The application of hierarchical cluster analysis to lignins classification based on data of high resolution NMR and solid-state NMR spectra on 13C nuclei

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A large amount of different technical lignins are produced as a waste product of the pulp and paper industry, and are not used rationally. The study of the structure of such lignins is relevant due to its potential applications. It is important to obtain comprehensive knowledge about the structure of lignin macromolecule and to classify lignins based on it. High resolution NMR (nuclear magnetic resonance) experiments for dissolved samples are widely used to study this biopolymer. However, this approach does not allow studying insoluble technical lignins. Solid state NMR spectroscopy may become a solution of this problem. In this paper, we propose an approach to classify the degree of lignin alteration by clustering of solid state spectra with HCA (hierarchical cluster analysis) method. This approach is important because of the lack of direct correlations between the NMR spectra of lignins in the dissolved and solid states, that is based on experimental data. Figures Figure 1 Figure 2 Figure 3 Introduction Lignin is a complex organic polymer that is one of the main components of the cell walls of plants, especially woody plants. It gives plants strength and rigidity and protects them from pathogens and mechanical damage [ 1 ]. Lignin makes up 15–30% of wood weight and is one of the most abundant biopolymers on Earth along with cellulose [ 2 ]. The study of the structure and properties of lignin is important for the development of effective methods of its processing and utilization. Lignin is a potential source of aromatic compounds that can be used in the production of biofuels, chemicals, and other materials [ 3 ]. Knowledge of its structure will make it possible to optimize depolymerization processes, improve catalytic processes to convert lignin into valuable products, and develop new materials such as bioplastics, carbon fibers, and others [ 4 ]. Modern methods of lignin study are aimed at investigating its properties and chemical structure. Despite this, the structure of lignin has not been fully studied, since each lignin molecule is unique, and the structure of lignin depends both on the botanical origin of the raw material [ 5 ] and on the conditions of the procedure for its production [ 6 ]. To find ways to utilize and process lignin, it is important to know not so much the structure of lignin as the degree of its modification [ 4 ], due to the fact that each lignin molecule has a unique structure. Since the study of lignins by chemical methods is difficult due to the variability of their structure, various physical methods are successfully used to study lignins. Nuclear magnetic resonance (NMR) spectroscopy is used to determine the structure and composition of lignin fragments at the molecular level [ 2 ]. Infrared spectroscopy (FTIR) helps to identify functional groups in lignin molecules. Mass spectrometry (MS) is used to analyze the molecular weight and structure of lignin fragments. X-ray photoelectron spectroscopy (XPS) is used to study the surface chemistry of lignin. Gel permeation chromatography (GPC) is used to determine the molecular weight distribution of lignin [ 3 ]. It is worth noting that NMR spectroscopy is currently the main and most promising tool for such objects as lignin. Soluble lignins make it possible to study their structure by methods of one-dimensional and two-dimensional high-resolution NMR spectroscopy, both with and without prior derivatization [ 7 ]. However, many lignins isolated by industrial methods are practically insoluble in standard solvents used for NMR, which leads to the impossibility of experiments with liquid samples. At the same time, the application of solid-state NMR is limited by the fact that the quality of solid state 13 C-NMR spectra of lignin are much worse than 13 C-NMR spectra of dissolved samples. In addition, CP/MAS method distorts the quantitative ratio of integral intensities of signals in 13 C-NMR spectra, due to the transferring of magnetization from hydrogen nuclei to carbon nuclei linked to them [ 8 ]. The degree of alteration of lignin is rather difficult to trace from solid-state NMR data. This problem can be solved by applying deep mathematical analysis of the data, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA). These methods can discard most of the same data and focus on the differences in spectral data [ 9 ]. Therefore, the main idea of this study is to determine the presence of possible correlations between the signal intensities in 13 