 本 植 物 研
究,1998,18(3):356-360
63 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Ch
lorophyll%20Analysis.pdf
64 D. S. Knowles, S. H. Lieberman. Field Results from the SCAPS Laser-induced Fluo-
rescence (LIF) Sensor for in-situ Subsurface Detection of Petroleum Hydrocarb- ons.
SPIE,1995,(2504):297-307
65 杨祥林.光纤通信系统.北京:国防工业出版社,2000:34
66 陈根祥.光波技术基础.北京:中国铁道出版社,2000:280
67 叶嘉雄,常大定,陈汝钧.光电系统与信号处理.北京:科学出版社,1997:260
68 林坚,林永钟,谢树森.荧光分析用系列滤光片.光电子.激光,2000,11(1):57-61
69 ADVANTECH. Total Solution for PC-based Automation. China: ADVANTECH Corp,
2003:7-14-7-15
70 刘君华,贾惠芹,丁晖.虚拟仪器图形化编程语言 LabVIEW 教程.北京:电子工业出
版社,2001:1-5
71 G. W. Gohson. LabVIEW Graphic Programming. USA: MC Graw-Hill,1998:232~133
72 张广军,李鑫,魏振忠.基于 RBF 神经网络的结构光三维视觉检测方法研究.计量学
报,2002,23(4):251-255
73 席道瑛,张涛.BP 人工神经网络模型在测井资料岩性自动识别中的应用.物探化计
算技术,1995,17(1):42-48
74 楼顺天,施阳.基于 MATLAB 的系统分析与设计.西安:西安电子科技大学出版
社,1998:13-14
75 J. N. Hwang, S. Y. Kung, M. Niranjan et al. The Past, Present, and Future of Neural
Networks for Signal Processing, IEEE Signal Process. Mag,1997(14):28-48
76 汪晓东,万旭,赵鹏程等.基于神经网络的传感器静态误差综合修正法.仪器仪表学
Press,1993:45-50
2 高洪峰,焦念志.通过藻类色素分析估测海洋浮游植物生物量和群落组成的研究进
展.海洋科学,1997,(3):51-53
3 王 世 忠 ,李 继 刚 ,高 志 ,等 .海 水 藻 类 和 悬 浮 物 浓 度 测 量 仪 应 用 研 究 .海 洋 技
术,2001,20(1):97-103
4 张滨,乔然.赤潮.海洋预报,1994,(3):31
5 王 文 翰 ,杨 坤.保 护 海 洋 生 态 促 进 海 洋 产 业 经 济 持 续 发 展 .东 北 财 经 大 学 学
报,2001,13 (1):23-24
6 夏 达 英 , 王 振 先 , 夏 敬 芳 , 等 . 海 水 叶 绿 素 a 现 场 测 量 仪 的 研 究 . 海 洋 与 湖
沼,1997,28(4):433-439
7 王 荣 , 鲁 北 纬 , 余 家 栋 . 海 水 叶 绿 素 a 含 量 的 连 续 走 航 测 定 . 海 洋 与 湖
沼,1996,27(3):296-301
8 C. J. Lorenzen. A Method for the Continuous Measurement of in vivo Chlorophyll-a
Concentration. Deep Sea Res,1966,(13):223-227
9 夏 达 英 , 王 振 先 , 张 士 魁 . 荧 光 技 术 在 海 洋 环 境 学 上 的 应 用 研 究 . 海 洋 学
报,1999,21(3):66-71
10 U. Schreiber. Chlorophll Fluorescence Yield Changes as a Tool in Plant Physiology.
Photosynthesis Res,1984,4:361-370
11 H. K. Lichtenthaler, U. Rinderle. The Role of Chlorophyll Fluorescence in the
Detection of Stress Condition in Plants. CRC Critical Reviews in Analytical
Chemistry,1988:29-85
12 W. Bilger, U. Schreiber, O. L. Lange. Chlorophyll Fluorescence as an Indicator of
Heat Induced Limitation of Photosynthesis. In Plant Response to stress, Spriger
Verglag, Berlin,1988:391-399
13 H. K. Hongsuk. New Algae Mapping Technique by the Use of an Airborne Laser
Fluorescence. Applied Optics,1973,12(7):1454-1459
14 C. S. Yentsch, C. M. Yentsch. Fluorescence Spectral Signatures: The Characterizati-
on of Phytoplankton Populations by Theuse of Excitation and Emission Spectra. J.
