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О.В. Градов Рубрика: Лесное хозяйство Скачать статью (pdf, 0.9MB )УДК58.02+58.056+58.055+58.03+58.087+58.084 ХРОМАТО-DOI:10.17238/issn0536-1036.2015.5.34АннотацияПредложена новая идеология объективных ауксанометрических измерений, позволя-ющая анализировать динамику роста с дифференциацией по стадиям развития. В ос-нову нового типа измерений заложен длительный мониторинг с привлечением мето-дов оптического анализа, «прямой» масс-спектрометрии и газовой хроматографии. Предложено, опираясь на известные одорологические различия запахов цветущих растений на разных точках фенологического мониторинга и используя флейво- и га-зохимические подходы, различать стадийную динамику различных видов и групп растений, анализировать и автоматически классифицировать древесные растения в модельных фитосообществах на феноритмотипы/фенологические группы на основе данных указанных методов и в рамках данного подхода с привлечением машинного распознавания образов и автоматического фингерпринтинга. Возможно феноспек-тральное ранжирование полученных данных при поиске зависимостей роста и фено-логии стадийного развития от факторов среды. На практике данный метод является внутреннеюстируемым, так как одновременно базируется на нескольких отличных источниках измерений, что позволяет использовать его как в лабораторных условиях или климатических камерах, так и в естественных полевых условиях при эргономич-ном размещении аппаратуры аналогично средствам метеоролого-климатического мо-ниторинга, монтируемым в метеобудке. Предложено принципиально отличная от из-вестных в ботанической и лесотехнической практике ауксанометрическая система, которая позволяет наблюдать за первичным ростом лесных пород в контексте разви-тия за счет того, что индикатором динамики является не количественный (как в обыч-ной ауксанометрии, где единственным критерием роста является удлинение пророст-ка), а комплексно-качественный критерий, складывающийся из взаимно-однозначного сопоставления результатов аналитико-химического анализа молекулярной эмиссии растений и вариаций характеристик среды, что позволяет анализировать обратные связи роста/развития растения и деформаций параметрики внешней среды. В ходе работы в различных режимах посредством обучения распознаванию образов с пополнением базы данных можно исследовать и моделировать не только один пат-терн развития растения, но и экспериментальный отклик экологической структуры признаков на изменение параметров среды, т. е. по мере необходимости переходить к фенологическому, модельно-биогеографическому, биометеорологическому, био-климатологическому, эколого-физиологическому подходам (если таковое позволяют параметры биотрона, климатической камеры, оранжереи, в которых производится выгонка проростков лесных пород), занося спектральные и хроматографические дан-ные в виде корреляционных паттернов в базы данных для последующего сличения. Феноспектральная экспериментальная выгонка позволяет программировать и с по-мощью обратной связи регулировать температуру, четко прогнозируя начало вегета-ции посредством суммирования эффективных температур или выявления их тренда, позволяющего реконструировать последовательность всхода или вегетации отдель-ных растительных форм в корреляции с характеристическими параметрами искус-ственного климата, автоматически классифицировать по комплексу характеристик на феноритмотипы или фенологические группы древесные растения в модельных фито-сообществах по более шкалированной градации, чем в устаревшей системе Морозо-вой, выделявшей только два феноритмотипа у древесных растений (вечнозеленые и листопадные), позволяет работать в режиме регуляции параметрики климатической камеры путем регистрации обратной связи растений за счет использования детекто-ров и датчиков их молекулярной эмиссии в контролируемом физическом окружении, т. е. сами параметры, регистрируемые детектирующей частью установки, могут пред-ставлять собой сигнал для изменения режима ее функционирования.Сведения об авторах© О.В. Градов, мл. науч. сотр., вед. инж.
Институт энергетических проблем химической физики РАН, Ленинский пр-т, д. 38, корп. 2, Москва В-334, Россия, 119334; е-mail: o.v.gradov@gmail.com
Ключевые словафлейвохимия, газохимия, оптическая спектроскопия, ауксанометрия, газовая хроматография, газовая хромато-масс-спектрометрия, феномониторингЛитература1. Вершинин В.И., Дерендяев Б.Г., Лебедев К.С. Компьютерная идентификация органических соединений. М.: Академкнига, 2002. 197 с.
2. Градов О.В., Нотченко А.В. Полуавтоматическая дендрохронография для ис-следования морфогенеза и тератоморфозов на спилах высших растений // Лесотехн. журн. 2012. № 4(8). C. 47–57.
3. Дмитриев М.Т., Мищихин В.А., Степанов Е.В. Газохроматографическое определение фитонцидов в воздухе. Гигиена и санитария, М.: Медицина, 1983. № 7. C. 43–45.
4. Михайленко И.М. Математическое моделирование роста растений на основе экспериментальных данных // Сельскохозяйственная биология. 2007. № 1. C. 103–111.
5. Мухин В.А., Воронин П.Ю. Метаногенная активность в древесных растениях // Физиология растений, 2009. Т. 56. С. 152–154.
6. Мухин В.А., Воронин П.Ю. Выделение метана из древесины живых деревьев // Физиология растений, 2011. Т. 58, № 2. С. 283–289.
7. Полесская О.Г. Растительная клетка и активные формы кислорода. М.: Уни-верситет, 2007. 140 с.
8. Рассадина В.А., Яронская Е.Б., Вершиловская И.В., Егоров В.М., Аверина Н.Г. Электронная ауксанометрия – новый способ регистрации ростовых реакций расте-ний // Земляробства i ахова раслiн: навукова-практычны часопіс. 2007. № 2. С. 19–20.
9. Acock A.C. Discovering Structural Equation Modeling Using Stata. Stata Press, College Station, Texas, 2013. 304 p.
10. Ball G.H., Hall D.J. Isodata: a method of data analysis and pattern classification, Stanford Research Institute. Office of Naval Research. Information Sciences Branch, Menlo Park, California, 1965. 79 p.
11. Barnes C.R. A Registering Auxanometer // Botanical Gazette. 1887. Vol. 12, No 7. P. 150–152.
12. Barrat A., Barthélemy M., Vespignani A. Dynamical Processes on Complex Net-works. Cambridge University Press, 2012. 361 p.
13. Becketti S. Introduction to Time Series using Stata. Stata Press, College Sta-tion, Texas, 2013. 741 p.
14. Beerling D.J., Franks P.J. Plant science: The hidden cost of transpiration // Na-ture. 2010. Vol. 464. P. 495–496.
15. Bergann F. Untersuchungen über Lichtwachstum, Lichtkrümmung und Lichtab-fall bei Avena sativa mit Hilfe monochromatischen Lichtes // Planta. 1930. Vol. 10, No 4. P. 666–743.
16. Binder B.M. Rapid Kinetic Analysis of Ethylene Growth Responses in Seedlings: New Insights into Ethylene Signal Transduction // Journal of Plant Growth Regulation. 2007. Vol. 26, No 2. P. 131–142.
17. Biswal U.C., Biswal B., Raval M.K. Chloroplast Biogenesis: From Proplastid to Gerontoplast. Kluwer Academic, Dordrecht–Boston–London, 2003. 380 p.
18. Botkin D.B. Forest Dynamics: An Ecological Model. Oxford University Press. Oxford–New York, 1993. 328 p.
19. Bovie W.T. A Precision Auxanometer // Botanical Gazette. 1912. Vol. 53, No 6. P. 504–509.
20. Bovie W.T. A Simplified Precision Auxanometer // American Journal of Botany. 1915. Vol. 2, No 2. P. 95–99.
21. Brodribb T.J., Feild T.S. Leaf hydraulic evolution led a surge in leaf photosynthetic capacity during early angiosperm diversification // Ecology Letters. 2010. Vol. 13. P. 175–183.
22. Budagovskaya N.V., Guliaev V.I. Effect of calcium channel blocker on the growth dynamics of plants studied by laser interference auxanometry // Developments in Plant and Soil Sciences. 2002. Vol. 92. P. 204–205.
23. Budagovskaya N.V., Guliaev V.I. Rapid and Slow Response Reactions of Plants on Effect of Antioxidant Ambiol. In: Advanced Research on Plant Lipids (Proceedings of the 15th International Symposium on Plant Lipids). Springer, Dordrecht, 2003. P. 323–326.
24. Bumble S. Computer Generated Physical Properties. CRC Press, Boca Raton, 1999. 288 p.
25. Bumpus H.C. A Simple and Inexpensive Self-Registering Auxanometer // Botanical Gazette. 1887. Vol. 12, No 7. P. 149–150.
