Jump to content

Draft:Roman Balabin

From Wikipedia, the free encyclopedia
Roman M. Balabin
Роман Михайлович Балабин
Born(1985-08-21)August 21, 1985
Moscow, USSR
NationalityRussia
Alma materGubkin University
Known forbiofuel analysis and melamine detection; machine learning in quantum chemistry
Scientific career
Fieldsanalytical chemistry,
vibrational spectroscopy,
computational chemistry
Institutions
Doctoral advisorRavilya Safieva
Renato Zenobi

Roman M. Balabin (Russian: Роман Михайлович Балабин; born 21 August 1985) is an analytical chemist who worked at the Georg-August University (Göttingen), Heidelberg University, and University of Basel; he was a Ph.D. student at the ETH Zurich from 2008 to 2013. He received Ph.D. in petroleum chemistry from the Gubkin University in 2013; his research interests include physical chemistry and applied spectroscopy.

Biography

[edit]

Roman M. Balabin was born in Moscow on August 21, 1985; he entered the Gubkin University in 2002. He joined the group of Martin Suhm from the Institute of Physical Chemistry (University of Göttingen) in 2007 and the laboratory of Applied Physical Chemistry (Prof. Michael Grunze) of the Institute of Physical Chemistry (PCI, University of Heidelberg) in 2008.[1] He also worked at the Department of Chemistry at the University of Basel and graduated from the Gubkin University in summer 2008 – before he became a Ph.D. student at the Analytical Chemistry group (Prof. Renato Zenobi) of the Organic Chemistry Laboratory at ETH Zurich, where he stayed till 2013. During these years he collaborated with Ryazan refinery (2005–2007),[2] Russneft oil company (Orsk, 2006), and ITMO University (Saint Petersburg, 2009); he received Ph.D. in petroleum chemistry from the Gubkin University in 2013.[3][4][5]

Melamine cyanurate: this molecular complex has been implicated as a causative agent for toxicity

Academic activity

[edit]

Infrared spectroscopy: Fuel analysis and melamine detection

[edit]
Main articles: Near-infrared spectroscopy and 2008 Chinese milk scandal

Roman Balabin and his collaborators have published a number of papers on comparing statistical methods based on near-infrared spectroscopy (NIRS), that can provide valuable functional group information about the sample,[6] for quality analysis of fuels and petroleum products.[7][8] In 2007–2008 Roman Balabin, Ravilya Safieva and Ekaterina Lomakina published two papers in Chemometrics and Intelligent Laboratory Systems where they compared modified versions of partial least squares regression (PLS) method with artificial neural networks (ANNs) for prediction of density, benzene content and ethanol content in gasoline.[9][10][11][12][13] In 2007–2011 this study was continued by a cycle of articles in Fuel and Energy & Fuels which showed that ANN/SVM[14][15] approach was superior to the linear and "quasi-nonlinear" calibration methods.[16][17][18][19][20][21][22] Two papers[23][24] in Analyst compared SVM regression with ANNs using NIRS data obtained from fourteen sets of petroleum products and benchmarked SVM for extrapolation problem (to predict the properties of samples outside the range used for the model calibration[25]):[26][27][28][29][30][31] it could be concluded that SVM-based data models have high precision and robustness[32] in small and noisy data sets ("in handling real-world, noisy, and variable spectra"[33]).[34][35] Two other papers published in Analytica Chimica Acta in 2011 were devoted to variable selection methods (including genetic algorithms[36])[37][38][39][40][41][42] and to benchmarking[43] of biodiesel classification models[20][44] that can be used for forensic identification purposes.[45]

In July 2011 Roman Balabin and Sergey Smirnov published in Talanta a paper "Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy" in which they proposed to use fourier transform[46] infrared spectroscopy to determine melamine in complex dairy products:[47] including liquid milk, infant formula, and milk powder. The authors observed no linear relationship between the vibrational spectrum of the milk sample and its melamine content, so they applied non-linear multivariate regression — such as partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), and least squares support vector machine (LS-SVM). An average of six hundred samples for each food was used for the algorithm optimization and training: the "systematic study"[48] found that, applying the right data pre-treatment and the correct multivariate techniques, a limit of detection (LOD) below 1 ppm (0.76 ± 0.11 ppm[49]) could be reached. Furthermore, Balabin and Smirnov showed that Poly-PLS is able to predict only low melamine concentrations (<15 ppm).[50] So, the robust determination of melamine adulteration in infant formula and dairy milk ("safety assessment of dairy products"[51]) is possible with infrared-based analytical techniques.[52][53] "The application of NIR spectroscopy and multivariate modeling have proved to be very successful",[54] that was considered by professor Xiaonan Lu as a "significant achievement",[48] since the total time for melamine detection using spectroscopy methods were less than for almost all other previous methods[47] – although "expensive statistical approaches and special software complex" were needed to achieve the task.[55]

Quantum chemistry: Machine learning and BSSE

[edit]
Nicotine molecular orbitals: HOMO/LUMO from JCP (2009)
Main articles: Machine learning and Basis set superposition error

In August 2009 The Journal of Chemical Physics published online a paper "Neural network approach to quantum-chemistry data" authored by Roman Balabin and Ekaterina Lomakina; there they exploited the idea of a large[56] ANN-based quantum chemical database — 208 organic molecules containing only carbon, hydrogen, fluorine, oxygen and nitrogen — and different sets of molecular descriptors that could predict the density functional theory (DFT) energies without having to undertake a detailed DFT calculation on the system of interest,[57][58][59] since machine learning provides a means to convert the large volume of diverse, complex data into actionable knowledge.[60][61] In particular they applied neural networks to predict energies of the molecules ("QSPRs for basis-set effects"[62]);[63] the estimation of DFT energies with converged basis sets using lower level electronic structure calculations[64] became a part of the organic chemistry community approach not only for enhancing the accuracy of hard modeling (e.g. ab initio calculations[65]) but also for making fast and accurate property predictions:[66][67] a possible scenario in which an algorithm decides or suggests internal parameters (or type) of density functional to be used for a given calculation.[68] Balabin and Lomakina continued their collaboration by publishing in Physical Chemistry Chemical Physics[66][62] a paper "Support vector machine regression (LS-SVM) — an alternative..." (June 2011) where SVMs were compared with ANNs for the basis-set effects estimation.[69][70][71]

In October 2008 in The Journal of Chemical Physics and in March 2011 in Molecular Physics Balabin published "considerably detailed"[72] papers on the effects of basis set on intramolecular basis set superposition error (BSSE),[73][74][75] where he noted a requirement to account for this effect when high accuracy theoretical results are needed, particularly for long-chain n-alkanes:[76][77][78] in other words he reported an eminent ("dramatic"[79]) intramolecular BSSE effect on the calculated relative stability of alkane conformers.[80][81] The magnitude of the BSSE is comparable to and in some cases even larger than the energy difference between the conformers, so BSSE can prevent quantum methods with incomplete basis sets from accurately modelling potential energy surfaces and thereby preclude agreement with experimental observations:[82] even with the large cc-pVTZ basis set, that greatly reduces the effect,[83] there is still a noticeable BSSE correction.[84] This project also included a theoretical study of peptides (oligoglycines) which has demonstrated that, when accounting for BSSE, the predicted stabilities of α-helices, β-strands, and γ-turns are reduced noticeably — even if helices remain the most stable conformation.[85]

Amino acids

[edit]
Main article: Protein structure

A cycle of works[86][87][88] on the structures of the simplest amino acids (glycine and alanine) was started by Balabin in September 2009 with publication of a theoretical paper "Conformational equilibrium in glycine" in Chemical Physics Letters: ab initio computations based on focal-point analysis (FPA) scheme were performed on glycine (Gly) conformers.[89][90] A year later an experimental[91] jet-cooled glycine Raman spectrum — that showed six molecular vibrations in a region between 160 cm−1 and 450 cm−1 — was published in Journal of Physical Chemistry Letters: all the peaks could be "matched up with vibrations from the three lowest energy conformations by comparison to the computed frequencies".[92][93] Non-equilibrium conditions of jet-cooled molecular beam allowed to observe one "elusive" — previously experimentally unknown — conformation of Gly:[94] a conformer that is formed as a result of a complex interplay between intramolecular hydrogen bond and steric factors.[95][87][96] Equilibrium gas-phase Raman study, published in January 2012 in Physical Chemistry Chemical Physics — allowed an estimation of the relative enthalpies of three glycine rotamers by decomposition of a broad, unresolved spectral band:[97] however, the thermodynamic characterization was based on van’t Hoff equation, whose absolute accuracy might be questionable.[98][99]

Two new conformers of free alanine reported in PCCP (2010).

