«IZVESTIYA IRKUTSKOGO GOSUDARSTVENNOGO UNIVERSITETA». SERIYA «BIOLOGIYA. ECOLOGIYA»
«THE BULLETIN OF IRKUTSK STATE UNIVERSITY». SERIES «BIOLOGY. ECOLOGY»
ISSN 2073-3372 (Print)

List of issues > Series «Biology. Ecology». 2025. Vol 54

Spectral Markers of Hydrothermal Treatment of Coniferous Wood Waste as a Tool for Environmentally Sustainable Technologies

Author(s)

A. V. Novikov, D. A. Yarygin, V. L. Mikhailenko, А. А. Pristavka, G. V. Yurinova, V. P. Salovarova

Irkutsk State University, Irkutsk, Russian Federation

Abstract
Lignocellulosic waste is a valuable resource for biologically active compounds in biotechnological processing. Hydrothermal extraction and subsequent fractionation represent key stages whose efficiency depends on temperature, time, and solvent type. Process optimization and quality control require objective reference points to assess extract composition at different stages. Traditional analytical methods (chromatography, mass spectrometry) are labor-intensive and unsuitable for operational control. UV-Vis spectroscopy (200–400 nm) offers a rapid alternative, as absorption spectra reflect aromatic and conjugated systems characteristic of lignans, flavonoids, and other wood-derived bioactives. However, spectral interpretation of complex mixtures is hindered by band overlap, necessitating data preprocessing and multivariate analysis such as principal component analysis (PCA) to extract informative spectral markers. This study evaluated UV-Vis spectroscopy for identifying reference points in wood waste processing-specifically, spectral markers correlating with technological parameters (extraction temperature, solvent type) and fraction bioactivity. Aqueous extracts from pine and larch sawdust were obtained at three temperatures (20°C, 80°C, 121°C) and fractionated with hexane and chloroform. UV-Vis spectra of all fractions were recorded, and fourteen normalization methods were compared; Max Scaling proved most effective for subsequent PCA. The first two principal components (PC1 and PC2) cumulatively explained 79.40% of total variance, enabling meaningful interpretation. Score analysis revealed that PC2 encodes the fundamental chemical difference determined by extractant nature: primary aqueous extracts clustered at high positive PC2 values, while hexane and chloroform fractions grouped in the negative region. Thus, PC2 serves as a reliable marker for monitoring liquid-liquid fractionation efficiency. PC1 proved sensitive to variations within solvent groups, particularly hydrothermal treatment temperature. A clear temperature-dependent trend in PC1 scores was observed for aqueous extracts. Notably, hexane fractions also exhibited pronounced temperature dependence along PC2, shifting from positive (+1.87 for W1.H) to sharply negative values (-2.80 for W3.H), suggesting a change in the prevailing extraction mechanism of lipophilic components with increasing severity. Loading analysis provided spectral interpretation: PC1 displayed a bipolar profile (positive at 200–215 nm, negative at 235–300 nm), likely reflecting distinct chromophore pools-hydrophilic lignans versus more lipophilic stilbenes or flavonoids. PC2 showed a broad positive plateau (210–260 nm), interpreted as a marker of the hydrophilic matrix retained after organic solvent extraction. The established correlations between technological parameters and spectral patterns in PC1-PC2 space form a basis for rapid control methods. A sample's position in this space enables operational assessment of both fractionation efficiency and hydrothermal treatment adequacy-critical for standardizing biotechnological processing of wood waste.
About the Authors

Novikov Artem Vladimirovich, Postgraduate Irkutsk State University 1, K. Marx st., Irkutsk, 664003, Russian Federation e-mail: artem.ru88@mail.ru 

Yarygin Dmitriy Andreevich, Undergraduate Irkutsk State University 1, K. Marx st., Irkutsk, 664003, Russian Federation e-mail: mr.dmitry.yarygin@gmail.com

Mikhailenko Valentina Lvovna, Candidate of Science (Chemistry), Associate Professor Irkutsk State University 1, K. Marx st., Irkutsk, 664003, Russian Federation e-mail: mival63@gmail.com

Pristavka Aleksey Aleksandrovich, Candidate of Sciences (Biology), Associate Professor 

Yurinova Galina Valerievna, Candidate of Sciences (Biology), Associate Professor Irkutsk State University e-mail: yurinova@yandex.ru 1, K. Marx st., Irkutsk, 664003, Russian Federation e-mail: yurinova@yandex.ru 