26. Buongiorno J., Zhu S., Zhang D., Turner J., Tomberlin D. The Global Forest Products Model: Structure, Estimation, and Applications. Academic Press, Amsterdam–Boston–London–New York–Oxford–Paris–San Diego–San Francisco–Singapore–Sydney–Tokyo, 2003. 300 p.
27. Burgerstein A. Das pflanzenphysiologische Institut der K.K. Wiener Universität von 1873–1884 // Österreichische botanische Zeitschrift. 1884. Vol. 34, No 12. P. 418–422.
28. Chang C.-I. Hyperspectral Data Processing: Algorithm Design and Analysis. Wiley, Hoboken, 2013. 1164 p.
29. Chang C.-I. Hyperspectral Imaging: Techniques for Spectral Detection and Clas-sification. Kluwer Academic - Plenum Publishers, New York, 2003. 367 p.
30. Chen J.-C., Chen C.-T. Correlation Analysis Between Indices of Tree Leaf Spectral Reflectance and Chlorophyll Content // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII, Part B7. 2008. P. 231–238.
31. Cholodny N. Über das Wachstum des vertikal und horizontal orientierten Sten-gels in Zusammenhang mit der Frage nach der hormonalen Natur der Tropismen // Planta. 1929. Vol. 7, No 5. P. 702–719.
32. Christian M., Lüthen H. New methods to analyze auxin-induced growth I: Classical auxinology goes Arabidopsis // Plant Growth Regulation. 2000. Vol. 32, No 2-3. P. 107–114.
33. Clarke L.J. Botany As An Experimental Science – In Laboratory And Garden. Oxford University Press, Milton, 1935. 138 p.
34. Claussen M., Lüthe H., Blatt M., Böttger M. Auxin-induced growth and its link-age to potassium channels // Planta. 1997. Vol. 201, No 2. P. 227–234.
35. Creswell J.W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications Inc., Los Angeles–London–New Delhi–Singapore–Washington, 2013. 304 p.
36. Cserhati T. Multivariate Methods in Chromatography: A Practical Guide. Wiley, Hoboken – Chichester, 2008. 352 p.
37. Dasgupta S. Remote Sensing of Vegetation Water and Fire Risk: Selected Re-search Topics. VDM, Saarbrücken, 2009. 176 p.
38. Davidson E.A., Keller M., Erickson H.E., Verchot L.V., Veldkamp E. Testing a conceptual model of soil emissions of nitrous and nitric oxides // BioScience. 2000. Vol. 50. P. 667–680.
39. De Rovira D. Dictionary of Flavors. Wiley-Blackwell, Ames, Iova, 2004. 736 p.
40. Degenhardt D.C., Greene J.K., Khalilian A. Temporal Dynamics and Electronic Nose Detection of Stink Bug-Induced Volatile Emissions from Cotton Bolls // Psyche. 2012. Vol. 2012. ID 236762. P. 1–9.
41. Drosos J.C., Viola-Rhenals M., Vivas-Reyes R. Quantitative structure-retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices // J. Chromatogr. A. 2010. Vol. 1217, No 26. P. 4411–4421.
42. Du H., Wang J., Hu Z., Yao X. Quantitative Structure Retention relationship study of the constituents of saffron aroma in SPME-GC-MS based on the projection pursuit regression method // Talanta. 2008. Vol. 77, No 1. P. 360–365.
43. Engel H., Heimann M. Weitere Untersuchungen über periodische Guttation // Planta. 1949. Vol. 37, No 3. P. 437–450.
44. Evans M.L., Mulkey T.J., Vesper M.J. Auxin action on proton influx in corn roots and its correlation with growth // Planta. 1980. Vol. 148, No 5. P. 510–512.
45. Evans M.L., Ishikawa H., Estelle M.A. Responses of Arabidopsis roots to auxin studied with high temporal resolution: Comparison of wild type and auxin-response mutants // Planta. 1994. Vol. 194, No 2. P. 215–222.
46. Evans M.L. Functions of Hormones at the Cellular Level of Organization // En-cyclopedia of Plant Physiology. 1984. Vol. 10. P. 23–79.
47. Fernandez S.R., Wagner E. A New Method of Measurement and Analysis of the Stem Extension Growth Rate to Demonstrate Complete Synchronization of Chenopodium rubrum Plants by Environmental Conditions // Journal of Plant Physiology. 1994. Vol. 144, No 3. P. 362–369.
48. Flavor and Health Benefits of Small Fruits (ACS Symposium Series). Ed. by M. Qian, A. Rimando. American Chemical Society, Washington, 2010. 336 p.
49. Flavor, Fragrance, and Odor Analysis. Ed. by R. Marsili. CRC Press, Boca Ra-ton, 2011. 280 p.
50. Flavours and Fragrances: Chemistry, Bioprocessing and Sustainability. Ed. by R.G. Berger. Springer, Berlin–Heidelberg–New York, 2007. 664 p.
51. Fredrickson E.L., Estell R.E., Remmenga M.D. Volatile compounds on the leaf surface of intact and regrowth tarbush (Flourensia cernua DC) canopies // J. Chem. Ecol. 2007. Vol. 33, No 10. P. 1867–1875.
52. Fritsch K. Akademien, Botanische Gesellschaften, Vereine, Kongresse etc. // Ös-terreichische botanische Zeitschrift. 1905. Vol. 55, No 6. P. 245–251.
53. Gas Enzymology. Ed. by H. Degn, R.P. Cox, H. Toftlund. Proceedings of a Sym-posium held at Odense University, Denmark, 1984. Kluwer Acad. Pub., Dordrecht, 1985. 264 p.
54. Geider R. Algal Photosynthesis: The Measurement of Algal Gas Exchange. Springer, 1992. 256 p.
55. Giantomassi A. Modeling estimation and identification of complex system dy-namics: issues and solutions. Lambert Academic Publishing, Saarbrücken, 2012. 136 p.
56. Gitelson A.A., Gritz Y., Merzlyak M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves // Journ. Plant Physiol. 2003. Vol. 160. P. 271–282.
57. Golden K.E. An Auxanometer for the Registration of Growth of Stems in Thick-ness // Botanical Gazette. 1894. Vol. 19, No 3. P. 113–116.
58. Gould W., Pitblado J., Poi B. Maximum Likelihood Estimation with Stata. Stata Press, College Station, Texas, 2010. 352 p.
59. Hall D.L. , McMullen S.A.H. Mathematical Techniques in Multisensor Data Fu-sion. Artech House, Boston–London, 2004. 466 p.
60. Handbook of Fruit and Vegetable Flavors. Ed. by Y.H. Hui. Wiley, Hoboken, 2010. 1095 p.
61. Hanes J.M. Spring leaf phenology and the diurnal temperature range in a temperate maple forest // International Journal of Biometeorology. 2013. Vol. 58, No 2. P. 103–108.
62. Helt M.F. Vegetation Identification With LIDAR. Thes. Naval Postgraduate School. Monterey, California, 2005. 83 p.
63. Hemmer M.C. Expert Systems in Chemistry Research. CRC Press, Boca Raton, 2007. 416 p.
64. Heydanek M.G., McGorrin R.J. Gas chromatography-mass spectroscopy investi-gations on the flavor chemistry of oat groats // J. Agric. Food Chem. 1981. Vol. 29, No 5. P. 950–954.
65. Hoffmann E.A., Fekete Z.A., Rajkó R., Pálinkó I., Körtvélyesi T. Theoretical char-acterization of gas-liquid chromatographic stationary phases with quantum chemical de-scriptors // Journ. Chromatogr. A. 2009. Vol. 1216, No 12. P. 2540–2547.
66. Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. Ed. by M. Kalacska, G.A. Sanchez-Azofeifa. CRC Press, Boca Raton - London - New York, 2008. 352 p.
67. Hyperspectral Remote Sensing of Vegetation. Ed. by P.S. Thenkabail, J.G. Lyon, A. Huete. CRC Press, Boca Raton, 2011. 781 p.
68. Iglesias-Rodriguez M.D., Halloran P.RRickaby R.E.M., Hall I.R., Colmenero-Hidalgo E., Gittins J.R., Green D.R.H., Tyrrell T., Gibbs S.J., Dassow P., Rehm E., Arm-brust E.V., Boessenkool K.P. Phytoplankton Calcification in a High-CO2 World // Science. 2008. Vol. 320. P. 336–340.
69. Inman-Bamber N.G. Automatic plant extension measurement in sugarcane in relation to temperature and soil moisture // Field Crops Research. 1995. Vol. 42, No 2-3. P. 135–142.