In 2010, in addition to a theoretical study,[100] Balabin recorded the jet-cooled Raman spectrum of alanine: he reported observation of four conformers of this amino acid, including two new ones — that had not been reported in previous studies[101][102] — but the unambiguous identification of this pair was still questionable.[103] As a part of the cycle and in a search of gaseous zwitterion he also examined the glycine-one water complex using vibrational spectroscopy: in addition to the most stable conformation, he was able to detect a small amount of two others by recording а low-frequency Raman spectrum (below 500 cm−1).[104][105] Professor Steven Bachrach thought that "an interesting side note [of the study was] that anharmonic corrections were necessary in order to match up the computed... frequencies with the experimental values".[106]

Zenobi group

[edit]

As a part of Zenobi group at ETH Zurich[107][108][109] Roman Balabin was a co-author of a number of papers on theory and practice of mass spectrometry (MS). In 2010 a paper of Liang Zhu and HuanWen Chen applied EESI method to classify beer samples according to their type by principal component analysis (PCA);[110][111][112] Wai Siang Law "successfully" used the same combination of methods to study olive oils.[113][114] In 2011 Konstantin Barylyuk published a series of "careful"[115] MS experiments, complemented by DFT calculations, on synthetic supramolecular complexes, which interact with β-cyclodextrins solely through hydrophobic forces: "the study provided unambiguous evidence that hydrophobic interactions can be preserved in the gas phase"[116] and suggested that other macromolecular associations held together exclusively by hydrophobic interactions may survive without solvent[117][118][119][120][121][122] — at least on the millisecond timescales.[123][124] Andrea Amantonico and Pawel Urban[125][126][127] studied the profile of selected ("only a few"[128]) metabolites containing phosphate groups in single cells of "simple algae"[129] (Closterium)[130] using negative-mode MALDI-MS:[131][132][133][134][135] when combined with SVM method, this "proof-of-principle"[136] experiment made it possible to observe single cells[137][138] in distinct metabolic levels and classify individuals within cell populations;[139] the study itself contributed to the growing body of research suggesting that cell populations — previously assumed to be largely homogeneous — are in fact made up of subpopulations.[140][141][142][143]

List of works

[edit]
"Development of express methods" (2013)

Ph.D. thesis

[edit]
  • Балабин, Роман Михайлович. Development of express methods based on vibrational spectroscopy for analysis of petroleum products and petrochemicals = Создание экспресс-методов анализа продуктов нефтепереработки и нефтехимии на основе колебательной спектроскопии : диссертация ... кандидата технических наук : 02.00.13 (ru) / Р. М. Балабин; [Место защиты: Рос. гос. ун-т нефти и газа им. И.М. Губкина]. — Москва, 2013. — 116 с.: ил.

Selected publications

[edit]
List of selected publications

See also

[edit]