Salovarova Valentina Petrovna, Doctor of Sciences (Biology), Professor, Head of Department, Irkutsk State University 1, K. Marx st., Irkutsk, 664003, Russian Federation e-mail: vsalovarova@gmail.com

For citation
Novikov A.V., Yarygin D.A., Mikhailenko V.L., Pristavka А.А., Yurinova G.V., Salovarova V.P. Spectral Markers of Hydrothermal Treatment of Coniferous Wood Waste as a Tool for Environmentally Sustainable Technologies. The Bulletin of Irkutsk State University. Series Biology. Ecology, 2025, vol. 54, pp. 3-22. https://doi.org/10.26516/2073-3372.2025.54.3 (in Russian)
Keywords
UV-visible spectroscopy, principal component analysis, wood waste, chemometrics, hydrothermal treatment, biorefining.
UDC
543.422:543.06:66.095.2
DOI
https://doi.org/10.26516/2073-3372.2025.54.3
References
  1. Ostroukhova L.A., Fedorova T.E., Onuchina N.A., Levchuk A.A., Babkin V.A. Opredelenie kolichestvennogo soderzhaniya ekstraktivnykh veshchestv iz drevesiny, kornei i kory derev'ev khvoynykh vidov sibiri: listvennitsy (Larix sibirica L.), sosny (Pinus sylvestris L.), pikhty (Abies sibirica L.), eli (Picea obovata L.) i kedra (Pinus sibirica Du Tour) [Determination of quantitative content of extractive substances from wood, roots and bark of Siberian coniferous tree species: larch (Larix sibirica L.), pine (Pinus sylvestris L.), fir (Abies sibirica L.), spruce (Picea obovata L.) and cedar (Pinus sibirica Du Tour)]. Khimija rastitel'nogo syr'ja [Chemistry of Plant Raw Materials], 2018, no. 4, pp. 185-195. https://doi.org/10.14258/jcprm.2018044245 (in Russian)
  2. Abraham M.H., Acree W.E. On the solubility of quercetin. J. Mol. Liq., 2014, vol. 197, pp. 157-159. https://doi.org/10.1016/j.molliq.2014.05.006
  3. Afseth N.K., Kohler A. Extended multiplicative signal correction in vibrational spectroscopy, a tutorial. Chemometr. Intell. Lab. Syst., 2012, vol. 117, pp. 92-99. https://doi.org/10.1016/j.chemolab.2012.03.004
  4. Andersen O.M., Markham K.R. Flavonoids: chemistry, biochemistry and applications. Boca Raton, CRC Press, 2005. 1256 p. https://doi.org/10.1201/9781420039443
  5. Ayres D. C., Loike J. D. Lignans: chemical, biological and clinical properties. Cambridge Univ. Press, 1990.
  6. Barnes R.J., Dhanoa M.S., Lister S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectroscop., 1989, vol. 43, no. 5, pp. 772-777. https://doi.org/10.1366/0003702894202201
  7. Beattie J.R., Esmonde-White F.W.L. Exploration of principal component analysis: deriving principal component analysis visually using spectra. Appl. Spectroscop., 2021, vol. 75, is. 4, pp. 361-375. https://doi.org/10.1177/0003702820987847
  8. Budavari S. The Merck index – an encyclopedia of chemicals, drugs, and biologicals. Whitehouse Station, NJ: Merck and Co., 1996. 1505 p.
  9. Chuen L.L., Liong C.-Y., Jemain A.A. Effects of data pre-processing methods on classification of ATR-FTIR spectra of pen inks using partial least squares-discriminant analysis (PLS-DA). Chemometr. Intell. Lab. Syst., 2018, vol. 182, pp. 90-100. https://doi.org/10.1016/j.chemolab.2018.09.001
  10. Wu M.-H., Zhu L., Zhou Z.-Z., Zhang Y.-Q. Coimmobilization of naringinases on silk Fibroin nanoparticles and its application in food packaging. J. Nanoparticl., 2013, art. 901401. https://doi.org/10.1155/2013/901401
  11. Cruz M.V., Sarraguça M.C., Freitas F., Lopes J.A., Reis M.A.M. Online monitoring of P(3HB) produced from used cooking oil with near-infrared spectroscopy. J. Biotechnol., 2015, vol. 194, pp. 1-9. https://doi.org/10.1016/j.jbiotec.2014.11.022