70. Isermann R., Münchhof M. Identification of Dynamic Systems: An Introduction with Applications. Springer, Heidelberg–Dordrecht–London–New York, 2011. 730 p.
71. Jaffe M.J. Thigmomorphogenesis: The response of plant growth and development to mechanical stimulation // Planta. 1973. Vol. 114, No 2. P. 143–157.
72. Jennings W. Qualitative Analysis of Flavor and Fragrance Volatiles by Glass Ca-pillary Gas Chromatography. Academic Press, New York–London–Sydney–Toronto–San Francisco, 1980. 472 p.
73. Jin H.J., Lee S.H., Kim T.H., Park J., Song H.S., Park T.H., Hong S. Nanovesicle-based bioelectronic nose platform mimicking human olfactory signal transduction // Biosensors and Bioelectronics, 2012. Vol. 35, No 1. P. 335–341.
74. Jones. H.G. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology. Cambridge University Press, Cambridge–New York–Melbourne, 1992. 456 p.
75. Jones H.G., Vaughan R.A. Remote Sensing of Vegetation: Principles, Tech-niques, and Applications. Oxford University Press, Oxford–New York, 2010. 400 p.
76. Jönsson S., Eriksson L.A., van Bavel B. Multivariate characterization and quantitative structure-property relationship modeling of nitroaromatic compounds // Anal. Chim. Acta. 2008. Vol. 621, No 2. P. 155–162.
77. Katou K., Ichino K. Effects of carbon dioxide on the spatially separate electro-genic ion pumps and the growth rate in the hypocotyl of Vigna sesquipedalis // Planta. 1982. Vol. 155, No 6. P. 486–492.
78. Keppler F., Hamilton J.T., Brass M., Rockmann T. Methane emissions from terrestrial plants under aerobic conditions // Nature. 2006. Vol. 439. P. 187–191.
79. Kim K.S. 3D Visualization of an Invariant Display Strategy for Hyperspectral Im-agery. Thes. Naval Postgraduate School, Monterey, California, 2002. 67 p.
80. Kim S.Y., Mulkey T.J. Effect of ethylene antagonists on auxin-induced inhibition of intact primary root elongation in maize (Zeamays L.) // Journal of Plant Biology. 1997. Vol. 40, No 4. P. 256–260.
81. Knohl A., Veldkamp E. Global change: Indirect feedbacks to rising CO2 // Na-ture. 2011. Vol. 475. P. 177–178.
82. Kohler U., Kreuter F. Data Analysis Using Stata. Stata Press, College Sta-tion, Texas, 2012. 497 p.
83. Kolb B., Ettre L.S. Static Headspace-Gas Chromatography: Theory and Prac-tice. Wiley, Hoboken, 2006. 350 p.
84. Kottek M., Grieser J., Beck C., Rudolf B., Rubel F. World Map of the Köppen–Geiger climate classification updated // Meteorol. Z. 2006. Vol. 15, No 3. P. 259–263.
85. Kozlowski T.T. The Physiological Ecology of Woody Plants. Academic Press, San Diego–New York–Boston–London–Sidney–Tokyo–Toronto, 1990. 678 p.
86. Kozlowski T.T., Pallardy S.G. Physiology of Woody Plants. Academic Press, San Diego–London–Boston–New York–Sidney–Tokyo–Toronto, 1996. 411 p.
87. Kunkel R., Gardner W.H. Potato tuber hydration and its effect on blackspot of Russet Burbank potatoes in the Columbia Basin of Washington // American Potato Journal. 1965. Vol. 42, No 5. P. 109–124.
88. Lago J.H.G., Favero O.A., Romoff P. Microclimatic Factors and Phenology In-fluences in the Chemical Composition of the Essential Oils from Pittosporum undulatum Vent. Leaves // Journ. Braz. Chem. Soc. 2006. Vol. 17, No 7. P. 1334–1338.
89. Lavalle M. Remote Sensing of Vegetation by Polarimetric Space Interferometers: Models and Methods. Lambert Academic Publishing, Saarbrücken, 2012. 220 p.
90. Lee M.J., Jeon S.W., Song W.K. Designation for an Ecological Network using Re-mote Sensing: Focusing on the North-East Asia. Lambert Academic Publishing, 2013. 64 p.
91. Lepeschkin W. Beschreibung und Erklärung der Wachstumserscheinun-gen. In: Lehrbuch der Pflanzenphysiologie Auf Physikalisch-Chemischer Grundlage. Verlag Von Julius Springer, Berlin, 1925. P. 191–242.
92. Li Q., Nakadai A., Matsushima H., Miyazaki Y., Krensky A.M., Kawada T., Morimoto K. Phytoncides (wood essential oils) induce human natural killer cell activity // Immunopharma-col. Immunotoxicol. 2006. Vol. 28, No 2. P. 319–333.
93. Li Q., Morimoto K., Kobayashi M., Inagaki H., Katsumata M., Hirata Y., Hirata K., Shimizu T., Li Y.J., Wakayama Y., Kawada T., Ohira T., Takayama N., Kagawa T., Miyazaki Y. A forest bathing trip increases human natural killer activity and expression of anti-cancer proteins in female subjects // Journ. Biol. Regul. Homeost. Agents. 2008. Vol. 22, No 1. P. 45–55.
94. Li Q., Morimoto K., Kobayashi M. Inagaki H., Katsumata M., Hirata Y., Hirata K., Suzuki H., Li Y.J., Wakayama Y., Kawada T., Park B.J., Ohira T., Matsui N., Kagawa T., Miyazaki Y., Krensky A.M. Visiting a forest, but not a city, increases human natural killer activity and expression of anti-cancer proteins // Int. J. Immunopathol. Pharmacol. 2008. Vol. 21, No 1. P. 117–127.
95. Liao Y.C., Chang Chien S.W., Wang M.C., Shen Y., Seshaiah K. Relationship between lead uptake by lettuce and water-soluble low-molecular-weight organic acids in rhizosphere as influenced by transpiration // J. Agric. Food Chem. 2007. Vol. 17, No 55. P. 8640–8649.
96. Liao Y.C., Chien S.W., Wang M.C., Shen Y., Hung P.L., Das B. Effect of transpiration on Pb uptake by lettuce and on water soluble low molecular weight organic acids in rhizosphere // Chemosphere. 2006. Vol. 65, No 2. P. 343–351.
97. Literatur-Übersicht // Österreichische botanische Zeitschrift. 1907. Vol. 57, No 2. P. 74–85.
98. Lloyd F.E. A New and Cheap Form of Auxanometer // Torreya. 1903. Vol. 3, No 7. P. 97–100.
99. Mager P.P. Multivariate Chemometrics in QSAR: A Dialogue. Wiley, New York–Chichester–Toronto–Brisbane–Singapure, 1988, 345 p.
100. Maina J.N. The Gas Exchangers: Structure, Function, and Evolution of the Res-piratory Processes. Springer, Berlin, 1998. 498 p.
101. Malone M., Herron M., Morales M.A. Continuous measurement of macronutri-ent ions in the transpiration stream of intact plants using the meadow spittlebug coupled with ion chromatography // Plant Physiology. 2002. Vol. 130, No 3. P. 1436–1442.
102. McBride R., Evans M.L. Auxin inhibition of acid-and fusicoccin-induced elon-gation in lentil roots // Planta. 1977. Vol. 136, No 2. P. 97–102.
103. McKown A.D., Cochard H., Sack L. Decoding leaf hydraulics with a spatially explicit model: principles of venation architecture and implications for its evolu-tion // American Naturalist. 2010. Vol. 175. P. 447–460.
104. Meron E. Nonlinear Physics of Ecosystems. CRC Press, Boca Raton, 2013. 350 p.
105. Meyer W.S., Green G.C. Plant indicators of wheat and soybean crop water stress // Irrigation Science. 1981. Vol. 2, No 3. P. 167–176.
106. Mitchell H.B. Multi-Sensor Data Fusion: An Introduction. Springer, Berlin, Heidelberg, 2010. 296 p.
107. Mohammed G.H., Noland T.L., Irving D., Sampson P.H., Zarco-Tejada P.J., Miller J.R. Natural and stress-induced effects on leaf spectral reflectance in Ontario species // Forest Research Report. 2000. No 156. 34 p.
108. Monje O., Bugbee B. Characterizing photosynthesis and transpiration of plant communities in controlled environments // Acta Hortic. 1996. Vol. 40. P. 123–128.
109. Mulkey T.J., Evans M.L., Kuzmanoff K.M. The kinetics of abscisic acid action on root growth and gravitropism // Planta. 1983. Vol. 157, No 2. P. 150–157.