References

[edit]
  1. ^ Edigarev, 2019
  2. ^ Zaitseva, Pashinina, 2019
  3. ^ Krutskikh, 2019
  4. ^ Demidova: Civic Chamber, 2019
  5. ^ Vorontsova, 2019
  6. ^ Vempatapu, Kanauji, 2017, pp. 8–9, 11
  7. ^ Marques et al., 2014, pp. 100–103, 106–107
  8. ^ Skvaril, Kyprianidis, Dahlquist, 2017, Characterization of biodiesel, pp. 683, 685, 709–716, 720–727
  9. ^ Shi H., Yu P., 2018, pp. 407, 417
  10. ^ Martins, Gonçalves, Peres, 2011, pp. 57–70
  11. ^ Khanmohammadi et al., 2012, pp. 140, 149
  12. ^ Shao X. et al., 2010, pp. 1663, 1665
  13. ^ Gutiérrez, Muñoz, Del Valle, 2011, pp. 258–270
  14. ^ Wakiru et al., 2019, pp. 117, 130
  15. ^ Motai, 2015, pp. 9–10, 33
  16. ^ Vershinin, 2011, pp. 1015, 1019
  17. ^ Curteanu, 2011, pp. 103–118
  18. ^ Giwa, 2016, pp. 87, 103
  19. ^ Luna, Lima, Alberton, 2016, pp. 37, 44
  20. ^ a b Jha S. Kr. et al., 2017, pp. 310, 316
  21. ^ Chen Q. et al., 2017, pp. 108–112
  22. ^ Butler et al., 2016, pp. 675–676, 686
  23. ^ Harrington, 2017, pp. 2, 14
  24. ^ Tange et al., «Benchmarking», 2017, pp. 382, 389
  25. ^ Baird, Oja, 2016, pp. 42–43, 47
  26. ^ Pasquini, 2018, pp. 18–19, 33
  27. ^ Cheng Ch. et al., 2015, pp. 1060, 1067
  28. ^ Constantinescu et al., 2015, pp. 385, 391
  29. ^ Gromski et al., 2015, pp. 12, 21
  30. ^ Lavine, Workman, 2013, pp. 711, 714
  31. ^ Hoehse et al., 2012, pp. 1447–1448, 1450
  32. ^ Palou et al., 2017, pp. 120, 126
  33. ^ Dingari et al., 2012, pp. 2688, 2692, 2694
  34. ^ Khayyam, Golkarnarenji, Jazar, 2018, p. 375
  35. ^ Kroll et al., 2017, pp. 2607–2608, 2613
  36. ^ Byrne et al., 2016, pp. 1867–1868, 1878
  37. ^ Sousa, Lopes, 2013, pp. 392, 413
  38. ^ Hanif et al., 2018, pp. 2073, 2081
  39. ^ Rammal et al., 2017, pp. 154, 160
  40. ^ Cetó, Voelcker, Prieto-Simón, 2016, pp. 611, 626
  41. ^ Liu D., Sun D.-W., Zeng X.-A., 2014, pp. 308, 320
  42. ^ Carreiro Soares et al., 2013, pp. 87, 98
  43. ^ Gharagheizi et al., 2011, pp. 4994, 5021
  44. ^ Rocha et al., 2012, pp. 12–31
  45. ^ Yang Z. et al., 2016, pp. 573, 633
  46. ^ Craig, Franca, Irudayaraj, 2015, pp. 180, 186
  47. ^ a b Fu X., Ying Y., 2014, pp. 1918–1922
  48. ^ a b Lu X., 2014, pp. 177–178, 187
  49. ^ Jha S. N. et al., 2015, pp. 1672–1682
  50. ^ Ritota, Manzi, 2017, pp. 140–141
  51. ^ Qu J.-H. et al., 2015, pp. 1940, 1949–1951
  52. ^ Panikuttira, O’Donnell, 2018, p. 840
  53. ^ Ni W., Nørgaard, Mørup, 2014, pp. 2, 7, 14
  54. ^ Sørensen, Khakimov, Engelsen, 2016, pp. 47, 49
  55. ^ Jawaid et al., 2013, p. 3067
  56. ^ Hajinazar, Shao, Kolmogorov, 2017, pp. 1, 12
  57. ^ Raff et al., 2012, pp. 234–236, 261
  58. ^ Sarkar, Bhattacharyya, 2017, 8.8. Neural Networks in Optimization
  59. ^ doi:10.1088/2632-2153/ab7d30
  60. ^ Iwasaki, Kusne, Takeuchi, 2017, pp. 1, 9
  61. ^ Li Y., Yu J., 2014, pp. 7298, 7314
  62. ^ a b Montavon et al., 2013, pp. 3, 13
  63. ^ Hansen et al., 2013, pp. 3405, 3418
  64. ^ Behler, 2011, pp. 17933, 17954
  65. ^ Kusne et al., 2014, pp. 1, 6
  66. ^ a b Pyzer-Knapp et al., 2015, pp. 211, 216
  67. ^ Granda, Jurcza, 2014, pp. 12369, 12372
  68. ^ Mosquera et al., 2017, pp. 160, 162
  69. ^ Behler, 2017, pp. 12830, 12839
  70. ^ Lilienfeld, 2018, pp. 4165, 4168
  71. ^ doi:10.1038/s41467-020-17995-8
  72. ^ Gruzman, Karton, Martin, 2009, pp. 11976, 11983
  73. ^ Hameed, Khan, van Mourik, 2018, pp. 1237–1238, 1243
  74. ^ Pele et al., 2011, pp. 8, 12
  75. ^ Hernandez-Castillo et al., 2017, pp. 57—58
  76. ^ Liu Ch., McGivern, Manion, Wang H., 2016, pp. 8067, 8074
  77. ^ Plumley, Dannenberg, 2011, pp. 10563, 10566
  78. ^ Fadda, Woods, 2013, pp. 863, 865
  79. ^ Ashouri, Maghari, Karimi-Jafari, 2015, pp. 13294—13295, 13300
  80. ^ Sladek, Holka, Tvaroška, 2015, pp. 18505—18506, 18513
  81. ^ Jensen, 2017, pp. 2, 6
  82. ^ Faver, Zheng Zh., Merz, 2012, pp. 7795, 7799
  83. ^ Hua Sh., Xu L., Li W., Li Sh., 2011, pp. 11465, 11469
  84. ^ Bachrach, 2014, pp. 120—121, 183
  85. ^ Alparone, 2013, pp. 1—2, 5
  86. ^ Kim J.-Y. et al., 2014, pp. 16352, 16359–16360
  87. ^ a b Gloaguen, Mons, 2015, pp. 227–229, 246, 260
  88. ^ doi:10.1021/acs.chemrev.9b00547
  89. ^ Ghosh, Choi T., Choi C., 2016, pp. 3, 11
  90. ^ Bazsó, Magyarfalvi, Tarczay, 2012, pp. 33–34, 42
  91. ^ Puzzarini, Biczysko, 2014, pp. 44, 63
  92. ^ Bachrach, 2014, pp. 66—68, 94
  93. ^ Liu F., Yu J., Huang Y.-R., 2018, pp. 1, 5, 8
  94. ^ Barone et al., PCCP, 2013, pp. 10095, 10098–10100, 10109
  95. ^ Cormanich, Rittner, Bühl, 2015, pp. 13052, 13059
  96. ^ Sacchi, Jenkins, 2014, pp. 6103, 6107
  97. ^ Bazsó, Magyarfalvi, Tarczay, 2012, pp. 34, 42
  98. ^ Barone, Biczysko, Carnimeo, 2014, pp. 288, 318
  99. ^ Barone et al., PCCP, «Glycine conformers», 2013, pp. 1358–1362
  100. ^ Karton et al., 2014, pp. 2, 7–8, 11, 13
  101. ^ Farrokhpour, Fathi, De Brito, 2012, pp. 7004–7015
  102. ^ Tia M. et al., 2014, pp. 2770–2771, 2777
  103. ^ Nunes et al., 2013, pp. 2–6, 12
  104. ^ Gadre, Yeole, Sahu, 2014, pp. 12156–12157, 12172
  105. ^ Kim J.-Y. et al., 2014, pp. 16353, 16360
  106. ^ Bachrach, 2014, pp. 490, 503
  107. ^ Cahill et al., 2015, pp. 8039, 8045
  108. ^ Czar, Jockusch, 2015, pp. 126, 130, 134
  109. ^ Nespovitaya, 2014, pp. i, 71, 89, 164
  110. ^ Vaclavik et al., 2014, pp. 55, 71
  111. ^ Blanco, Andrés-Iglesias, Montero, 2014, pp. 1381–1385, 1388
  112. ^ Šedo, Márová, Zdráhal, 2012, pp. 474, 478
  113. ^ Doezema et al., 2012, pp. 2931, 2938
  114. ^ Li X. et al., 2011, pp. 1010–1025
  115. ^ Goldstein et al., 2014, pp. 10, 15
  116. ^ Hopper, Robinson, 2014, pp. 14008, 14014
  117. ^ Kaltashov, Eyles, 2012, pp. 90, 119
  118. ^ Dyck, Konijnenberg, Sobott, 2017, pp. 208, 229
  119. ^ Przybylski, Bonnet, Cézard, 2015, pp. 19289, 19304
  120. ^ Konermann, Vahidi, Sowole, 2014, pp. 226, 232
  121. ^ Lemaur et al., 2013, pp. 959–960, 968
  122. ^ Maple et al., 2012, pp. 838, 849
  123. ^ Wyttenbach et al., 2014, pp. 185, 194
  124. ^ Fernandes et al., 2014, pp. 853, 860
  125. ^ Knolhoff et al., 2013
  126. ^ Vertes, Shrestha, Nemes, 2013
  127. ^ Onjiko, Portero, Nemes, 2018
  128. ^ Misra, Assmann, Chen S., 2014, pp. 638, 641, 646
  129. ^ Sims, Manteiga, Lee K., 2013, pp. 936, 939
  130. ^ Tanaka, Liang, Maeda, 2017, pp. 580, 584
  131. ^ Bergman, Lanekoff, 2017, pp. 3639, 3646
  132. ^ Moussaieff et al., 2013, pp. E1232, E1241
  133. ^ He X. et al., 2014, pp. 95, 97
  134. ^ Klepárník, Foret, 2013, pp. 16, 20
  135. ^ Fujii et al., 2015, pp. 1445, 1456
  136. ^ Gao D. et al., 2013, pp. 3313, 3320
  137. ^ Yang Y. et al., 2017, pp. 14, 25
  138. ^ Cole R. H. et al., 2017, pp. 8732–8733
  139. ^ Mao S. et al., 2018, pp. 44, 55
  140. ^ Cook, Nielsen, 2017, pp. 6, 14
  141. ^ Cole L. M., Clench, 2015, pp. 338, 341
  142. ^ Galler et al., 2014, pp. 1254, 1269
  143. ^ Passarelli, Ewing, 2013, pp. 854, 858