C-NMR spectra of lignins registered in dissolved and solid state and to develop an approach to the classification of lignins using solid state 13 C-NMR spectra Materials and methods 1. Lignin isolation Lignin of birch ( Bétula péndula ), isolated by various methods, was used as the object of study. Birch wood was chosen due to several reasons, shown in our previous works. First, it is a traditional raw material in the pulp and paper industry [ 10 ]. Second, birch lignin has a more diverse structure compared to softwood lignins and other hardwood lignins. As evidence of diversity, it is possible to note a rather high ratio of the number of syringyl and guaiacyl aromatic rings of lignin, which is one of the most important characteristics of the lignin macromolecule [ 11 – 12 ]. For delignification, xylem chips from a 40-year-old birch tree that grew in a temperate climatic zone (Arkhangelsk region, Russia) were used. The chips were obtained the same as those used for delignification in industrial methods of pulp production: the length of the chips is from 15 to 25 mm, width is about 20 mm, thickness is 3–5 mm, cutting angle is 45 degrees, which provides good impregnation with chemical reagents. Extractives were removed from the chips by acetone extraction, pre-drying the raw material to air-dry condition. Pre-extraction allows to remove from birch xylem such compounds as aldehydes such as furfurols, terpenoids, lignans and other compounds with low molecular weight, which can contribute to NMR signals and hinder the accurate determination of lignin. The amount of extractives was of the order of 8%. The delignification of birch wood was carried out both by industrial methods and by methods that allow obtaining low-altered lignin, where the lignin structure undergoes minimal changes, as far as possible. The following processes were used as industrial lignin samples, where structures undergo significant changes during the processes of lignin dissolution and its subsequent condensation from solution: Kraft lignin (SO4-L). This is the most widely used method in industry at present. The lignin was obtained according to the protocols described in [ 13 – 14 ]. Briefly, the delignification process of birch chips was carried out with an aqueous solution containing sodium hydroxide and sodium sulfide. The delignification process is carried out with successive stepwise increase in temperature for more effective impregnation of wood. The total pulping time was 2 hours and 40 minutes, and the temperature ranged from 115°C to 160°C. Next, an alkaline lignin solution was obtained, from which lignin was extracted by acidifying the solution with sulfuric acid to pH = 3. The precipitated lignin was washed thoroughly with clean water. The yield of kraft lignin was 60.6 ± 1.4% of the total amount of lignin in birch xylem. Hydrolyzed lignin (Hydr-L). This is one of the oldest processes used in the pulp and paper industry. Birch chips were treated with 1% sulfuric acid at 180°C, and the duration of the whole process was 4.5 hr. The obtained Hydr-L and hydrolysates were separated and the lignin was washed thoroughly with clean water. The yield of Hydr-L was 95.5 ± 1.5% of the amount of lignin in birch xylem. Alkali lignin (Na-L). The lignin dissolution process uses an aqueous solution of sodium hydroxide at elevated temperature. The process runs for 4 hours at temperatures up to 150°C. Then lignin is precipitated from the solution in the same way as in other processes, by acidification with sulfuric acid and further washing. The sample yield was 62.5 ± 2.0% of the amount of lignin in birch xylem. Soda-ethanol lignin (EtOH-L). Lignin was prepared according to the protocols described in [ 12 ]. The process is similar to the preparation of alkali lignin, but with the addition of ethanol. Birch xylem chips were heated in 40% aqueous ethanol solution, with the addition of 5% NaOH. The process is carried out for 4 hours at temperatures up to 150°C. Then lignin is precipitated from the solution in the same way as in other processes, by acidification with sulfuric acid and further washing. The yield of soda-ethanol lignin was 45.4 ± 1.1% of the amount of lignin in birch xylem. Lignin isolated by dimethylsulfoxide (DMSO-L). Лигнин получали согласно протоколов, описанных в [ 11 ]. Данный процесс также относится к органосольвентным процессам и характеризуется меньшими изменениями структуры лигнина. Для получения лигнина чипсы березы обрабатывали водным раствором ДМСО (85%) с кислотным катализатором – серная кислота в концентрации 0.2%. Общая продолжительность процесса составила 2 часа при 156 °С. Выход lignin isolated by dimethylsulfoxide составил 80,7 ± 0,7% от