110. Multispectral Image Processing and Pattern Recognition (Series in Machine Perception and Artificial Intelligence, 44). Ed. by J. Shen, P.S.P. Wang, T. Zhang. World Scientific Pub. Co Inc., Singapore–New Jersey–London–Hong Kong, 2001. 130 p.
111. Mutaftschiev S., Prat R., Pierron M., Devilliers G., Goldberg R. Relationships between cell-wall β-1,3-endoglucanase activity and auxin-induced elongation in mung bean hypocotyl segments // Protoplasma. 1997. Vol. 199, No 1-2. P. 49–56.
112. Nelles O. Nonlinear System Identification: From Classical Approaches to Neu-ral Networks and Fuzzy Models. Springer, Berlin–Heidelberg–New York, 2001. 785 p.
113. Nendza M. Structure-Activity Relationships in Environmental Sciences. Chap-man and Hall, London, 1998. 288 p.
114. Nestler A. Das pflanzenphysiologische Institut der k. k. deutschen Universität in Prag // Österreichische botanische Zeitschrift. 1909. Vol. 59, No 2. P. 54–62.
115. Parida L. Pattern Discovery in Bioinformatics: Theory & Algorithms. Chapman and Hall / CRC, Boca raton–London–New York, 2007. 512 p.
116. Peel M. C., Finlayson B. L., McMahon T. A. Updated world map of the Kö-ppen–Geiger climate classification // Hydrol. Earth Syst. Sci. 2007. Vol. 11. P. 1633–1644.
117. Persaud K., Dodd G. Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose // Nature. 1982. Vol. 299, No 5881. P. 352–355.
118. Plant Cell Death Processes. Ed. by L.D. Nooden. Academic Press, Amsterdam–Boston–Heidelberg–London–New York–Oxford–Paris–San Diego-San Francisco–Singapore–Sydney–Tokyo, 2003. 392 p.
119. Pretzsch H. Forest Dynamics, Growth and Yield: From Measurement to Model. Springer, Heidelberg–Dordrecht–London–New York, 2010. 683 p.
120. Puc M., Kasprzyk I. The patterns of Corylus and Alnus pollen seasons and pol-lination periods in two Polish cities located in different climatic regions // Aerobiologia. 2013. Vol. 29, No 4. P. 495–511.
121. Qaderi M.M., Reid D.M. Methane emissions from six crop species exposed to three components of global climate change: temperature, ultraviolet-B radiation and water stress // Physiologia Plantarum. 2009. Vol. 137, No 2. P. 139–147.
122. Rabe-Hesketh S., Skrondal A. Multilevel and Longitudinal Modeling Using Stata, Vol.1. Stata Press, College Station, Texas, 2012. 497 p.
123. Rabe-Hesketh S., Skrondal A. Multilevel and Longitudinal Modeling Using Stata, Vol. 2. Stata Press, College Station, Texas, 2012. 477 p.
124. Raol J.R. Multi-Sensor Data Fusion with MATLAB. CRC Press, Boca Raton, 2009. 568 p.
125. Rayle D.L., Cleland R. Rapid Growth Responses in the Avena Coleoptile: A Comparison of the Action of Hydrogen Ions, CO2, and Auxin // Proceedings of the 7th In-ternational Conference on Plant Growth Substances / Australia, 1972. P. 44–51.
126. Röck F., Barsan N., Weimar U. Electronic Nose: Current Status and Future Trends // Chemical Reviews. 2008. Vol. 108, No 2. P. 705–725.
127. Rodriguez-Bachiller A., Glasson J. Expert Systems and Geographic Information Systems for Impact Assessment. Taylor & Francis, London–New York, 2004. 408 p.
128. Schwarz J., Gries R., Hillier K., Vickers N., Gries G. Phenology of semiochemi-cal-mediated host foraging by the western boxelder bug, Boisea rubrolineata, an aposematic seed predator // J. Chem. Ecol. 2009. Vol. 35, No 1. P. 58–70.
129. Siddiqui K.J., Eastwood D.L., Liu Y-H. Spectral pattern recognition: the meth-odology // SPIE Proceedings. 1999. Vol. 3854. P. 84–97.
130. Sims D.A., Gamon J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages // Re-mote Sensing of Environment. 2002. Vol. 81. P. 337–354.
131. Skogestad S., Postlethwaite I. Multivariable Feedback Control: Analysis and Design. Wiley, Chichester–New York–Brisbane–Toronto–Singapore, 2005. 592 p.
132. Spalding E.P., Miller N.D. Image analysis is driving a renaissance in growth measurement // Current Opinion in Plant Biology. 2013. Vol. 16, No 1. P. 100–104.
133. Sparks W.C. A review of abnormalities in the potato due to water uptake and translocation // American Potato Journal. 1958. Vol. 35, No 3. P. 430–436.
134. Spectral Theory And Nonlinear Analysis With Applications to Spatial Ecology. Ed. by Cano-Сasanova S., Lopez-Gomez J., Mora-Сorral C. World Scientific Pub. Co Inc., New Jersey–London–Singapore–Beijing–Shanghai–Hong Kong–Taipei–Chennai, 2005. 276 p.
135. Steffens B., Lüthen H. New methods to analyse auxin-induced growth II: The swelling reaction of protoplasts – a model system for the analysis of auxin signal transduc-tion? // Plant Growth Regulation. 2000. Vol. 32, No 2-3. P. 115–122.
136. Stone G.E. A Simple Self-Registering Auxanometer // Botanical Gazette. 1892. Vol. 17, No 4. P. 105–107.
137. Taiz L., Métraux J.-P. The kinetics of bidirectional growth of stem sections from etiolated pea seedlings in response to acid, auxin and fusicoccin // Planta. 1979. Vol. 146. No 2, P. 171–178.
138. Tan Y., Siebert K.J. Modeling bovine serum albumin binding of flavor com-pounds (alcohols, aldehydes, esters, and ketones) as a function of molecular properties // Journ. Food Sci. 2008. Vol. 73, No 1. P. 56–63.
139. Todeschini R., Consonni V. Molecular Descriptors for Chemoinformatics (Methods and Principles in Medicinal Chemistry). Wiley-VCH, Weinheim, 2009. 1257 p.
140. Tucker A.O., De Baggio T. The Encyclopedia of Herbs: A Comprehensive Ref-erence to Herbs of Flavor and Fragrance. Timber Press, Portland - London, 2009. 604 p.
141. van Groenigen K.J., Osenberg C.W., Hungate B.A. Increased soil emissions of po-tent greenhouse gases under increased atmospheric CO2 // Nature. 2011. Vol. 475. P. 214–216.
142. Vollmer M., Möllmann K.-P. Infrared Thermal Imaging: Fundamentals, Research and Applications. Wiley-VCH, Weinheim, 2010. 612 p.
143. W.E.B. Botany as an Experimental Science in Laboratory and Garden // Nature. 1935. Vol. 136. P. 890.
144. Warnock C. Backyard Winter Gardening: Vegetables Fresh and Simple, In Any Climate without Artificial Heat or Electricity the Way It's Been Done for 2,000 Years. Cedar Fort, Inc. Springville, 2013. 176 p.
145. Went F.A.F.C. Die Bedeutung des Wuchsstoffes (Auxin) für Wachstum, photo- und geotropische Krümmungen // Naturwissenschaften. 1933. Vol. 21, No 1. P. 1–7.
146. Werkhoff P., Guntert M., Krammer G., Sommer H., Kaulen J. Vacuum Head-space Method in Aroma Research: Flavor Chemistry of Yellow Passion Fruits // J. Agric. Food Chem. 1998. Vol. 46. P. 1076−1093.
147. Wise P.M., Olsson M.J., Cain W.S. Quantification of Odor Quality // Chemical Senses. 2000. Vol. 25, No 4. P. 429–443.
148. Zachor A.S. Spectral pattern recognition in IR remote sensing // Applied Optics. 1983. Vol. 22, No 17. P. 2699–2703.