Literature

[edit]
Newspaper articles
Books
  • Panikuttira B., O’Donnell C. P. Process Analytical Technology for the Fruit Juice Industry // Fruit Juices: Extraction, Composition, Quality and Analysis / eds. Gaurav Rajauria, Brijesh K. Tiwari. — London: Elsevier, Academic Press, 2018. — P. 835–847. — xxx, 878 p. — ISBN 9780128024911. — ISBN 9780128022306. — ISBN 0128024917. — ISBN 0128022302. — DOI:10.1016/b978-0-12-802230-6.00040-0.
  • Craig A. P., Franca A. S., Irudayaraj J. Vibrational spectroscopy for food quality and safety screening // High Throughput Screening for Food Safety Assessment: Biosensor Technologies, Hyperspectral Imaging and Practical Applications / eds. A. K. Bhunia, M. S. Kim, C. R. Taitt. — Elsevier, 2015. — P. 165–194. — ISBN 9780857098016. — ISBN 978-085709807-8. — DOI:10.1016/b978-0-85709-801-6.00007-1.
  • Lu X. Recent developments in infrared spectroscopy for the detection of food chemical hazards // Food Chemical Hazard Detection: Development and Application of New Technologies / ed. S. Wang. — Chichester: John Wiley & Sons, 2014. — P. 173–189. — ISBN 9781118488553. — ISBN 9781118488591. — DOI:10.1002/9781118488553.ch5.
  • Raff L., Komanduri R., Hagan M., Bukkapatnam S. Other Applications of NNs to Quantum Mechanical Problem // Neural networks in chemical reaction dynamics. — NY: Oxford University Press, 2012. — P. 215—243. — xiv, 283 p. — ISBN 9780199909889. — ISBN 0199909881.
  • Sarkar K., Bhattacharyya S. P. Soft Computing in Chemical and Physical Sciences : a Shift in Computing Paradigm. — 1st ed. — Boca Raton, FL: CRC Press, 2017. — xvi, 418 p. — ISBN 9781315152899. — ISBN 9781498755955. — ISBN 1315152894. — ISBN 149875595X.
  • Bachrach S. M. Computational Organic Chemistry. — 2nd ed. — John Wiley & Sons, 2014. — 1070 p. — ISBN 9781118671221. — ISBN 978-111867119-1. — ISBN 978-111829192-4. — ISBN 1118671228. — DOI:10.1002/9781118671191.
  • Barone V., Biczysko M., Carnimeo I. Computational Tools for Structure, Spectroscopy and Thermochemistry: Computational and Experimental Tools // Understanding Organometallic Reaction Mechanisms and Catalysis / ed. V. P. Ananikov. — Weinheim: Wiley-VCH, 2014. — P. 249–320. — ISBN 9783527678211. — ISBN 9783527335626. — DOI:10.1002/9783527678211.ch10.
  • Puzzarini Cr., Biczysko M. Computational Spectroscopy Tools for Molecular Structure Analysis // Structure Elucidation in Organic Chemistry: The Search for the Right Tools / eds. M. M. Cid, J. Bravo. — Weinheim: Wiley-VCH, 2014. — P. 27–64. — ISBN 9783527664610. — ISBN 9783527333363. — DOI:10.1002/9783527664610.ch2.
  • Gloaguen E., Mons M. Isolated Neutral Peptides // Gas-Phase IR Spectroscopy and Structure of Biological Molecules / eds. Anouk Rijs, Jos Oomens. — Cham: Springer, 2015. — P. 225–270. — ix, 406 p. — (Topics in Current Chemistry, Vol. 364; ISSN 0340-1022). — ISBN 9783319192031. — ISBN 9783319192048. — ISBN 978-3-319-37865-7. — DOI:10.1007/128 2014 580.
  • Martins F. G., Gonçalves D. J. D., Peres J. Artificial neural networks in environmental sciences and chemical engineering // Focus on artificial neural networks / ed. J. A. Flores. — NY: Nova Science Publishers, 2011. — P. 55—74. — xiv, 410 p. — ISBN 9781619421004. — ISBN 1619421003. — ISBN 9781613242858. — ISBN 1613242859.
  • Khanmohammadi M., Fard H. G., Garmarudi A. B., De La Guardia M. Determination of gasoline quality parameters by FTIR spectroscopy and chemometrics // Infrared Spectroscopy: Theory, Developments and Applications / ed. Daniel Cozzolino. — Nova Science Publishers, 2014. — P. 287—306. — 557 p. — (Chemistry research and applications). — ISBN 9781629485218. — ISBN 1629485217.
    • Khanmohammadi M., Garmarudi A. B., De La Guardia M. Characterization of petroleum-based products by infrared spectroscopy and chemometrics // TrAC Trends in Analytical Chemistry. — 2012. — May (vol. 35). — P. 135–149. — DOI:10.1016/j.trac.2011.12.006.
  • Gutiérrez J. M., Muñoz R., Del Valle M. Wavelet neural networks: A recent strategy for processing complex signals applications to chemistry // Focus on Artificial Neural Networks / ed. J. A. Flores. — NY: Nova Science Publishers, 2011. — P. 257—275. — xiv, 410 p. — ISBN 9781619421004. — ISBN 1619421003. — ISBN 9781613242858. — ISBN 1613242859.
  • Curteanu S. Different types of applications performed with different types of neural networks // Artificial neural networks / ed. S. J. Kwon. — NY: Nova Science Publishers, 2011. — P. 101—136. — xiii, 426 p. — ISBN 9781617616976. — ISBN 1617616974.
  • Giwa S. O. Applications of Artificial Neural Networks to Predict Biodiesel Fuel Properties from Fatty Acid Constituents // Artificial Neural Networks: New Research / ed. Gayle Cain. — Nova Science Publishers, 2016. — 221 p. — (Computer science, technology and applications). — ISBN 9781634859646. — ISBN 978-163485979-0. — ISBN 1634859642.
  • Chen Q., Zhai Z., You X., Zhang T. Inverse design methods for the built environment. — Abingdon: Taylor and Francis, 2017. — 248 p. — ISBN 9781315468006. — ISBN 9781315467993. — ISBN 131546800X. — ISBN 1315467992.
  • Motai Y. Data-Variant Kernel Analysis. — John Wiley & Sons, 2015. — 248 p. — (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control). — ISBN 9781119019329. — ISBN 978-111901935-0. — ISBN 111901932X. — DOI:10.1002/9781119019350.
  • Khayyam H., Golkarnarenji G., Jazar R. N. Limited Data Modelling Approaches for Engineering Applications // Nonlinear Approaches in Engineering Applications / eds. Liming Dai, Reza N. Jazar. — Cham: Springer International Publishing, 2018. — P. 345—379. — xxvi, 456 p. — ISBN 9783319694795. — ISBN 9783319694801. — DOI:10.1007/978-3-319-69480-1 12.
  • Constantinescu S., Sturla S. J., Marra G., Wollscheid B., Beerenwinkel N. Computational Data Integration in Toxicogenomics // Computational Systems Toxicology / eds. Julia Hoeng, Manuel C. Peitsch. — NY: Springer, Humana Press, 2015. — P. 371–392. — x, 430 p. — (Methods in Pharmacology and Toxicology; ISSN 1557-2153). — ISBN 9781493927784. — DOI:10.1007/978-1-4939-2778-4 15.
  • Sousa C. C., Lopes J. A. Infrared spectroscopy detection coupled to chemometrics to characterize foodborne pathogens at a subspecies level // Mathematical and Statistical Methods in Food Science and Technology / eds. D. Granato, G. Ares. — Chichester: John Wiley & Sons, 2013. — P. 385—418. — ISBN 9781118434635. — ISBN 9781118433683. — DOI:10.1002/9781118434635.ch20.
  • Rocha W. F. C., Nogueira R., Vaz B. G., Fidelis C. H. V., Romão W. Review on quality control in science // Quality control: Developments, methods and applications / ed. J. A.Orosa. — Nova Science Publishers, 2012. — P. 1—46. — 197 p. — ISBN 978-162257139-0.
  • Yang Z., Wang Z., Hollebone B.P., Yang C., Brown C. E. Forensic Fingerprinting of Biodiesel and its Blends with Petroleum Oil // Standard Handbook Oil Spill Environmental Forensics: Fingerprinting and Source Identification / eds. S. A. Stout, Z. Wang. — 2nd ed. — Academic Press, Elsevier, 2016. — P. 565—640. — 1142 p. — ISBN 978-012809659-8. — ISBN 978-0-12-803832-1. — ISBN 9780128039021. — ISBN 0128038322. — ISBN 0128039027. — DOI:10.1016/B978-0-12-809659-8.00012-7.
  • Nespovitaya N. New insights to functional CRF, somatostatin 14, and β-endorphin aggregation / recom. Roland Riek, Paola Picotti. — Zurich: ETH Library, 2014. — xi, 203 p. — DOI:10.3929/ethz-a-010259277.
  • Vaclavik L., Čajka T., Zhou W., Wang P. G. Survey of Mass Spectrometry-Based High-Throughput Methods in Food Analysis // High-Throughput Analysis for Food Safety / eds. P. G. Wang, M. F. Vitha, J. F. Kay. — Hoboken, NJ: John Wiley & Sons, 2014. — P. 15–72. — ISBN 9781118907924. — ISBN 9781118396308. — DOI:10.1002/9781118907924.ch02.