  12. Telange D.R., Patil A.T., Tatode A.A., Bhoyar B.S. Development and validation of UV spectrophotometric method for the estimation of kaempferol in kaempferol: Hydrogenated soy phosphatidylcholine (HSPC) complex. Pharmaceutical Methods, 2014, vol. 5, no. 1, pp. 34-38. https://doi.org/10.5530/phm.2014.1.6
  13. de Jong E., Jungmeier G. Biorefinery concepts in comparison to petrochemical refineries. Industrial Biorefineries & White Biotechnology. Amsterdam, Elsevier, 2015, pp. 3-33. https://doi.org/10.1016/B978-0-444-63453-5.00001-X
  14. dos Santos Grasel F., Ferrão M.F., Wolf C.R. Ultraviolet spectroscopy and chemometrics for the identification of vegetable tannins. Ind. Crop. Prod., 2016, vol. 91, pp. 279-285. https://doi.org/10.1016/j.indcrop.2016.07.022
  15. Sun C., Wu Z., Wang Z., Zhang H. Effect of ethanol/water solvents on phenolic profiles and antioxidant properties of beijing propolis extracts. Evid. Based Complement. Alternat. Med., 2015, pp. 1-9. https://doi.org/10.1155/2015/595393
  16. Khlupova M., Vasil’eva I., Shumakovich G., Zaitseva E., Chertkov V., Shestakova A., Morozova O., Yaropolov A. Enzymatic Polymerization of Dihydroquercetin (Taxifolin) in BetaineBased Deep Eutectic Solvent and Product Characterization. Catalysts, 2021, vol. 11, art. 639. https://doi.org/10.3390/catal11050639
  17. Latunde-Dada A.O., Cabello-Hurtado F., Czittrich N., Didierjean L., Schopfer C., Hertkorn N., Werck-Reichhart D., Ebel J. Flavonoid 6-hydroxylase from soybean (Glycine max L.), a novel plant P-450 monooxygenase. J. Biol. Chem., 2001, vol. 276, is. 3, pp. 1688-1695. https://doi.org/10.1074/jbc.M006277200
  18. Villegas-Camacho O., Francisco-Valencia I., Alejo-Eleuterio R., Granda-Gutiérrez E. E., Martínez-Gallegos S., Villanueva-Vásquez D. FTIR-based microplastic classification: a comprehensive study on normalization and ML techniques. Recycling, 2025, vol. 10, no. 2, art. 46. https://doi.org/10.3390/recycling10020046
  19. Gabriela C., Simion G. Analysis of medicinal plants by HPLC: recent approaches. J. Liq. Chromatogr. Relat. Technol., 2002, vol. 25, is. 13-15, pp. 2225–2292. https://doi.org/10.1081/JLC120014003
  20. Haldar D., Purkait M.K. A review on the environment-friendly emerging techniques for pretreatment of lignocellulosic biomass: Mechanistic insight and advancements. Chemosphere, 2021, vol. 264, art. 128523. https://doi.org/10.1016/j.chemosphere.2020.128523
  21. Hansch C., Leo A., Hoekman D. Exploring QSAR: Hydrophobic, Electronic, and Steric Constants. Washington, Am. Chem. Soc., 1995, 348 p.
  22. Holiday E.R., Jope E.M. The ultraviolet spectral absorption of chrysene, its monomethoxy-derivatives and 1:2 dimethoxychrysene. Spectrochim. Acta, 1950, vol. 4, is. 2, pp. 157-164. https://doi.org/10.1016/s0371-1951(50)80008-5
  23. Hosseinian F.F.H. Antioxidant properties of faxseed lignans using in vitro model systems. Ph.D. Thesis. Univ. Saskatchewan. Saskatoon, 2006, 234 p.
  24. Mistrzak P., Celejewska-Marciniak H., Szypuła W.J., Olszowska O., Kiss A.K. Identification and quantitative determination of pinoresinol in Taxus ×media Rehder needles, cell suspension and shoot cultures. Acta Soc. Bot. Pol., 2015, vol. 84, no. 1, pp. 125-132. https://doi.org/10.5586/asbp.2014.038
  25. Guo H., Zhao Y., Chang J.-S., Lee D.-J. Inhibitor formation and detoxification during lignocellulose biorefinery: A review. Biores. Technol., 2022, vol. 361, art. 127666. https://doi.org/10.1016/j.biortech.2022.127666