Mar. Res,1979,37:471-483
79
15 J. S. Cleveland, M. J. Perry. Quantum Yield, Relative Specific Absorption and Fluo-
rescence in Nitrogenlimited Chaetoceros Grasilis. Mar. Biol,1987,94:489-497
16 J. B. SooHoo, D. A. Kiefer, D. J. Collins et al. In vivo Fluorescence Excitation and
Absorption Spectra of Marine Phytoplankton: I. Taxonomic Characteristics and
Responses to Photoadaptation. J. Plankton. Res,1986,8:197-214
17 E. Sakeshaug, G. Johnsen, K. Andresen et al. Modeling of light-dependent Algal
Photosynthesis and Growth: Experiments with Barents Sea Diatoms Thalassiosira
Nordenskioedii and Chaetoceros Furcellatus. Deep Sea. Res,1991,38:415-430
18 C. S. Yentsch, D. A. Phinney. Spectral Fluorescence: an Ataxonomic Tool for Study-
ing the Structure of Phytoplankton Populations. J. Plankton. Res,1985,7:617-632
19 P. B. Oldham, E. J. Zillioux, I. M. Warner. Spectral ‘Fingerprinting’ of Phytoplankt-
on Populations by Two-dimensional Fluorescence and Fourier- transform-based
Pattern Recognition. J. mar. Res,1985,43:893-906.
20 T. J. Cowles, R. A. Desiderio, S. Neuer. In situ Characterization of Phytoplankton
from Vertical Profiles of Fluorescence Emission Spectra. Mar. Biol,1993,115:
217-222
21 L. Poryvkina, S. Babichenko, S. Kaitala et al. Spectral Fluorescence Signature in the
Characterization of Phytoplankton Community Composition. J. Plankton. Res,1994,
16:1315-1327
22 A. M. Chekalyuk, D. K. Bunin, A. V. Chechendaev et al. Lidar Mapping of chloroph-
yll-a and Organic Matter Distributions in the Russian Coastal zone of the Black Sea.