  • Kaltashov I. A., Eyles S. J. Mass spectrometry in structural biology and biophysics : architecture, dynamics, and interaction of biomolecules. — 2nd ed. — Hoboken, NJ: Wiley, 2012. — 289 p. — ISBN 9781118232125. — ISBN 1118232127. — ISBN 9781118232088. — ISBN 1118232089. — ISBN 9781280590283.
  • Dyck J. F., Konijnenberg A., Sobott F. Native Mass Spectrometry for the Characterization of Structure and Interactions of Membrane Proteins // Membrane Protein Structure and Function Characterization: Methods and Protocols / ed. Jean-Jacques Lacapere. — NY: Springer, 2017. — P. 205–232. — (Methods in Molecular Biology, Vol. 1635, ISSN 1064-3745). — ISBN 9781493971497. — ISBN 9781493971510. — DOI:10.1007/978-1-4939-7151-0 11.
  • Knolhoff A. M., Nemes P., Rubakhin S. S., Sweedler J. V. Mass spectrometry–based methodologies for single-cell metabolite detection and identification // Methodologies for Metabolomics : Experimental Strategies and Techniques / eds. Norbert W. Lutz, Jonathan V. Sweedler, Ron A. Wevers. — Cambridge: Cambridge University Press, 2013. — P. 119—139. — 642 p. — ISBN 9781139615334. — ISBN 9781283870375. — ISBN 9780511996634. — ISBN 9780521765909. — ISBN 9781139624633.
  • Onjiko R. M., Portero E. P., Nemes P. Single-cell Metabolomics with Capillary Electrophoresis–Mass Spectrometry // Capillary Electrophoresis–Mass Spectrometry for Metabolomics / ed. Rawi Ramautar. — Cambridge: Royal Society of Chemistry, 2018. — P. 209–224. — ISBN 9781788011044. — ISBN 978-1-78801-273-7. — ISBN 978-1-78801-484-7. — DOI:10.1039/9781788012737-00209.
  • Tanaka T., Liang Y., Maeda Y. Lipidomic analysis of marine microalgae // Marine OMICS : principles and applications / ed. S.-K. Kim. — Boca Raton: CRC Press, 2017. — P. 575—588. — xx, 724 p. — ISBN 9781315372303. — ISBN 9781482258219. — ISBN 1315372304. — ISBN 1482258218. — DOI:10.1201/9781315372303.
  • Luna A. S., Lima E. R. A., Alberton K. P. F. Applications of Artificial Neural Networks in Chemistry and Chemical Engineering // Artificial Neural Networks: New Research / ed. Gayle Cain. — Nova Science Publishers, 2016. — 221 p. — (Computer science, technology and applications). — ISBN 9781634859646. — ISBN 978-163485979-0. — ISBN 1634859642.
  • Corrosion of Pipeline Steel // Fuels and Lubricants Handbook: Technology, Properties, Performance, and Testing, 2nd Edition / eds. George E. Totten, Rajesh J. Shah, David R. Forester, 978-0-8031-7089-6.
Reviews
  • Ritota M., Manzi P. Melamine detection in milk and dairy products: Traditional analytical methods and recent developments // Food Analytical Methods. — 2017. — July (vol. 11, iss. 1). — P. 128–147. — DOI:10.1007/s12161-017-0984-1.
  • Sørensen K. M., Khakimov B., Engelsen S. B. The use of rapid spectroscopic screening methods to detect adulteration of food raw materials and ingredients // Current Opinion in Food Science. — 2016. — August (vol. 10). — P. 45–51. — DOI:10.1016/j.cofs.2016.08.001.
  • Fu X., Ying Y. Food Safety Evaluation Based on Near Infrared Spectroscopy and Imaging: A Review // Critical Reviews in Food Science and Nutrition. — 2014. — June (vol. 56, iss. 11). — P. 1913–1924. — DOI:10.1080/10408398.2013.807418.
  • Jha S. N., Jaiswal P., Grewal M. K., Gupta M., Bhardwaj R. Detection of Adulterants and Contaminants in Liquid Foods—A Review // Critical Reviews in Food Science and Nutrition. — 2015. — May (vol. 56, iss. 10). — P. 1662–1684. — DOI:10.1080/10408398.2013.798257.
  • Qu J.-H., Liu D., Cheng J.-H., Sun D.-W., Ma J. Applications of Near-infrared Spectroscopy in Food Safety Evaluation and Control: A Review of Recent Research Advances // Critical Reviews in Food Science and Nutrition. — 2015. — May (vol. 55, iss. 13). — P. 1939–1954. — DOI:10.1080/10408398.2013.871693.
  • Li Y., Yu J. New Stories of Zeolite Structures: Their Descriptions, Determinations, Predictions, and Evaluations // Chemical Reviews. — 2014. — May (vol. 114, iss. 14). — P. 7268–7316. — DOI:10.1021/cr500010r.
  • Pyzer-Knapp E. O., Suh Ch., Gómez-Bombarelli R., Aguilera-Iparraguirre J., Aspuru-Guzik A. What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery // Annual Review of Materials Research. — 2015. — July (vol. 45, iss. 1). — P. 195–216. — DOI:10.1146/annurev-matsci-070214-020823.
  • Mosquera M. A., Fu B., Kohlstedt K. L., Schatz G. C., Ratner M. A. Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges // ACS Energy Letters. — 2017. — December (vol. 3, iss. 1). — P. 155–162. — DOI:10.1021/acsenergylett.7b01058.
  • Behler J. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems // Angewandte Chemie International Edition. — 2017. — August (vol. 56, iss. 42). — P. 12828–12840. — DOI:10.1002/anie.201703114.
    • Behler J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations // Physical Chemistry Chemical Physics. — 2011. — Vol. 13, iss. 40. — P. 17930–17955. — DOI:10.1039/c1cp21668f.
  • Lilienfeld A. O. Quantum Machine Learning in Chemical Compound Space // Angewandte Chemie International Edition. — 2018. — March (vol. 57, iss. 16). — P. 4164–4169. — DOI:10.1002/anie.201709686.
  • Gadre S. R., Yeole S. D., Sahu N. Quantum Chemical Investigations on Molecular Clusters // Chemical Reviews. — 2014. — December (vol. 114, iss. 24). — P. 12132–12173. — DOI:10.1021/cr4006632.
  • Kim J.-Y., Ahn D.-S., Park S.-W., Lee S. Gas phase hydration of amino acids and dipeptides: effects on the relative stability of zwitterion vs. canonical conformers // RSC Advances. — 2014. — Vol. 4, iss. 31. — P. 16352–16361. — DOI:10.1039/c4ra01217h.
  • Marques D. B., Barradas Filho A. O., Romariz A. R. S., Viegas I. M. A., Luz D. A. Recent Developments on Statistical and Neural Network Tools Focusing on Biodiesel Quality // International Journal of Computer Science and Application. — 2014. — Vol. 3, iss. 3. — P. 97-110. — DOI:10.14355/ijcsa.2014.0303.01.
  • Skvaril J., Kyprianidis K. G., Dahlquist E. Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review // Applied Spectroscopy Reviews. — 2017. — September (vol. 52, iss. 8). — P. 675–728. — DOI:10.1080/05704928.2017.1289471.
  • Shi H., Yu P. Exploring the potential of applying infrared vibrational (micro)spectroscopy in ergot alkaloids determination: Techniques, current status, and challenges // Applied Spectroscopy Reviews. — 2018. — Vol. 53, iss. 5. — P. 395—419. — DOI:10.1080/05704928.2017.1363771.
  • Shao X., Bian X., Liu J., Zhang M., Cai W. Multivariate calibration methods in near infrared spectroscopic analysis // Analytical Methods. — 2010. — November (vol. 2, iss. 11). — P. 1662–1666. — DOI:10.1039/c0ay00421a.
  • Jha S. Kr., Bilalovic J., Jha A., Patel N., Zhang H. Renewable energy: Present research and future scope of Artificial Intelligence // Renewable and Sustainable Energy Reviews. — 2017. — September (vol. 77). — P. 297–317. — DOI:10.1016/j.rser.2017.04.018.
  • Wakiru J. M., Pintelon L., Muchiri P. N., Chemweno P. K. A review on lubricant condition monitoring information analysis for maintenance decision support // Mechanical Systems and Signal Processing. — 2019. — March (vol. 118). — P. 108–132. — DOI:10.1016/j.ymssp.2018.08.039.
  • Pasquini C. Near infrared spectroscopy: A mature analytical technique with new perspectives — A review // Analytica Chimica Acta. — 2018. — October (vol. 1026). — P. 8—36. — DOI:10.1016/j.aca.2018.04.004.