количества лигнина в ксилеме березы. Lignin isolated by dimethylsulfoxide (DMSO-L). Lignin was obtained according to the protocols described in [ 11 ]. This process also refers to organosolvent processes and is characterized by smaller changes in lignin structure. To obtain lignin, birch chips were treated with an aqueous solution of DMSO (85%) with acid catalyst - sulfuric acid at a concentration of 0.2%. The total duration of the process was 2 hours at 156°C. The yield of lignin isolated by dimethylsulfoxide was 80.7 ± 0.7% of the amount of lignin in birch xylem. In addition to investigating lignins that are produced by industrial processes, we have investigated low-altered lignins. Dioxanelignin (DL). DL is the most typical representative of low-altered lignins and is often used to characterize lignin in the cell wall. The procedure for separating wood into polymeric constituents and isolating the lignin sample was performed according to our previous protocols [ 12 , 15 – 16 ] based on Pepper's method [ 17 ]. Briefly, birch chips were brought to sawdust size. Then, they were suspended in an aqueous solution of dioxane (90%) with 1.8% HCl, and boiled with a reverse refrigerator for 2 h in a nitrogen current. The lignin was precipitated from the solution by acidification, filtered, neutralized with distilled water and concentrated using a rotary evaporator. It was further dried in a vacuum oven. The yield of DL was 41.1 ± 0.6% of the amount of lignin in birch xylem. The Millipore Simplicity UV system (Merck KGaA, Germany) were used for purification and distillation of the water for all experiments. Other chemical solvents and reagents used for delignification were purchased from Aldosa (Russia). Chemical solvents and reagents for NMR analysis were used from Sigma-Aldrich (Merck KGaA, Germany). All experiments were repeated in three iterations to obtain reliable results. 2. Nuclear magnetic resonance spectroscopy NMR spectrometer Bruker AVANCE III™ 600 was used for the registration of NMR spectra. The obtained data were primary processed with software of spectrometer TopSpin 3.2. High-resolution NMR The prepared solution of Cr(AcAc)(III) in DMSO-d6 (concentration of 6 mg/mL) was used to dissolve the studied samples of soluble lignins. The sample weight was dissolved in 0.6 mL of the mentioned solution and transported the mixture to NMR sample tube (5 mm). The parameters of 13 C-NMR experiment are represented in Table 1 . Table 1 Parameters of 13 C-NMR spectra registration. Parameter 13 C-NMR Pulse length, µs 12 Acquisition time, s 0.909 Delay, s 2 Spectral width, ppm 238,9 Number of scans 22528 Sample temperature, K 298 In addition to 13 C-NMR, HSQC NMR spectra were registered. The point size was 1024×256, number of scans was 36, delay was 2 s. Solid-state NMR The powder of sample was placed into ceramic rotor with diameter of 3.2 mm. CP/MAS method was used to improve the signal-to-noise ratio and spectra resolution. The parameters of 13 C-NMR experiment are represented in Table 2 . Table 2 Experimental parameters for solid-state NMR. Parameter 13 C-NMR Contact time, µs 2000 Acquisition time, s 0.022 Delay, s 4 Spectral width, ppm 300 Number of scans 8192 MAS rate, kHz 13 3. Mathematical analysis of spectral data The solid-state and high resolution spectra were converted into numerical data sets. These data sets contain information on signal intensity and chemical shift. All chemical shifts were then compared, resulting in a matrix of rows containing signal intensities for 11 lignin samples at the same chemical shift. The resulting dataset was examined using hierarchical cluster analysis (HCA). Hierarchical cluster analysis (HCA) is a method for investigating grouping of data. It is based on a procedure that maps relationships in the data using averaging. HCA finds and extracts features from the dataset as well as patterns of similarity and then estimates the distances between the datasets and then according to the distances obtained, it sorts the original datasets into groups and produces a dendrogram. [ 9 ]. The parameters correlation matrix and listwise were selected for PCA analysis. HCA distances were compared using Ward classification based on Euclidean distance type. The mathematical processing methods were implemented using Origin® Pro 2021 software. Results and discussion 1 Calculation of correlations between high-resolution and solid-state spectra 13 C-NMR spectra were registered of several lignin samples in dissolved and solid state in order to test the presence of correlations between the intensities of corresponding signals. This experiment was done for three technical lignins, which are soluble in standard