  26. Shu R., Zhang Q., Ma L., Xu Y., Chen P., Wang C., Wang T. Insight into the solvent, temperature and time effects on the hydrogenolysis of hydrolyzed lignin. Biores. Technol., 2016, vol. 221, pp. 568-575. https://doi.org/10.1016/j.biortech.2016.09.043
  27. Isaksson T., Næs T. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl. Spectroscop.y, 1988, vol. 42, no. 7, pp. 1273-1284. https://doi.org/10.1366/0003702884429869
  28. Jin J., Zheng Y., Brash A.R. Demonstration of HNE-related aldehyde formation via lipoxygenase-catalyzed synthesis of a bis-allylic dihydroperoxide intermediate. Chem. Res. Toxicol., 2013, vol. 26, no. 6, pp. 896-903. https://doi.org/10.1021/tx4000396
  29. Modonova L.D., Voronov V.K., Leont'eva V.G., Tyukavkina N. A.. Lignan compounds from Picea obovata. Chem. Nat. Compd., 1972, vol.8, pp. 170-174. https://doi.org/10.1007/BF00565293
  30. Yeo H., Chin Y.-W., Park S.-Y., Kim J. Lignans of Rosa multiflora roots. Arch. Pharm. Res., 2004, vol. 27, pp. 287-290. https://doi.org/10.1007/bf02980061
  31. Andargie M., Vinas M., Rathgeb A., Möller E., Karlovsky P. Lignans of sesame (Sesamum indicum L.): A Comprehensive Review. Molecules, 2021, vol. 26, no. 4, art. 883. https://doi.org/10.3390/molecules26040883
  32. Lutoshkin M.A., Kuznetsov B.N., Levdansky V.A. Spectrophotometric and quantum-chemical study of acid-base and complexing properties of (±)-taxifolin in aqueous solution. Heterocycl. Commun., 2017, vol. 23, no. 5, pp. 395-400. https://doi.org/10.1515/hc-2017-0075
  33. Mabry T.J., Markham K.R., Thomas M.B. The systematic identification of flavonoids. Berlin, Heidelberg, New York, Springer-Verlag, 1970, 354 p. https://doi.org/10.1007/978-3-642-88458-0
  34. Markham K.R., MabryT.J. Ultraviolet-visible and proton magnetic resonance spectroscopy of flavonoids. The Flavonoids / J. B. Harborne, T. J. Mabry, H. Mabry (eds.). Academic Press, 1975, pp. 45-77.
  35. Mowbray M., Savage T., Wu C., Song Z., Cho B.A., Del Rio-Chanona E.A., Zhang D. Machine learning for biochemical engineering: A review. Biochem. Engineer. J., 2021, vol. 172, 108054. https://doi.org/10.1016/j.bej.2021.108054
  36. Leont'eva V.G., Modonova L.D., Voronov V.K., Tyukavkina N. A. New O-acyl derivatives of lariciresinol. Chem. Nat. Compd., 1976, vol. 12, pp. 147-150. https://doi.org/10.1007/BF00566332
  37. Nishibe S., Hisada S., Inagak I. The ether-soluble lignans of Trachelospermum asiaticum var. intermedium. Phytochemistry, 1971, vol. 10, is. 9, pp. 2231-2232. https://doi.org/10.1016/S0031-9422(00)97230-3
  38. Petridis L., Smith J.C. Molecular-level driving forces in lignocellulosic biomass deconstruction for bioenergy. Nat. Rev. Chem., 2018, vol. 2, no. 11, pp. 382-389. https://doi.org/10.1038/s41570-018-0050-6
  39. Azevedo da Silva N., Rodrigues E., Mercadante A. Z., de Rosso V. V. Phenolic compounds and carotenoids from four fruits native from the Brazilian atlantic forest. J. Agric. Food Chem, 2014, vol. 62, is. 22, pp.5072-5084. https://doi.org/10.1021/jf501211p
  40. Pilkington L.I. Lignans: A chemometric analysis. Molecules, 2018, vol. 23, no. 7, 1666. https://doi.org/10.3390/molecules23071666
  41. Rahman A.-U., Choudhary M. I., Wahab A.-T. Logical approach for solving structural problems. Solving Problems with NMR Spectroscopy. London, Academic Press, 2016, pp. 431-494. https://doi.org/10.1016/B978-0-12-411589-7.00010-3
  42. Mondal P.P., Galodha A., Verma V.K., Singh V., Show P.L., Awasthi M.K., Lall B., Anees S., Pollmann K., Jain R. Review on machine learning-based bioprocess optimization, monitoring, and control systems. Biores. Technol., 2023, vol. 370, 128523. https://doi.org/10.1016/j.biortech.2022.128523