SPIE,1992,(1922):384-390
23 A. M. Chekalyuk, M. Yu. Gorbunov. Laser remote Sensing of Phytoplankton photos-
ynthetic Activity and Chlorophyll by Using a Shipboard Lidar System. SPIE,1992,
(1922):391-400
24 M. Yu. Gorbunov, A. M. Chekalyuk. Lidar in-situ Study of Sunlight Regulation of
Phytoplankton photosynthetic Activity and Chlorophyll Fluorescence. SPIE,1992,
(1922):421-427
25 B. Ruth. Monitoring the Herbicide Concentration in Ponds by Detecting the Chloro-
phyll Fluorescence of Algae with ms-Laser-Excitation. SPIE,1995,(2586):46-55
26 D. Mckee, A. Cunningham, K. Jones. An integrated submersible Fluorometer/Nephe-
lometer/Transmissometer: Design and Testing at Sea. Optics and Laser Technoloty,
1997,29(1):35-39
80
27 J. J. Cullen, A. M. ciotti, R. F. Davis. The Relationship between Near-surface Chlor-
ophyll and Solar-stimulated Fluorescence: Biological Effects. SPIE,1997,
(2963):272-277
28 K. Wild-Allen, P. Tett, D. Bowers. Observations of Diffuse Upwelling Irradiance and
Chlorophyll in Case I Waters near the Canary Islands(Spain). Optics and Laser
Technology,1997,29(1):3-8
29 M. Beutler, K. H. Wiltshire, B. Meyer et al. A fluorometric Method for the Different-
iation of Algal Populations in vivo and in situ. Photosynthesis Research,2002,72
(1):39-53
30 潘德炉.利用荧光高度遥感海洋中叶绿素 a 的浓度.海洋学报,1989,11(6):780-787
31 陈卫标,吴东,张亭禄,等.海洋表层叶绿素浓度的激光雷达测量方法和海上实验.海
洋与湖沼,1998,29(3):255-260
32 靳伟,廖延彪,张志鹏,等.导波光学传感器原理与技术.北京:科学出版社,1998:1
33 E. J. D’sa. Time Series Measurements of Chlorophyll Fluorescence in Oceanic Bott-
om Boundary Layer with Multisensor Fiber Optic Fluorometer. J. Atmos, Ocean,
Technol,1997,14(4):101-106
34 M. Lehaitre. Optical Fiber Spectrphotometry for in situ Seawater Biological and
Chemical Analysis. J. Mar. Environ. Eng,1997,4(1):89-97
35 P. R. Coulet. Luminescence-based Fiber Optic Probes. Sensors and Actuators,1993,
11(3):45-52
36 S. E. Lohrenz. Development of a multi-Sensor in situ Fiber Optic Fluorometer. Prog-
ress Report,1992,(1):101-106
37 张季熊,李郁章,王海波.荧光光纤传感器.仪表技术与传感器,1998,(7):6-9
38 孙即祥.现代模式识别.北京:国防科技大学出版社,2002:2-4
39 D. O. Hebb. The Organization of Behavior. New York: Wiley,1949:1-335
40 F. Rosenblatt. The Perceptron: A Probabilistic Model for Information Storage and
Organization in the Brain. Cornell Aeronautical Laboratory: Psychological
Review,1958,65(6):386-408
41 B. Widrow, M. E. Hoff. Adaptive Switching Circuits. New York: IRE WESCON
Convention Record,1960:96-104
42 M. Minsky, S. Papert. Perceptron: An Introduction to Computational Geometry.
Cambridge, MA: MIT Press,1969:1-98
81
43 J. L. McClelland, D. E. Rumelhart, the PDP Research Group. Parallel Distributed
Processing: Psychological and Biological Models. Cambridge, MA: MIT Press,1986:
1-109
44 R. Miikkulainen. Dyslexic and category-specific aphasic impairments in a self-orga-
nizing feature map models of the lexicon. Brain and Language,1997,59:334-366
45 S. Grossberg. The Complementary Brain: Unifying Brain Dynamics and Modularity.
Trends in Cognitive Science,2000,4(5):233-246
46 E. Oja, M. Kuusela. The ALSM algorithm-an improved subspace method of classif-
ication. Pattern Recognition,1983,16(4):421-427
47 L. Xu, A. Krzyzak, E. Oja. A Neural Net for Dual Subspace Pattern Recognition Me-
thods. International Journal of Neural Systems,1991,12(3):169-184
48 J. J. Hopfield. Neural Networks and Physical Systems with Emergent Collective
Computational Abilities. Proceedings of the National Academy of Sciences, USA,
1982,79:2554-2558
49 R. Hecht-Nielsen. Counterpropagation Networks. Applied Optics,1987,26:4979-4984
50 L. O. Chua, L. Yang. Cellular Neural Networks: Theory. IEEE Transactions on Circ-
uits and Systems,1988,35(10):1257-1272
51 L. O. Chua, L. Yang. Cellular Neural Networks: Applications. IEEE Transactions on
Circuits and Systems,1988,35(10):1273-1290
52 王永骥,涂健.神经元网络控制.北京:机械工业出版社,1998:6-7
53 A. Sharma, S. G. Schulman. Introduction to Fluorescence Spectroscopy. New York:
John Wiley and Sons, Inc,1999:22-23
54 C. L. Catharina. Fluorescence Quenching in Four Unicellar Algae with Diffenert
Light-harvesting and Xanthophylls-cycle Pigments. Photosynthesis Research,1998,
56:277-289
55 M. Balode. The Use of spectral Fluorescence Methods to Detect Changes in the
Phytoplankton Community. Hydrobiologia,1998,363:207-217
56 陈峰,姜锐.微藻生物技术.北京:中国轻工业出版社,1999:1-36
57 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Ch
lorophyll%20Analysis.pdf
58 http://www.biologie.uni-hamburg.de/ b-online/ge24/03.htm
59 温 少 红 , 徐 春 野 , 鞠 宝 , 等 . 紫 球 藻 藻 红 蛋 白 的 分 离 纯 化 及 光 谱 特 性 . 海 洋 通
82
报,2000,19(6):90-94
60 王勇,钱凯先,董强.高纯度藻蓝蛋白分离纯化及光谱特性研究.生物化学与生物物
理进展,1999,26(5):457-460
61 R. Aguirre-Gomez, S. R. Boxall, A. R. Weeks. Detecting photosynthetic Algal Pigm-
ents in natural Populations Using a high-spectral-resolution Spectroradiometer.
International Journal of Remote Sensing,2001,22(15):2867-2884
62 邱 丽 氚 , 凌 元 洁 , 谢 树 莲 . 大 螺 旋 藻 一 些 生 理 特 性 的 研 究 . 木 本 植 物 研
究,1998,18(3):356-360
63 http://www.comm-tec.com/Library/pdfs/Tutorials/CTD/FAQ%20Fluorometric%20Ch
lorophyll%20Analysis.pdf
64 D. S. Knowles, S. H. Lieberman. Field Results from the SCAPS Laser-induced Fluo-
rescence (LIF) Sensor for in-situ Subsurface Detection of Petroleum Hydrocarb- ons.
SPIE,1995,(2504):297-307
65 杨祥林.光纤通信系统.北京:国防工业出版社,2000:34
66 陈根祥.光波技术基础.北京:中国铁道出版社,2000:280
67 叶嘉雄,常大定,陈汝钧.光电系统与信号处理.北京:科学出版社,1997:260
68 林坚,林永钟,谢树森.荧光分析用系列滤光片.光电子.激光,2000,11(1):57-61
69 ADVANTECH. Total Solution for PC-based Automation. China: ADVANTECH Corp,
2003:7-14-7-15
70 刘君华,贾惠芹,丁晖.虚拟仪器图形化编程语言 LabVIEW 教程.北京:电子工业出
版社,2001:1-5
71 G. W. Gohson. LabVIEW Graphic Programming. USA: MC Graw-Hill,1998:232~133
72 张广军,李鑫,魏振忠.基于 RBF 神经网络的结构光三维视觉检测方法研究.计量学
报,2002,23(4):251-255
73 席道瑛,张涛.BP 人工神经网络模型在测井资料岩性自动识别中的应用.物探化计
算技术,1995,17(1):42-48
74 楼顺天,施阳.基于 MATLAB 的系统分析与设计.西安:西安电子科技大学出版
社,1998:13-14
75 J. N. Hwang, S. Y. Kung, M. Niranjan et al. The Past, Present, and Future of Neural
Networks for Signal Processing, IEEE Signal Process. Mag,1997(14):28-48
76 汪晓东,万旭,赵鹏程等.基于神经网络的传感器静态误差综合修正法.仪器仪表学