Поступила 06.10.14
Ссылка на английскую версию:Chromatography-Auxanometry and Chromate-Mass-Auxanometry in Forest Species Vegetation Phenological Monitoring Based on Gas and Flavor Chemical Principles with Patterns Automatic IdentificationUDC 58.02+58.056+58.055+58.03+58.087+58.084
Chromatography-Auxanometry and Chromate-Mass-Auxanometry in Forest Species Vegetation Phenological Monitoring Based on Gas and Flavor Chemical Principles with Patterns Automatic Identification
O.V. Gradov, Research Assistant, Principal Engineer
Institute of Energy Problems of Сhemical Physics of the Russian Academy of Sciences, Leninskiy pr., 38/2, Moscow, 119334, Russia; e-mail: o.v.gradov@gmail.com
A new ideology for direct auxanometric measurements is proposed, which allows to analyze growth dynamics at different developmental stages. The above measurements include long-term monitoring using optical analysis with direct mass spectroscopic and gas chromatograph-ic detection. Based on the known flavor differences оf blooming plants at various phenological stages, we propose to distinguish phenological stage dynamics of various plant species and groups according to modern trends in flavor chemistry. It is possible to perform a simultaneous chemical analysis and automatic classification of forest plants in model plant communities according to their phenorhythm types and phenological groups with the approach described using automatic pattern recognition and fingerprinting. The novel method can also provide phenospectral data ranging useful for establishing the dependence of plant growth and devel-opmental stage phenology on the environmental factors. Since the method is based on combi-nation of several different measurement sources, it possesses a wide application in laboratory climatic chambers as well as in natural field conditions with the equipment placed in mete-orological instrument shelter like the abundant tools for meteo-climatic monitoring. In this paper we propose a novel auxanometric system which is fundamentally different from all the previously known analogues, as it allows a simultaneous monitoring of forest plant species growth together with plant development stages. Unlike the quantitative approach in standard
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auxanometric measurement techniques with the shoot elongation, our system implements a comprehensive qualitative growth dynamics criterion including one-to-one correspondence between the chemical analysis of plant molecular emission and the environmental conditions variations, that allows to analyze the feedback between the plant growth/development and the environmental parameter variation. This is provided by the fact that the automatic system dur-ing operation in different modes after pattern recognition learning (with the subsequent com-pletion of the database) allows us to investigate and simulate not only a single plant develop-ment pattern characteristic of a certain standard feature space, but also to study experimentally the response of the ecological feature structure upon the changing of external factors. The above strategy leads to the emergence of phenological, simulating-biogeographical, biomete-orological, bioclimatological and ecology-physiological approaches in auxanometry. The sys-tem proposed parameter variation in the course of the germination/elongation process monitor-ing as needed (if it is possible within the biotron, climatic chamber or the greenhouse used for germination/elongation of forest plant species), with the following addition of the spectral and chromatographic data in the form of correlation patterns into the database for subsequent com-parison. In the case of experimental phelonological spectral plant germination/elongation with the described system it is possible to program and control the temperature using a feedback and thus to predict vegetation initiation accurately. This can be implemented by summing the effective temperatures or their trend detection, which allows to reconstruct the sequence of germination or vegetation of individual plant forms in correlation with the characteristic pa-rameters of the artificial climate (for example, if known that for maple (g. Acer) the sum of effective temperatures is 156.2 °С, and for linden (g. Tilia) – 739 °С, it is obvious that in the thermal ranking database the linden will be behind the maple). Due to this fact the auxanomet-ric described system allows automatic classification of forest plants in the model plant com-munities on a set of characteristics according to the phenorhythm types or phenological groups using a more scaled classification than in outdated systems. The same fact allows monitoring in a climate chamber parameter regulation mode by the plant feedback registration using their molecular emission sensors in a controlled physical environment, i.e. the registered parameters from the detector can be considered as a signal changing the chamber operation mode. The described system operates in a wide range of conditions and possesses an amount of different application fields, so it is worth being recommended for implementation in both field and laboratory practice of forest engineering.
Keywords: flavor chemistry, gas chemistry, optical spectroscopy, auxanometry, gas chroma-tography , gas chromate-mass-spectrometry, phenological monitoring.
REFERENCES
1. Bumpus H.C. A Simple and Inexpensive Self-Registering Auxanometer. Botanical Gazette, 1887, vol. 12, no 7, pp. 149–150.
2. Barnes C.R. A Registering Auxanometer. Botanical Gazette, 1887, vol. 12, no. 7, pp. 150–152.
3. Stone G.E. A Simple Self-Registering Auxanometer. Botanical Gazette, 1892, vol. 17, no. 4, pp. 105–107.
4. Golden K.E. An Auxanometer for the Registration of Growth of Stems in Thick-ness. Botanical Gazette, 1894, vol. 19, no 3, pp. 113–116.
5. Lloyd F. E. A New and Cheap Form of Auxanometer. Torreya, 1903, vol. 3, no 7, pp. 97–100.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
59
6. Bovie W.T. A Precision Auxanometer. Botanical Gazette, 1912, vol. 53, no 6, pp. 504–509.
7. Bovie W.T. A Simplified Precision Auxanometer. American Journal of Botany, 1915, vol. 2, no 2, pp. 95–99.
8. Burgerstein A. Das pflanzenphysiologische Institut der K.K. Wiener Universität von 1873-1884. Österreichische botanische Zeitschrift, 1884, vol. 34, no 12, pp. 418–422.
9. Fritsch K. Akademien, Botanische Gesellschaften, Vereine, Kongresse etc. Öster-reichische botanische Zeitschrift, 1905, vol. 55, no 6, pp. 245–251.
10. Literatur-Übersicht. Österreichische botanische Zeitschrift, 1907, vol. 57, no 2, pp. 74–85.
11. Nestler A. Das pflanzenphysiologische Institut der k. k. deutschen Universität in Prag. Österreichische botanische Zeitschrift, 1909, vol. 59, no 2, pp. 54–62.
12. Lepeschkin W. Lehrbuch der Pflanzenphysiologie auf Physikalisch-Chemischer Grundlage. Beschreibung und Erklärung der Wachstumserscheinungen,1925, pp. 191–242.
13. Cholodny N. Über das Wachstum des vertikal und horizontal orientierten Sten-gels in Zusammenhang mit der Frage nach der hormonalen Natur der Tropismen. Planta, 1929, vol. 7, no 5, pp. 702–719.
14. Went F.A.F.C. Die Bedeutung des Wuchsstoffes (Auxin) für Wachstum, photo- und geotropische Krümmungen. Naturwissenschaften, 1933, vol. 21, no 1, pp. 1–7.
15. Engel H., Heimann M. Weitere Untersuchungen über periodische Guttation. Planta, 1949, vol. 37, no 3, pp. 437–450.
16. Sparks W.C. A review of Abnormalities in the Potato due to Water Uptake and Translocation. American Potato Journal, 1958, vol. 35, no 3, pp. 430–436.
17. Kunkel R., Gardner W.H. Potato Tuber Hydration and its Effect on Blackspot of Russet Burbank Potatoes in the Columbia Basin of Washington. American Potato Journal, 1965, vol. 42, no 5, pp. 109–124.
18. Meyer W.S., Green G.C. Plant Indicators of Wheat and Soybean Crop Water Stress. Irrigation Science, 1981, vol. 2, no 3, pp. 167–176.
19. Rayle D.L., Cleland R. Rapid Growth Responses in the Avena Coleoptile: A Comparison of the Action of Hydrogen Ions, CO2, and Auxin. Proc. the 7th Int. Conf. on Plant Growth Substances. Australia, 1972, pp. 44–51.
20. Jaffe M.J. Thigmomorphogenesis: The Response of Plant Growth and Develop-ment to Mechanical Stimulation. Planta, 1973, vol. 114, no 2, pp. 143–157.
21. McBride R., Evans M.L. Auxin Inhibition of Acid-and Fusicoccin-Induced Elon-gation in Lentil Roots. Planta, 1977, vol. 136, no. 2, pp. 97–102.
22. Evans M.L., Mulkey T.J., Vesper M.J. Auxin Action on Proton Influx in Corn Roots and its Correlation with Growth. Planta, 1980, vol. 148, no 5, pp. 510–512.
23. Katou K., Ichino K. Effects of Carbon Dioxide on the Spatially Separate Electro-genic Ion Pumps and the Growth Rate in the Hypocotyl of Vigna Sesquipedalis. Planta, 1982, vol. 155, no 6, pp. 486–492.
24. Mulkey T.J., Evans M.L., Kuzmanoff K.M. The Kinetics of Abscisic Acid Action on Root Growth and Gravitropism. Planta, 1983, vol. 157, no 2, pp. 150–157.
25. Evans M.L., Ishikawa H., Estelle M.A. Responses of Arabidopsis Roots to Auxin Studied with High Temporal Resolution: Comparison of Wild Type and Auxin-Response Mutants. Planta, 1994, vol. 194, no 2, pp. 215–222.