  • Kroll P., Hofer A., Ulonska S., Kager J., Herwig Ch. Model-Based Methods in the Biopharmaceutical Process Lifecycle // Pharmaceutical Research. — 2017. — December (vol. 34, iss. 12). — P. 2596–2613. — DOI:10.1007/s11095-017-2308-y.
  • Cheng Ch., Sa-Ngasoongsong A., Beyca O., Le Tr., Yang H. Time series forecasting for nonlinear and non-stationary processes: a review and comparative study // Institute of Industrial Engineers (IIE) Transactions. — 2015. — October (vol. 47, iss. 10). — P. 1053–1071. — DOI:10.1080/0740817x.2014.999180.
  • Gromski P. S., Muhamadali H., Ellis D. I., Xu Y., Correa E., Turner M. L., Goodacre R. A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding // Analytica Chimica Acta. — 2015. — June (vol. 879). — P. 10–23. — DOI:10.1016/j.aca.2015.02.012.
  • Lavine B. K., Workman J. Chemometrics // Analytical Chemistry. — 2013. — January (vol. 85, iss. 2). — P. 705–714. — DOI:10.1021/ac303193j.
  • Harrington P. B. Automated support vector regression // Journal of Chemometrics. — 2017. — April (vol. 31, iss. 4). — P. e2867 [1–14]. — DOI:10.1002/cem.2867.
  • Baird Z. S., Oja V. Predicting fuel properties using chemometrics: a review and an extension to temperature dependent physical properties by using infrared spectroscopy to predict density // Chemometrics and Intelligent Laboratory Systems. — 2016. — November (vol. 158). — P. 41–47. — DOI:10.1016/j.chemolab.2016.08.004.
  • Vempatapu B. P., Kanaujia P. K. Monitoring petroleum fuel adulteration: A review of analytical methods // TrAC Trends in Analytical Chemistry. — 2017. — July (vol. 92). — P. 1–11. — DOI:10.1016/j.trac.2017.04.011.
  • Vershinin V. I. Chemometrics in the works of Russian analysts // Journal of Analytical Chemistry. — 2011. — November (vol. 66, iss. 11). — P. 1010–1019. — DOI:10.1134/s1061934811110153.
  • Hanif M. A., Nisar S., Akhtar M. N., Nisar N., Rashid N. Optimized production and advanced assessment of biodiesel: A review // International Journal of Energy Research. — 2018. — May (vol. 42, iss. 6). — P. 2070–2083. — DOI:10.1002/er.3990.
  • Cetó X., Voelcker N. H., Prieto-Simón B. Bioelectronic tongues: New trends and applications in water and food analysis // Biosensors and Bioelectronics. — 2016. — May (vol. 79). — P. 608–626. — DOI:10.1016/j.bios.2015.12.075.
  • Byrne H. J., Knief P., Keating M. E., Bonnier F. Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells // Chemical Society Reviews. — 2016. — April (vol. 45, iss. 7). — P. 1865–1878. — DOI:10.1039/c5cs00440c.
  • Liu D., Sun D.-W., Zeng X.-A. Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry // Food and Bioprocess Technology. — 2014. — Vol. 7, iss. 2. — P. 307–323. — DOI:10.1007/s11947-013-1193-6.
  • Carreiro Soares S. F., Gomes A. A., Ugulino Araujo M., Galvão Filho A. R., Harrop Galvão R. K. The successive projections algorithm // TrAC Trends in Analytical Chemistry. — 2013. — January (vol. 42). — P. 84–98. — DOI:10.1016/j.trac.2012.09.006.
  • Czar M. F., Jockusch R. A. Sensitive probes of protein structure and dynamics in well-controlled environments: combining mass spectrometry with fluorescence spectroscopy // Current Opinion in Structural Biology / eds. Ben Schuler, Janet L. Smith. — 2015. — October (vol. 34). — P. 123–134. — DOI:10.1016/j.sbi.2015.09.004.
  • Blanco C. A., Andrés-Iglesias C., Montero O. Low-alcohol Beers: Flavor Compounds, Defects, and Improvement Strategies // Critical Reviews in Food Science and Nutrition. — 2014. — August (vol. 56, iss. 8). — P. 1379–1388. — DOI:10.1080/10408398.2012.733979.
  • Doezema L. A., Longin T., Cody W., Perraud V., Dawson M. L., Ezell M. J., Greaves J., Johnson K. R., Finlayson-Pitts B. J. Analysis of secondary organic aerosols in air using extractive electrospray ionization mass spectrometry (EESI-MS) // RSC Advances. — 2012. — Vol. 2, iss. 7. — P. 2930–2938. — DOI:10.1039/c2ra00961g.
  • Hopper J. T. S., Robinson C. V. Mass Spectrometry Quantifies Protein Interactions-From Molecular Chaperones to Membrane Porins // Angewandte Chemie International Edition. — 2014. — October (vol. 53, iss. 51). — P. 14002–14015. — DOI:10.1002/anie.201403741.
  • Konermann L., Vahidi S., Sowole M. A. Mass Spectrometry Methods for Studying Structure and Dynamics of Biological Macromolecules (Review) // Analytical Chemistry. — 2014. — January (vol. 86, iss. 1). — P. 213–232. — DOI:10.1021/ac4039306.
  • Wyttenbach T., Pierson N. A., Clemmer D. E., Bowers M. T. Ion Mobility Analysis of Molecular Dynamics // Annual Review of Physical Chemistry. — 2014. — April (vol. 65, iss. 1). — P. 175–196. — DOI:10.1146/annurev-physchem-040513-103644.
  • Maple H. J., Garlish R. A., Rigau-Roca L., Porter J., Whitcombe I., Prosser Ch. E., Kennedy J., Henry A. J., Taylor R. J., Crump M. P., Crosby J. Automated Protein–Ligand Interaction Screening by Mass Spectrometry // Journal of Medicinal Chemistry. — 2012. — January (vol. 55, iss. 2). — P. 837–851. — DOI:10.1021/jm201347k.
  • Misra B. B., Assmann S. M., Chen S. Plant single-cell and single-cell-type metabolomics // Trends in Plant Science. — 2014. — October (vol. 19, iss. 10). — P. 637–646. — DOI:10.1016/j.tplants.2014.05.005.
  • Sims J. K., Manteiga S., Lee K. Towards high resolution analysis of metabolic flux in cells and tissues // Current Opinion in Biotechnology. — 2013. — October (vol. 24, iss. 5). — P. 933–939. — DOI:10.1016/j.copbio.2013.07.001.
  • He X., Chen Q., Zhang Y., Lin J.-M. Recent advances in microchip-mass spectrometry for biological analysis // TrAC – Trends in Analytical Chemistry. — 2014. — January (vol. 53). — P. 84–97. — DOI:10.1016/j.trac.2013.09.013.
  • Klepárník K., Foret F. Recent advances in the development of single cell analysis — A review // Analytica Chimica Acta. — 2013. — October (vol. 800). — P. 12–21. — DOI:10.1016/j.aca.2013.09.004.
  • Gao D., Liu H., Jiang Y., Lin J.-M. Recent advances in microfluidics combined with mass spectrometry: technologies and applications // Lab on a Chip. — 2013. — Vol. 13, iss. 17. — P. 3309–3322. — DOI:10.1039/c3lc50449b.
  • Yang Y., Huang Y., Wu J., Liu N., Deng J. Single-cell analysis by ambient mass spectrometry // TrAC Trends in Analytical Chemistry. — 2017. — May (vol. 90). — P. 14–26. — DOI:10.1016/j.trac.2017.02.009.
  • Mao S., Li W., Zhang Q., Zhang W., Huang Q., Lin J.-M. Cell analysis on chip-mass spectrometry // TrAC Trends in Analytical Chemistry. — 2018. — October (vol. 107). — P. 43–59. — DOI:10.1016/j.trac.2018.06.019.
  • Cook D. J., Nielsen J. Genome-scale metabolic models applied to human health and disease // Wiley Interdisciplinary Reviews: Systems Biology and Medicine. — 2017. — November/December (vol. 9, iss. 6). — P. e1393 [1–18]. — DOI:10.1002/wsbm.1393.
  • Galler K., Bräutigam K., Große Ch., Popp J., Neugebauer U. Making a big thing of a small cell – recent advances in single cell analysis // The Analyst. — 2014. — February (vol. 139, iss. 6). — P. 1237–1273. — DOI:10.1039/c3an01939j.
  • Passarelli M. K., Ewing A. G. Single-cell imaging mass spectrometry // Current Opinion in Chemical Biology. — 2013. — October (vol. 17, iss. 5). — P. 854–859. — DOI:10.1016/j.cbpa.2013.07.017.
  • Ni W., Nørgaard L., Mørup M. Non-linear calibration models for near infrared spectroscopy // Analytica Chimica Acta. — 2014. — February (vol. 813). — P. 1–14. — DOI:10.1016/j.aca.2013.12.002.
  • Ashouri M., Maghari A., Karimi-Jafari M. H. Hydrogen bonding motifs in a hydroxy-bisphosphonate moiety: revisiting the problem of hydrogen bond identification // Physical Chemistry Chemical Physics. — 2015. — May (vol. 17, iss. 20). — P. 13290–13300. — DOI:10.1039/c5cp00693g.
