Fraud Warning
We officially declare that NO MONEY from authors and members of the Editorial Board IS CHARGED! A big request to ignore spam e-mails.

Address: 17 Naberezhnaya Severnoy Dviny, Arkhangelsk 163002 Russian Federation. Northern (Arctic) Federal University named after M.V.Lomonosov. Office 1425

Phone / Fax: (818-2) 21-61-18


Chromatography-Auxanometry and Chromate-Mass-Auxanometry in Forest Species Vegetation Phenological Monitoring Based on Gas and Flavor Chemical Principles with Patterns Automatic Identification

Версия для печати
Creative Commons License
These works are licensed under a Creative Commons Attribution 4.0 International License.

O.V. Gradov

Complete text of the article:

Download article (pdf, 0.9MB )






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 chromatographic 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 developmental stage phenology on the environmental factors. Since the method is based on combination 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 meteorological 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 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 during operation in different modes after pattern recognition learning (with the subsequent completion of the database) allows us to investigate and simulate not only a single plant development 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, biometeorological, bioclimatological and ecology-physiological approaches in auxanometry. The system proposed parameter variation in the course of the germination/elongation process monitoring 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 comparison. 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 parameters 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 auxanometric described system allows automatic classification of forest plants in the model plant communities 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.


O.V. Gradov, Research Assistant, Principal Engineer

Authors job

Institute of Energy Problems of Сhemical Physics of the Russian Academy of Sciences, Leninskiy pr., 38/2, Moscow, 119334,  Russia; e-mail:


flavor chemistry, gas chemistry, optical spectroscopy, auxanometry, gas chromatography, gas chromate-mass-spectrometry, phenological monitoring.


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. 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 Universit?t von 1873-1884. ?sterreichische botanische Zeitschrift, 1884, vol. 34, no 12, pp. 418–422. 9. Fritsch K. Akademien, Botanische Gesellschaften, Vereine, Kongresse etc. ?ster-reichische botanische Zeitschrift, 1905, vol. 55, no 6, pp. 245–251. 10. Literatur-?bersicht. ?sterreichische botanische Zeitschrift, 1907, vol. 57, no 2, pp. 74–85. 11. Nestler A. Das pflanzenphysiologische Institut der k. k. deutschen Universit?t in Prag. ?sterreichische botanische Zeitschrift, 1909, vol. 59, no 2, pp. 54–62. 12. Lepeschkin W. Lehrbuch der Pflanzenphysiologie auf Physikalisch-Chemischer Grundlage. Beschreibung und Erkl?rung der Wachstumserscheinungen,1925, pp. 191–242. 13. Cholodny N. ?ber 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) f?r Wachstum, photo- und geotropische Kr?mmungen. Naturwissenschaften, 1933, vol. 21, no 1, pp. 1–7. 15. Engel H., Heimann M. Weitere Untersuchungen ?ber 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., L?then H. New Methods to Analyse Auxin-Induced Growth I: Classical Auxinology Goes Arabidopsis. Plant Growth Regulation, 2000, vol. 32, no 2-3, pp. 107–114. 27. Steffens B., L?then 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., L?the H., Blatt M., B?ttger 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., M?traux 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 Relation 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 ?-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 eksperimental'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. 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. 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. Nanovesicle-Based Bioelectronic Nose Platform Mimicking Human Olfactory Signal Transduc-tion. Biosensors and Bioelectronics, 2012, vol. 35, no 1, pp. 335–341. 76. R?ck 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 & Homeostatic 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. 81. Schwarz J., Gries R., Hillier K., Vickers N., Gries G. Phenology of Semiochemical-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 Symposium 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 Design. 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 ?ber Lichtwachstum, Lichtkr?mmung 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 Products 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., M?nchhof 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. Saarbr?cken, 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. 103. A. Barrat, M. Barth?lemy, 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 Retention Indices. Journal of Chromatography A, 2010, vol. 1217, no 26, pp. 4411–4421. 111. J?nsson 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., Rajk? R., P?link? I., K?rtv?lyesi 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 K?ppen–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 K?ppen–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. 123. Lavalle M. Remote Sensing of Vegetation by Polarimetric Space Interferome-ters: Models and Methods. Saarbr?cken, 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. Saarbr?cken, 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., M?llmann 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. 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

Chromatography-Auxanometry and Chromate-Mass-Auxanometry in Forest Species Vegetation Phenological Monitoring Based on Gas and Flavor Chemical Principles with Patterns Automatic Identification