26. Christian M., Lüthen H. New Methods to Analyse Auxin-Induced Growth I: Clas-sical Auxinology Goes Arabidopsis. Plant Growth Regulation, 2000, vol. 32, no 2-3, pp. 107–114.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
60
27. Steffens B., Lüthen H. New Methods to Analyse Auxin-Induced Growth II: The Swelling Reaction of Protoplasts – a Model System for the Analysis of Auxin Signal Trans-duction? Plant Growth Regulation, 2000, vol. 32, no 2-3, pp. 115–122.
28. Claussen M., Lüthe H., Blatt M., Böttger M. Auxin-Induced Growth and its Linkage To Potassium Channels. Planta, 1997, vol. 201, no. 2, pp. 227–234.
29. Budagovskaya N.V., Guliaev V.I. Effect of Calcium Channel Blocker on the Growth Dynamics of Plants Studied by Laser Interference Auxanometry. Developments in Plant and Soil Sciences, 2002, vol. 92, pp. 204–205.
30. Budagovskaya N.V., Guliaev V.I. Rapid and Slow Response Reactions of Plants on Effect of Antioxidant Ambiol. Advanced Research on Plant Lipids, 2003, pp. 323–326.
31. Taiz L., Métraux J.-P. The Kinetics of Bidirectional Growth of Stem Sections from Etiolated Pea Seedlings in Response to Acid, Auxin and Fusicoccin. Planta, 1979, vol. 146, no 2, pp. 171–178.
32. Fernandez S.R., Wagner E.A. New Method of Measurement and Analysis of the Stem Extension Growth Rate to Demonstrate Complete Synchronisation of Chenopodium Rubrum Plants by Environmental Conditions. Journal of Plant Physiology, 1994, vol. 144, no 3, pp. 362–369.
33. Inman-Bamber N.G. Automatic Plant Extension Measurement in Sugarcane in Rela-tion to Temperature and Soil Moisture. Field Crops Research, 1995, vol. 42, no 2-3, pp. 135–142.
34. Spalding E.P., Miller N.D. Image Analysis is Driving a Renaissance in Growth Measurement. Current Opinion in Plant Biology, 2013, vol. 16, no 1, pp. 100–104.
35. Evans M.L. Functions of Hormones at the Cellular Level of Organization. Ency-clopedia of Plant Physiology, 1984, vol. 10, pp. 23–79.
36. Mutaftschiev S., Prat R., Pierron M., Devilliers G., Goldberg R. Relationships be-tween Cell-Wall β-1,3-Endoglucanase Activity and Auxin-Induced Elongation in Mung Bean Hypocotyl Segments. Protoplasma, 1997, vol. 199, no 1-2, pp. 49–56.
37. Kim S.Y., Mulkey T.J. Effect of Ethylene Antagonists on Auxin-Induced Inhibi-tion of Intact Primary Root Elongation in Maize (Zeamays L.). Journal of Plant Biology, 1997, vol. 40, no 4, pp. 256–260.
38. Rassadina V.A., Yaronskaya E.B., Vershilovskaya I.V., Egorov V.M., Averina N.G. Elektronnaya auksanometriya - novyy sposob registratsii rostovykh reaktsiy rasteniy [Electronic Auxanometry – is a New Method of Registration of Plant Growth Reaction]. Zemljarobstva i ahova raslin: navukova-praktychny chasopіs, 2007, no 2, pp. 19–20.
39. Binder B.M. Rapid Kinetic Analysis of Ethylene Growth Responses in Seedlings: New Insights into Ethylene Signal Transduction. Journal of Plant Growth Regulation, 2007, vol. 26, no 2, pp. 131–142.
40. Clarke L.J. Botany as an Experimental Science – in Laboratory and Garden. Oxford University Press, Milton, 1935. 138 p.
41. W.E.B. Botany as an Experimental Science in Laboratory and Garden. Nature, 1935, vol. 136, p. 890.
42. Mikhaylenko I.M. Matematicheskoe modelirovanie rosta rasteniy na osnove eks-perimental'nykh dannykh [Mathematical Modeling of Plant Growth Based on Experimental Data]. Sel'skokhozyaystvennaya biologiya, 2007, no 1, pp. 103–111.
43. Kozlowski T.T. The Physiological Ecology of Woody Plants. San Diego–New York–Boston–London–Sidney–Tokyo–Toronto, 1990. 678 p.
44. Kozlowski T.T., Pallardy S.G. Physiology of Woody Plants. San Diego–London–Boston–New York–Sidney–Tokyo–Toronto, 1996. 411 p.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
61
45. Keppler F. et al. Methane Emissions from Terrestrial Plants under Aerobic Con-ditions. Nature, 2006, vol. 439, pp. 187–191.
46. Mukhin V.A., Voronin P.Yu. Vydelenie metana iz drevesiny zhivykh derev'ev [Methane Emanation from Living Tree Wood]. Fiziologiya rasteniy, 2011, vol. 58, no. 2, pp. 283–289.
47. Mukhin V.A., Voronin P.Yu. Metanogennaya Aktivnost' v Drevesnykh Rasteniyakh. [Methanogenic Activity in Wood Plants]. Fiziologiya rasteniy, 2009, vol. 56, pp. 152–154.
48. Polesskaya O.G. Rastitel'naya kletka i aktivnye formy kisloroda [Vegetative Cell and Oxygen Active Forms]. Moscow, 2007. 140 p.
49. Kees Jan van Groenigen, Craig W. Osenberg, Bruce A. Hungate. Increased Soil Emissions of Potent Greenhouse Gases under Increased Atmospheric CO2 . Nature, 2011, vol. 475, pp. 214–216.
50. Knohl A., Veldkamp E. Global Change: Indirect Feedbacks to Rising CO2. Nature, 2011, vol. 475, pp. 177–178.
51. Davidson E.A., Keller M., Erickson H.E. et al. Testing a Conceptual Model of Soil Emissions of Nitrous and Nitric Oxides. BioScience, 2000, vol. 50, pp. 667–680.
52. Qaderi M.M., Reid D.M. Methane Emissions from Six Crop Species Exposed to Three Components of Global Climate Change: Temperature, Ultraviolet-B Radiation and Water Stress. Physiologia Plantarum, 2009, vol. 137, no 2, pp. 139–147.
53. Iglesias-Rodriguez M.D. et al. Phytoplankton Calcification in a High-CO2 World. Science, 2008, vol. 320, pp. 336–340.
54. Beerling D.J., Franks P.J. Plant science: The hidden Cost of Transpira-tion. Nature, 2010, vol. 464, pp. 495–496.
55. McKown A.D., Cochard H., Sack L. Decoding Leaf Hydraulics with a Spatially Explicit Model: Principles of Venation Architecture and Implications for its Evolu-tion. American Naturalist, 2010, vol. 175, pp. 447–460.
56. Brodribb T.J., Feild T.S. Leaf Hydraulic Evolution Led a Surge in Leaf Photo-synthetic Capacity During Early Angiosperm Diversification. Ecology Letters, 2010, vol. 13, pp. 175–183.
57. Malone M., Herron M., Morales M.A. Continuous Measurement of Macronutri-ent Ions in the Transpiration Stream of Intact Plants using the Meadow Spittlebug Coupled with Ion Chromatography. Plant Physiology, 2002, vol. 130, no 3, pp. 1436–1442.
58. Monje O., Bugbee B. Characterizing Photosynthesis and Transpiration of Plant Communities in Controlled Environments. Acta Hortic, 1996, vol. 40, pp. 123–128.
59. Liao Y.C., Chang Chien S.W., Wang M.C., Shen Y., Seshaiah K. Relationship between Lead Uptake by Lettuce and Water-Soluble Low-Molecular-Weight Organic Acids in Rhizosphere as Influenced by Transpiration. Journal of Agricultural and Food Chemis-try, 2007, vol. 17, no 55, pp. 8640–8649.
60. Liao Y.C., Chien S.W., Wang M.C., Shen Y., Hung P.L., Das B. Effect of Transpiration on Pb Uptake by Lettuce and on Water Soluble Low Molecular Weight Organic Acids in Rhizosphere. Chemosphere, 2006, vol. 65, no. 2, pp. 343–351.
61. Kolb B., Ettre L.S. Static Headspace-Gas Chromatography: Theory and Prac-tice. Wiley, Hoboken, 2006. 350 p.
62. Jennings W. Qualitative Analysis of Flavor and Fragrance Volatiles by Glass Capillary Gas Chromatography. New York–London–Sydney–Toronto–San Francisco, 1980. 472 p.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
62
63. Heydanek M.G., McGorrin R.J. Gas Chromatography-Mass Spectroscopy Inves-tigations on the Flavor Chemistry of Oat Groats. Journal of Agricultural and Food Chemis-try, 1981, vol. 29, no 5, pp. 950–954.