  • Jensen F. Using valence bond methods to estimate intramolecular basis set superposition errors // The Journal of Chemical Physics. — 2017. — May (vol. 146, iss. 18). — P. 184109 [1–7]. — DOI:10.1063/1.4983229.
Academic articles
  • Hansen K., Montavon G., Biegler F., Fazli S., Rupp M. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies // Journal of Chemical Theory and Computation. — 2013. — July (vol. 9, iss. 8). — P. 3404–3419. — DOI:10.1021/ct400195d.
    • Montavon G., Rupp M., Gobre V., Vazquez-Mayagoitia A., Hansen K., Tkatchenko A., Müller K.-R., Lilienfeld A. O. Machine learning of molecular electronic properties in chemical compound space // New Journal of Physics. — 2013. — Vol. 15, iss. 9. — P. 095003 [1–16]. — DOI:10.1088/1367-2630/15/9/095003.
  • Kusne A. G., Gao T., Mehta A., Ke L., Nguyen M. C., Ho K.-M., Antropov V., Wang C.-Z., Kramer M. J., Long Ch., Takeuchi I. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets // Scientific Reports. — 2014. — September (vol. 4, iss. 1). — P. 6367 [1–7]. — DOI:10.1038/srep06367.
    • Iwasaki Y., Kusne A. G., Takeuchi I. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries // npj Computational Materials. — 2017. — February (vol. 3, iss. 1, no. 4 [1–9]). — DOI:10.1038/s41524-017-0006-2.
  • Granda J. M., Jurczak J. Artificial Neural Networks for Guest Chirality Classification through Supramolecular Interactions // Chemistry — A European Journal. — 2014. — September (vol. 20, iss. 39). — P. 12368–12372. — DOI:10.1002/chem.201404081.
  • Hajinazar S., Shao J., Kolmogorov A. N. Stratified construction of neural network based interatomic models for multicomponent materials // Physical Review B. — 2017. — January (vol. 95, iss. 1). — P. 14114 [1–13]. — DOI:10.1103/PhysRevB.95.014114.
  • Karton A., Yu L.-J., Kesharwani M. K., Martin J. M. L. Heats of formation of the amino acids re-examined by means of W1-F12 and W2-F12 theories // Theoretical Chemistry Accounts. — 2014. — April (vol. 133, iss. 6). — P. 1483 [1–15]. — DOI:10.1007/s00214-014-1483-8.
  • Ghosh M. K., Choi T. H., Choi C. H. Conformational free energy surfaces of non-ionized glycine in aqueous solution // Theoretical Chemistry Accounts. — 2016. — Vol. 135, iss. 4. — P. 103 [1–11]. — DOI:10.1007/s00214-016-1857-1.
  • Bazsó G., Magyarfalvi G., Tarczay G. Near-infrared laser induced conformational change and UV laser photolysis of glycine in low-temperature matrices: Observation of a short-lived conformer // Journal of Molecular Structure. — 2012. — October (vol. 1025). — P. 33–42. — DOI:10.1016/j.molstruc.2012.04.066.
  • Barone V., Biczysko M., Bloino J., Puzzarini Cr. Accurate structure, thermodynamic and spectroscopic parameters from CC and CC/DFT schemes: the challenge of the conformational equilibrium in glycine // Physical Chemistry Chemical Physics. — 2013. — Vol. 15, iss. 25. — P. 10094—10111. — DOI:10.1039/c3cp50439e.
    • Barone V., Biczysko M., Bloino J., Puzzarini Cr. Characterization of the Elusive Conformers of Glycine from State-of-the-Art Structural, Thermodynamic, and Spectroscopic Computations: Theory Complements Experiment // Journal of Chemical Theory and Computation. — 2013. — February (vol. 9, iss. 3). — P. 1533–1547. — DOI:10.1021/ct3010672.
  • Cormanich R. A., Rittner R., Bühl M. Conformational preferences of Ac-Gly-NHMe in solution // RSC Advances. — 2015. — Vol. 5, iss. 17. — P. 13052—13060. — DOI:10.1039/c4ra16472e.
  • Liu F., Yu J., Huang Y.-R. High-level theoretical study of the evolution of abundances and interconversion of glycine conformers // Chinese Physics B. — 2018. — April (vol. 27, iss. 4). — P. 043102 [1–8]. — DOI:10.1088/1674-1056/27/4/043102.
  • Sacchi M., Jenkins S. J. Co-adsorption of water and glycine on Cu{110} // Physical Chemistry Chemical Physics. — 2014. — Vol. 16, iss. 13. — P. 6101—6107. — DOI:10.1039/c3cp55094j.
  • Farrokhpour H., Fathi F., Naves De Brito A. Theoretical and Experimental Study of Valence Photoelectron Spectrum of d,l-Alanine Amino Acid // The Journal of Physical Chemistry A. — 2012. — June (vol. 116, iss. 26). — P. 7004–7015. — DOI:10.1021/jp3023716.
  • Nunes C. M., Lapinski L., Fausto R., Reva I. Near-IR laser generation of a high-energy conformer of L-alanine and the mechanism of its decay in a low-temperature nitrogen matrix // The Journal of Chemical Physics. — 2013. — March (vol. 138, iss. 12). — P. 125101 [1–12]. — DOI:10.1063/1.4795823.
  • Tia M., Cunha De Miranda B. K., Daly St., Gaie-Levrel Fr., Garcia G. A., Nahon L., Powis I. VUV Photodynamics and Chiral Asymmetry in the Photoionization of Gas Phase Alanine Enantiomers // The Journal of Physical Chemistry A. — 2014. — April (vol. 118, iss. 15). — P. 2765–2779. — DOI:10.1021/jp5016142.
  • Butler H. J., Ashton L., Bird B., Cinque G., Curtis K. Using Raman spectroscopy to characterize biological materials // Nature Protocols. — 2016. — March (vol. 11, iss. 4). — P. 664—687. — DOI:10.1038/nprot.2016.036.
  • Hoehse M., Paul A., Gornushkin I., Panne U. Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS // Analytical and Bioanalytical Chemistry. — 2012. — February (vol. 402, iss. 4). — P. 1443–1450. — DOI:10.1007/s00216-011-5287-6.
  • Dingari N. C., Barman I., Myakalwar A. K., Tewari S. P., Gundawar M. Incorporation of Support Vector Machines in the LIBS Toolbox for Sensitive and Robust Classification Amidst Unexpected Sample and System Variability // Analytical Chemistry. — 2012. — March (vol. 84, iss. 6). — P. 2686–2694. — DOI:10.1021/ac202755e.
  • Tange R. I., Rasmussen M. A., Taira E., Bro R. Benchmarking support vector regression against partial least squares regression and artificial neural network: Effect of sample size on model performance // Journal of Near Infrared Spectroscopy. — 2017. — October (vol. 25, iss. 6). — P. 381–390. — DOI:10.1177/0967033517734945.
    • Tange R., Rasmussen M. A., Taira E., Bro R. Application of Support Vector Regression for Simultaneous Modelling of near Infrared Spectra from Multiple Process Steps // Journal of Near Infrared Spectroscopy. — 2015. — Vol. 23, iss. 2. — P. 75–84. — DOI:10.1255/jnirs.1149.
  • Palou A., Miró A., Blanco M., Larraz R., Gómez J. F. Calibration sets selection strategy for the construction of robust PLS models for prediction of biodiesel/diesel blends physico-chemical properties using NIR spectroscopy // Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. — 2017. — June (vol. 180). — P. 119–126. — DOI:10.1016/j.saa.2017.03.008.
  • Rammal A., Perrin E., Vrabie V., Assaf R., Fenniri H. Selection of discriminant mid-infrared wavenumbers by combining a naïve Bayesian classifier and a genetic algorithm: Application to the evaluation of lignocellulosic biomass biodegradation // Mathematical Biosciences. — 2017. — July (vol. 289). — P. 153–161. — DOI:10.1016/j.mbs.2017.05.002.
  • Gharagheizi F., Eslamimanesh A., Tirandazi B., Mohammadi A. H., Richon D. Handling a very large data set for determination of surface tension of chemical compounds using Quantitative Structure–Property Relationship strategy // Chemical Engineering Science. — 2011. — November (vol. 66, iss. 21). — P. 4991–5023. — DOI:10.1016/j.ces.2011.06.052.
  • Li X., Hu B., Ding J., Chen H. Rapid characterization of complex viscous samples at molecular levels by neutral desorption extractive electrospray ionization mass spectrometry // Nature Protocols. — 2011. — June (vol. 6, iss. 7). — P. 1010–1025. — DOI:10.1038/nprot.2011.337.
  • Šedo O., Márová I., Zdráhal Z. Beer fingerprinting by Matrix-Assisted Laser Desorption-Ionisation-Time of Flight Mass Spectrometry // Food Chemistry. — 2012. — November (vol. 135, iss. 2). — P. 473–478. — DOI:10.1016/j.foodchem.2012.05.021.
