Wikipedia

Draft:Roman Balabin

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

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  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
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  35. ^ Kroll et al., 2017, pp. 2607–2608, 2613
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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.
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External links 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
  • 10.1039/9781839165849-00111 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


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

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