64. Werkhoff P., Guntert M., Krammer G., Sommer H., Kaulen J. Vacuum Head-space Method in Aroma Research: Flavor Chemistry of Yellow Passion Fruits. Journal of Agricultural and Food Chemistry, 1998, vol. 46, pp. 1076–1093.
65. Tucker A.O., DeBaggio T. The Encyclopedia of Herbs: A Comprehensive Refer-ence to Herbs of Flavor and Fragrance. Portland–London, 2009. 604 p.
66. Handbook of Fruit and Vegetable Flavors. Ed. by Y. H. Hui. Wiley, Hoboken, 2010. 1095 p.
67. Flavor and Health Benefits of Small Fruits (ACS Symposium Series). Ed. by M. Qian, A. Rimando. Washington, 2010. 336 p.
68. Li Q., Nakadai A., Matsushima H., Miyazaki Y., Krensky A.M., Kawada T., Morimoto K. Phytoncides (Wood Essential Oils) Induce Human Natural Killer Cell Activity. Immunopharmacol. Immunotoxicol, 2006, vol. 28, no 2, pp. 319–333.
69. Dmitriev M.T., Mishchikhin V.A., Stepanov E.V. Gazokhromatograficheskoe o-predelenie fitontsidov v vozdukhe[Gas and Chromatography Fitocyd Definition in the Air]. Gigiena i sanitariya, 1983, no 7, pp. 43–45.
70. Flavor, Fragrance, and Odor Analysis. Ed. by R. Marsili. Boca Raton, 2011. 280 p.
71. Flavours and Fragrances: Chemistry, Bioprocessing and Sustainability. Ed. by R. G. Berger. Berlin–Heidelberg–New York, 2007. 664 p.
72. De Rovira D. Dictionary of Flavors. Wiley-Blackwell, Ames, Iova, 2004. 736 p.
73. Wise P.M., Olsson M.J., Cain W.S. Quantification of Odor Quality. Chemical Senses, 2000, vol. 25, no 4, pp. 429–43.
74. Persaud K., Dodd G. Analysis of Discrimination Mechanisms in the Mammalian Olfactory System Using a Model Nose. Nature, 1982, vol. 299, no 5881, pp. 352–355.
75. Jin H.J., Lee S.H., Kim T.H., Park J., Song H.S., Park T.H., Hong S. Nanovesi-cle-Based Bioelectronic Nose Platform Mimicking Human Olfactory Signal Transduc-tion. Biosensors and Bioelectronics, 2012, vol. 35, no 1, pp. 335–341.
76. Röck F., Barsan N., Weimar U. Electronic Nose: Current Status and Future Trends. Chemical Reviews, 2008, vol. 108, no 2, pp. 705–725.
77. Degenhardt D. C., Greene J. K., Khalilian A. Temporal Dynamics and Electronic Nose Detection of Stink Bug-Induced Volatile Emissions from Cotton Bolls. Psyche, 2012, vol. 2012, ID 236762, pp. 1–9.
78. Li Q., Morimoto K., Kobayashi M., Inagaki H., Katsumata M., Hirata Y., Hirata K., Shimizu T., Li Y.J., Wakayama Y., Kawada T., Ohira T., Takayama N., Kagawa T., Miyazaki Y. A Forest Bathing Trip Increases Human Natural Killer Activity and Expression of Anti-Cancer Proteins in Female Subjects. Journal of Biological Regulators & Homeostat-ic Agents, 2008, vol. 22, no 1, pp. 45–55.
79. Li Q., Morimoto K., Kobayashi M. Inagaki H., Katsumata M., Hirata Y., Hirata K., Suzuki H., Li Y.J., Wakayama Y., Kawada T., Park B.J., Ohira T., Matsui N., Kagawa T., Miyazaki Y., Krensky A.M. Visiting a Forest, but not a City, Increases Human Natural Killer Activity and Expression of Anti-Cancer Proteins. International Journal of Immuno-pathology and Pharmacology, 2008, vol. 21, no 1, pp. 117–27.
80. Lago J.H.G., Favero O.A., Romoff P. Microclimatic Factors and Phenology In-fluences in the Chemical Composition of the Essential Oils from Pittosporum undulatum Vent. Leaves. Journal of the Brazilian Chemical Society, 2006, vol. 17, no 7, pp. 1334–1338.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
63
81. Schwarz J., Gries R., Hillier K., Vickers N., Gries G. Phenology of Semiochemi-cal-Mediated Host Foraging by the Western Boxelder Bug, Boisea Rubrolineata, an Apose-matic Seed Predator. Journal of Chemical Ecology, 2009, vol. 35, no 1, pp. 58–70.
82. Puc M., Kasprzyk I. The Patterns of Corylus and Alnus Pollen Seasons and Polli-nation Periods in Two Polish Cities Located in Different Climatic Regions. Aerobiologia, 2013, vol. 29, no 4, pp. 495–511.
83. Fredrickson E.L., Estell R.E., Remmenga M.D. Volatile Compounds on the Leaf Surface of Intact and Regrowth Tarbush (Flourensia Cernua DC) Canopies. Journal of Chemical Ecology, 2007, vol. 33, no 10, pp. 1867–1875.
84. Gas Enzymology. Ed. by H. Degn, R.P. Cox, H. Toftlund. Proceedings of a Symposi-um Held at Odense University, Denmark, 1984. Dordrecht, 1985. 264 p.
85. Hanes J.M. Spring Leaf Phenology and the Diurnal Temperature Range in a Temperate Maple Forest. International Journal of Biometeorology, 2013. 10.1007/s00484-012-0603-1.
86. Geider R. Algal Photosynthesis: The Measurement of Algal Gas Exchange. New York, 1992. 256 p.
87. Maina J.N. The Gas Exchangers: Structure, Function, and Evolution of the Res-piratory Processes. Berlin, 1998. 498 p.
88. Skogestad S., Postlethwaite I. Multivariable Feedback Control: Analysis and De-sign. Wiley, Chichester–New York–Brisbane–Toronto–Singapore, 2005. 592 p.
89. Todeschini R., Consonni V. Molecular Descriptors for Chemoinformatics (Methods and Principles in Medicinal Chemistry). Weinheim, 2009. 1257 p.
90. Vershinin V.I., Derendyaev B.G., Lebedev K.S. Komp'yuternaya identifikatsiya organicheskikh soedineniy [Computer Identification of Organic Compounds]. Moscow, 2002. 197 p.
91. Hemmer M.C. Expert Systems in Chemistry Research. Boca Raton, 2007. 416 p.
92. Mitchell H.B. Multi-Sensor Data Fusion: An Introduction. Berlin, Heidelberg, 2010. 296 p.
93. D.L. Hall, McMullen S.A.H. Mathematical Techniques in Multisensor Data Fu-sion. Boston – London, 2004. 466 p.
94. Raol J. R. Multi-Sensor Data Fusion with MATLAB. Boca Raton, 2009. 568 p.
95. Bergann F. Untersuchungen über Lichtwachstum, Lichtkrümmung und Lichtab-fall bei Avena sativa mit Hilfe monochromatischen Lichtes. Planta, 1930, vol. 10, no 4, pp. 666–743.
96. Meron E. Nonlinear Physics of Ecosystems. Boca Raton, 2013. 350 p.
97. Botkin D. B. Forest Dynamics: An Ecological Model. Oxford–New York, 1993. 328 p.
98. Buongiorno J., Zhu S., Zhang D., Turner J., Tomberlin D. The Global Forest Prod-ucts Model: Structure, Estimation, and Applications. Amsterdam–Boston–London–New York–Oxford–Paris–San Diego–San Francisco–Singapore–Sydney– Tokyo, 2003. 300 p.
99. Pretzsch H. Forest Dynamics, Growth and Yield: From Measurement to Model. Heidelberg–Dordrecht–London–New York, 2010. 683 p.
100. Isermann R., Münchhof M. Identification of Dynamic Systems: An Introduction with Applications. Heidelberg–Dordrecht–London–New York, 2011. 730 p.
101. Giantomassi A. Modeling Estimation and Identification of Complex System Dynamics: Issues and Solutions. Saarbrücken, 2012. 136 p.
102. Nelles O. Nonlinear System Identification: From Classical Approaches to Neu-ral Networks and Fuzzy Models. Berlin–Heidelberg–New York, 2001. 785 p.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
64
103. A. Barrat, M. Barthélemy, A. Vespignani. Dynamical Processes on Complex Networks. Cambridge University Press, 2012. 361 p.