  • Przybylski C., Bonnet V., Cézard Ch. Probing the common alkali metal affinity of native and variously methylated β-cyclodextrins by combining electrospray-tandem mass spectrometry and molecular modeling // Physical Chemistry Chemical Physics. — 2015. — August (vol. 17, iss. 29). — P. 19288–19305. — DOI:10.1039/c5cp02895g.
  • Goldstein M., Zmiri L., Segev E., Wyttenbach T., Gerber R. B. An atomistic structure of ubiquitin +13 relevant in mass spectrometry: Theoretical prediction and comparison with experimental cross sections // International Journal of Mass Spectrometry. — 2014. — June (vol. 367). — P. 10–15. — DOI:10.1016/j.ijms.2014.04.013.
  • Fernandes A. M., Schröder B., Barata T., Freire M. G., Coutinho J. A. P. Inclusion Complexes of Ionic Liquids and Cyclodextrins: Are They Formed in the Gas Phase? // Journal of the American Society for Mass Spectrometry. — 2014. — May (vol. 25, iss. 5). — P. 852–860. — DOI:10.1007/s13361-013-0820-9.
  • Lemaur V., Carroy G., Poussigue F., Chirot F., De Winter J. Homotropic Allosterism: In-Depth Structural Analysis of the Gas-Phase Noncovalent Complexes Associating a Double-Cavity Cucurbit(n)uril-Type Host and Size-Selected Protonated Amino Compounds // ChemPlusChem. — 2013. — September (vol. 78, iss. 9). — P. 959–969. — DOI:10.1002/cplu.201300208.
  • Bergman H.-M., Lanekoff I. Profiling and quantifying endogenous molecules in single cells using nano-DESI MS // The Analyst. — 2017. — October (vol. 142, iss. 19). — P. 3639–3647. — DOI:10.1039/c7an00885f.
  • Moussaieff A., Rogachev I., Brodsky L., Malitsky S., Toal T. W. High-resolution metabolic mapping of cell types in plant roots // Proceedings of the National Academy of Sciences. — 2013. — March (vol. 110, iss. 13). — P. E1232–E1241. — DOI:10.1073/pnas.1302019110.
  • Fujii T., Matsuda Sh., Tejedor M. L., Esaki T., Sakane I. Direct metabolomics for plant cells by live single-cell mass spectrometry // Nature Protocols. — 2015. — August (vol. 10, iss. 9). — P. 1445–1456. — DOI:10.1038/nprot.2015.084.
  • Cole R. H., Tang S.-Y., Siltanen Ch. A., Shahi P., Zhang J. Q. Printed droplet microfluidics for on demand dispensing of picoliter droplets and cells // Proceedings of the National Academy of Sciences. — 2017. — August (vol. 114, iss. 33). — P. 8728–8733. — DOI:10.1073/pnas.1704020114.
  • Cole L. M., Clench M. R. Mass spectrometry imaging for the proteomic study of clinical tissue // Proteomics — Clinical Applications. — 2015. — April (vol. 9, iss. 3—4). — P. 335–341. — DOI:10.1002/prca.201400103.
  • Jawaid S., Talpur F. N., Sherazi S. T. H., Nizamani S. M., Khaskheli A. A. Rapid detection of melamine adulteration in dairy milk by SB-ATR–Fourier transform infrared spectroscopy // Food Chemistry. — 2013. — December (vol. 141, iss. 3). — P. 3066–3071. — DOI:10.1016/j.foodchem.2013.05.106.
  • Hameed R., Khan A., Mourik T. Intramolecular BSSE and dispersion affect the structure of a dipeptide conformer // Molecular Physics. — 2018. — May (vol. 116, iss. 9). — P. 1236–1244. — DOI:10.1080/00268976.2017.1418029.
  • Liu Ch., McGivern W. S., Manion J. A., Wang H. Theory and Experiment of Binary Diffusion Coefficient of n-Alkanes in Dilute Gases // The Journal of Physical Chemistry A. — 2016. — October (vol. 120, iss. 41). — P. 8065–8074. — DOI:10.1021/acs.jpca.6b08261.
  • Sladek V., Holka F., Tvaroška I. Ab initio modelling of the anomeric and exo anomeric effects in 2-methoxytetrahydropyran and 2-methoxythiane corrected for intramolecular BSSE // Physical Chemistry Chemical Physics. — 2015. — July (vol. 17, iss. 28). — P. 18501–18513. — DOI:10.1039/c5cp02191j.
  • Faver J. C., Zheng Zh., Merz K. M. Statistics-based model for basis set superposition error correction in large biomolecules // Physical Chemistry Chemical Physics. — 2012. — June (vol. 14, iss. 21). — P. 7795—7799. — DOI:10.1039/c2cp23715f.
  • Pele L., Šebek J., Potma E. O., Gerber R. B. Raman and IR spectra of butane: Anharmonic calculations and interpretation of room temperature spectra // Chemical Physics Letters. — 2011. — October (vol. 515, iss. 1—3). — P. 7–12. — DOI:10.1016/j.cplett.2011.09.015.
  • Plumley J. A., Dannenberg J. J. Comparison of β-Sheets of Capped Polyalanine with Those of the Tau-Amyloid Structures VQIVYK and VQIINK. A Density Functional Theory Study // The Journal of Physical Chemistry B. — 2011. — September (vol. 115, iss. 35). — P. 10560–10566. — DOI:10.1021/jp205388q.
  • Hernandez-Castillo A. O., Abeysekera Ch., Hays B. M., Kleiner I., Nguyen H. V. L. Conformational preferences and internal rotation of methyl butyrate by microwave spectroscopy // Journal of Molecular Spectroscopy. — 2017. — July (vol. 337). — P. 51–58. — DOI:10.1016/j.jms.2017.03.016.
  • Fadda E., Woods R. J. Contribution of the empirical dispersion correction on the conformation of short alanine peptides obtained by gas-phase QM calculations // Canadian Journal of Chemistry. — 2013. — September (vol. 91, iss. 9). — P. 859–865. — DOI:10.1139/cjc-2012-0542.
  • Hua Sh., Xu L., Li W., Li Sh. Cooperativity in Long α- and 310-Helical Polyalanines: Both Electrostatic and van der Waals Interactions Are Essential // The Journal of Physical Chemistry B. — 2011. — October (vol. 115, iss. 39). — P. 11462–11469. — DOI:10.1021/jp203423w.
  • Alparone A. The effect of secondary structures on the NLO properties of single chain oligopeptides: a comparison between β-strand and α-helix polyglycines // Physical Chemistry Chemical Physics. — 2013. — Vol. 15, iss. 31. — P. 12958 [1–5]. — DOI:10.1039/c3cp51496j.
[edit]
  • 10.1021/acs.chemrev.0c00168 Neutral Peptides in the Gas Phase
  • 978-1945612008
  • Machine learning technology in biodiesel research: A review 10.1016/j.pecs.2021.100904
  • 10.1007/978-3-030-70400-1_4; 10.1016/j.aca.2021.338403; 10.1038/s43586-021-00034-1; 10.1039/D1AN00082A; 10.1038/s41598-021-92850-4; 10.3390/molecules29040781.
  • Chapter 14 -- Artificial intelligence applied to healthcare and biotechnology // Biotechnology in Healthcare, Volume 1
  • Debt in Times of Crisis: Does Economic Crisis Really Impact Debt? Thomas Poufinas
  • 10.1002/9780470027318.a9081 Encyclopedia of Analytical Chemistry;; 10.1016/j.comptc.2023.114224
  • 10.1039/9781839165849-00111 CHAPTER 6: Visible and Near-infrared Spectroscopy for Quality Analysis of Wine, By Daniel Cozzolino // Advanced Spectroscopic Techniques for Food Quality / Edited by Ashutosh Kumar Shukla 2022
  • 10.1007/978-981-97-3023-0_2 Mathematical Preliminaries for Video Anomaly Detection Techniques // Anomaly Detection in Video Surveillance (2024)
  • 10.1109/ICCR60000.2023.10444799
  • ISBN 978-3-96831-018-3
  • Jia, W., Koidis, A. (2024). Mid-Infrared Spectroscopy (MIR). In: Jiménez-Carvelo, A.M., Arroyo-Cerezo, A., Cuadros-Rodríguez, L. (eds) Non-invasive and Non-destructive Methods for Food Integrity. Springer, Cham. https://doi.org/10.1007/978-3-031-76465-3_4


Category:Living people Category:Spectroscopists Category:Mass spectrometrists Category:Swiss academics Category:Russian physical chemists Category:Gubkin Russian State University of Oil and Gas Category:Academic staff of ETH Zurich Category:Academic staff of Heidelberg University Category:Academic staff of the University of Basel Category:Academic staff of the University of Göttingen Category:21st-century German chemists Category:1985 births

Retrieved from "https://wiki.riteme.site/w/index.php?title=Draft:Roman_Balabin&oldid=1263569477"