  43. Cordenonsi L.M., Sponchiado R.M., Campanharo S.C., Garcia C.V., Raffin R.P., Schapoval E.E.S. Study of flavonoids present in Pomelo (Citrus maxima) by DSC, UV-VIS, IR, 1H and 13C NMR and MS. Drug Anal. Res, 2017, vol. 1, pp. 31-37. https://doi.org/10.22456/2527-2616.74097
  44. Dodge L.A., Kalinoski R.M., Das L., Bursavich J., Muley P., Boldor D., Shi J. Sequential extraction and characterization of lignin-derived compounds from thermochemically processed biorefinery lignins. Energy & Fuels, 2019, vol. 33, no 5, pp. 4322-4330. https://doi.org/10.1021/acs.energyfuels.9b00376
  45. Silva F., Figueiras A., Gallardo E., Nerín C., Domingues F. C. Strategies to improve the solubility and stability of stilbene antioxidants: A comparative study between cyclodextrins and bile acids. Food Chem., 2014, vol. 145, pp. 115-125. https://doi.org/10.1016/j.foodchem.2013.08.034
  46. Jurinjak Tušek A., Petrus A., Weichselbraun A., Mundani R., Müller S., Barkow I., Bucić-Kojić A., Planinić M., Tišma M. Systematic review of machine-learning techniques to support development of lignocellulose biorefineries. Chem. Biochem. Engineer. Quart., 2024, vol. 38, no. 3, pp. 241-263. https://doi.org/10.15255/CABEQ.2023.2273
  47. Struijs K., Vincken J.-P., Doeswijk T.G., Voragen A.G.J., Gruppen H. The chain length of lignan macromolecule from flaxseed hulls is determined by the incorporation of coumaric acid glucosides and ferulic acid glucosides. Phytochem., 2009, vol. 70, is. 2, pp. 262-269. https://doi.org/10.1016/j.phytochem.2008.12.015
  48. Sangadji N.L., Wijaya C., Muharja M., Elaine E., Sangian H.F., Lau R., Widjaja A. Two step fractionation of oil palm empty fruit bunches integrating hydrothermal-organosolv pretreatment for enhanced lignin extraction and enzymatic hydrolysis efficiency. Case Stud. Chem. Environ. Engin., 2025, vol. 12, 101275. https://doi.org/10.1016/j.cscee.2025.101275
  49. Beisl S., Binder M., Varmuza K., Miltner A., Friedl A. UV-Vis spectroscopy and chemometrics for the monitoring of organosolv pretreatments. ChemEngineer., 2018, vol. 2, is. 4, 45. https://doi.org/10.3390/chemengineering2040045
  50. Zhang H., Wang X., Wang J., Chen Q., Huang H., Huang L., Cao S., Ma X. UV–visible diffuse reflectance spectroscopy used in analysis of lignocellulosic biomass material. Wood Sci. Technol., 2020, vol. 54, pp. 837-846. https://doi.org/10.1007/s00226-020-01199-w
  51. Li M., Wijewardane N.K., Ge Y., Xu Z., Wilkins M.R.. Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover. Biores. Technol. Rep., 2020, vol. 9, art. 100386. https://doi.org/10.1016/j.biteb.2020.100386
  52. Wold S., Esbensen K., Geladi P. Principal component analysis. Chemometr. Intell. Lab. Syst., 1987, vol. 2, is.1-3, pp. 37-52. https://doi.org/10.1016/0169-7439(87)80084-9
  53. Weast R.C. Handbook of Chemistry and Physics. 52 th ed. Cleveland, The Chemical Rubber, 1972, 2313 p.
  54. Weast R.C. Handbook of Chemistry and Physics. 60th ed. Boca Raton, CRC Press, 1979, 2450 p.
  55. Zhang W., Xu S. Purification of Secoisolariciresinol Diglucoside with column chromatography on a sephadex LH-20. J. Chromatogr. Sci., 2007, vol. 45, is. 4, pp. 177-182. https://doi.org/10.1093/chromsci/45.4.177
  56. Zimmermann B., Kohler A. Optimizing Savitzky–Golay parameters for improving spectral resolution and quantification in infrared spectroscopy. Appl. Spectroscop., 2013, vol. 67, is. 8, pp. 892-902. https://doi.org/10.1366/12-06723

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