104. Lee M.J., Jeon S.W., Song W.K. Designation for an Ecological Network using Remote Sensing: Focusing on the North-East Asia. USA, 2013. 64 p.
105. Creswell J.W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Los Angeles–London–New Delhi–Singapore–Washington, 2013. 304 p.
106. Cserhati T. Multivariate Methods in Chromatography: a Practical Guide. Wiley, Hoboken–Chichester, 2008. 352 p.
107. Mager P.P. Multivariate Chemometrics in QSAR: a dialogue. New York–Chichester–Toronto–Brisbane–Singapure, 1988. 345 p.
108. Nendza M. Structure-Activity Relationships in Environmental Sciences. London, 1998. 288 p.
109. Bumble S. Computer Generated Physical Properties. Boca Raton, 1999. 288 p.
110. Drosos J.C., Viola-Rhenals M., Vivas-Reyes R. Quantitative Structure-Retention Relationships of Polycyclic Aromatic Hydrocarbons Gas-Chromatographic Reten-tion Indices. Journal of Chromatography A, 2010, vol. 1217, no 26, pp. 4411–4421.
111. Jönsson S., Eriksson L.A., van Bavel B. Multivariate Characterisation and Quantitative Structure-Property Relationship Modelling of Nitroaromatic Compounds. Analytica Chimica Acta, 2008, vol. 621, no 2, pp. 155–62.
112. Du H., Wang J., Hu Z., Yao X. Quantitative Structure-Retention Relationship Study of the Constituents of Saffron Aroma in SPME-GC-MS Based on the Projection Pur-suit Regression Method. Talanta, 2008, vol. 77, no 1, pp. 360–365.
113. Tan Y., Siebert K.J. Modeling Bovine Serum Albumin Binding of Flavor Com-pounds (Alcohols, Aldehydes, Esters, and Ketones) as a Function of Molecular Properties. Journal of Food Science, 2008, vol. 73, no 1, pp. 56–63.
114. Hoffmann E.A., Fekete Z.A., Rajkó R., Pálinkó I., Körtvélyesi T. Theoretical Characterization of Gas-Liquid Chromatographic Stationary Phases with Quantum Chemical Descriptors. Journal of Chromatography A, 2009, vol. 1216, no 12, pp. 2540–2547.
115. Rodriguez-Bachiller A., Glasson J. Expert Systems and Geographic Information Systems for Impact Assessment. London–New York, 2004. 408 p.
116. Warnock C. Backyard Winter Gardening: Vegetables Fresh and Simple, In Any Climate without Artificial Heat or Electricity the Way It's Been Done for 2,000 Years. Springville, 2013. 176 p.
117. Jones H.G. Plants and Microclimate: A Quantitative Approach to Environmen-tal Plant Physiology. Cambridge–New York–Melbourne, 1992. 456 p.
118. Kottek M., Grieser J., Beck C., Rudolf B., Rubel F. World Map of the Köppen–Geiger Climate Classification Updated. Meteorologische Zeitschrift, 2006, vol. 15, no 3, pp. 259–263.
119. Peel M.C., Finlayson B.L., McMahon T.A. Updated World Map of the Köppen–Geiger Climate Classification. Hydrology and Earth System Sciences, 2007, vol. 11, pp. 1633–1644.
120. Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. Ed. by M. Kalacska, G.A. Sanchez-Azofeifa. Boca Raton–London–New York, 2008. 352 p.
121. Hyperspectral Remote Sensing of Vegetation. Ed. by P.S. Thenkabail, J.G. Lyon, A. Huete. Boca Raton, 2011. 781 p.
122. Helt M.F. Vegetation Identification With LIDAR. Thes. Naval Postgraduate School. Monterey, California, 2005, 83 p.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
65
123. Lavalle M. Remote Sensing of Vegetation by Polarimetric Space Interferome-ters: Models and Methods. Saarbrücken, 2012. 220 p.
124. Gitelson A.A., Gritz Y., Merzlyak M.N. Relationships Between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assess-ment in Higher Plant Leaves. Journal of Plant Physiology, 2003, vol. 160, pp. 271 –282.
125. Biswal U.C., Biswal B., Raval M.K. Chloroplast Biogenesis: From Proplastid to Gerontoplast. Dordrecht–Boston–London, 2003. 380 p.
126. Mohammed G.H., Noland T.L., Irving D., Sampson P.H., Zarco-Tejada P.J., Miller J.R. Natural and Stress-Induced Effects on Leaf Spectral Reflectance in Ontario Spe-cies. Forest Research Report, no 156, 2000. 34 p.
127. Dasgupta S. Remote Sensing of Vegetation Water and Fire Risk: Selected Re-search Topics. Saarbrücken, 2009. 176 p.
128. Chen J.-C., Chen C.-T. Correlation Analysis Between Indices of Tree Leaf Spec-tral Reflectance and Chlorophyll Content. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, part B7, 2008, pp. 231–238.
129. Sims D.A., Gamon J.A. Relationships Between Leaf Pigment Content and Spec-tral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment, 2002, vol. 81, pp. 337–354.
130. Plant Cell Death Processes. Ed. by L.D. Nooden. Amsterdam–Boston–Heidelberg–London–New York–Oxford–Paris–San Diego–San Francisco–Singapore–Sydney–Tokyo, 2003. 392 p.
131. Chang C.-I. Hyperspectral Data Processing: Algorithm Design and Analysis. Hoboken, 2013. 1164 p.
132. Chang C.-I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York, 2003. 367 p.
133. Kim K.S. 3D Visualization of an Invariant Display Strategy for Hyperspectral Imagery. Thes. Naval Postgraduate School, Monterey, California, 2002. 67 p.
134. Becketti S. Introduction to Time Series using Stata. Texas, 2013. 741 p.
135. Gould W., Pitblado J., Poi B. Maximum Likelihood Estimation with Stata. Tex-as, 2010. 352 p.
136. Rabe-Hesketh S., Skrondal A. Multilevel and Longitudinal Modeling Using Stata. Texas, 2012, Vol. 1, 497 p.
137. Rabe-Hesketh S., Skrondal A. Multilevel and Longitudinal Modeling Using Stata. Texas, 2012, Vol. 2, 477 p.
138. Acock A.C. Discovering Structural Equation Modeling Using Stata. Texas, 2013. 304 p.
139. Kohler U., Kreuter F. Data Analysis Using Stata. Texas, 2012. 497 p.
140. Siddiqui K.J., Eastwood D.L., Liu Y-H. Spectral Pattern Recognition: the Methodology. SPIE Proceedings, 1999, vol. 3854, pp. 84–97.
141. Zachor A.S. Spectral Pattern Recognition in IR Remote Sensing. Applied Op-tics, 1983, vol. 22, no 17, pp. 2699–2703.
142. Vollmer M., Möllmann K.-P. Infrared Thermal Imaging: Fundamentals, Re-search and Applications. Weinheim, 2010. 612 p.
143. Parida L. Pattern Discovery in Bioinformatics: Theory & Algorithms. Boca Ra-ton–London–New York, 2007. 512 p.
144. Multispectral Image Processing and Pattern Recognition (Series in Machine Perception and Artificial Intelligence, 44). Ed. by J. Shen, P. S. P. Wang, T. Zhang. Singa-pore–New Jersey–London–Hong Kong, 2001. 130 p.
ISSN 0536 – 1036. ИВУЗ. «Лесной журнал». 2015. № 5
66
145. Ball G.H., Hall D.J. Isodata: a Method of Data Analysis and Pattern Classifica-tion, California, 1965. 79 p.
146. Spectral Theory and Nonlinear Analysis with Applications to Spatial Ecology. Ed. by Cano-Сasanova S., Lopez-Gomez J., Mora-Сorral C. New Jersey–London–Singapore–Beijing–Shanghai–Hong Kong–Taipei–Chennai, 2005. 276 p.
147. Jones H.G., Vaughan R.A. Remote Sensing of Vegetation: Principles, Tech-niques, and Applications. Oxford–New York, 2010. 400 p.
148. Gradov O.V., Notchenko A.V. Poluavtomaticheskaya Dendrokhronografiya Dlya Issledovaniya Morfogeneza i Teratomorfozov na Spilakh Vysshikh Rasteniy [Semi-Automatic Dendrochronology for the Study of Morphogenesis and Teratomorphosis on Sawings on the Highest Plants]. Lesotekhnicheskiy zhurnal, 2012, no 4(8), pp. 47–57.
Received on October 6, 2014
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