solvents for NMR. The example of 13 C-NMR spectra of dissolved and solid lignin sample is presented at Fig. 1 . The signals on the spectra of the dissolved and solid sample correspond to each other, but the width of the signals in the solid state NMR spectrum is much larger, and the intensity distribution is significantly different from the high resolution spectrum. In the high resolution spectrum (Fig. 1 b) there is also a signal in the region of 39 ppm, corresponding to the signal of DMSO solvent. We selected and fixated the chemical shifts ranges for integration of signals intensities in all spectra. The reference of spectral signals to atom groups in substances was implemented according to the literature data (Table 3 ) [ 17 – 20 ]. All intensities were normalized on mass with the purpose to exclude its influence on calculation results. Table 3 Integrated ranges of chemical shifts and atom groups, corresponding to them. Range, ppm Groups 162.8–142.6 Ar-CH = CH-CHO 142.6–120.0 H 2,6 120.0–95.1 G 2 ; S 2,6 ; PhGly; GlcU 95.1–67.8 β-5 (phenylcoumarone); S β-O-4 E; S β-O-4 T; β-β (resinol); G β-O-4 E + T; α-CO/β-O-4; BE; β-O-4/α-OH 67.8–45.7 γ-ethers; OMe; Hk The integration of spectra signals was implemented with the tools of spectrometer software. After integration of signals and their normalization on samples masses we calculated the ratio of integral intensities of signals in liquid (I l ) and solid (I s ) samples in corresponding chemical shifts ranges (Table 4 ). Table 4 The ratio of integral intensities of corresponding ranges of chemical shifts in 13 C-NMR spectra of analyzed lignins. Chemical shift, ppm I s /I l EtOH-L DMSO-L Na-L 162.8–142.6 0.091 0.060 0.075 142.6–120.0 0.088 0.048 0.062 120.0–95.1 0.063 0.033 0.069 95.1–67.8 0.087 0.070 0.192 67.8–45.7 0.110 0.108 0.137 At the current stage of research, the comparison of integral intensities in corresponding ranges did not allow confirming the presence of correlations between intensities of NMR signals in liquid and solid lignins samples. The possible reason could be a presence or absence of lignins fragments, as well as contaminants of extraneous substances, which presence in sample depends on the method of isolation. In particular, technical lignins contain large amounts of sugars as impurities. The registered HSQC spectra of studied lignins allow to determine the correlations, which can be assigned to the groups of carbohydrates (Fig. 2 ). According to the presented spectrum it is possible to note presence of characteristic cross-peak for β-D-xylopyranose 97.2/4.22, and also other cross-peaks proceeding from structure of this sugar in a range 70–80/2.8–3.7, high intensity of these cross-peaks speaks about high content of these impurities in a sample. We attempted to separate carbohydrates signals from signals of lignin fragments and calculate their fraction in general integral intensity with application of HSQC NMR spectroscopy, but the subtraction of carbohydrates fraction of intensity did not change the resulting ratio. Native low-altered lignins contain less sugars; however, in such lignins sugars are linked to lignin by covalent bonds, forming LCC lignocarbohydrate complexes [ 21 ]. As a consequence, on the one hand, the macromolecules of LCC and technical lignins behave differently in solution; on the other hand, the effect of cross-polarization on the intensities of carbon signals of lignin and residual sugars in undissolved samples of technical lignins becomes uncertain, which limits the possibility of comparing the signal intensities of the same sample in the dissolved and undissolved states. The molecular weight distribution of lignins also contributes to the uncertainty. Low-altered native lignins have smaller molecular masses, while technical lignins have large molecular masses due to condensation processes during their isolation, which has an unpredictable effect on the signal intensities of solid-state spectra. Also, one of the possible reasons for the lack of correlations could be the influence of relaxant, which is used to shorten the time of the high-resolution NMR experiment. 2 Mathematical analysis of spectral data. HCA method was used to classify technical and native birch lignins from high-resolution and solid-state 13 C NMR spectra data. In the current study, birch lignins are classified according to the degree of alteration as measured by 13 C NMR spectra in dissolved lignins. This is important to characterize the macromolecule structure to determine the future use of lignins. But some technical lignins are insoluble, so we decided to use high-resolution spectra for classification at the first stage. Then, based on the obtained data, the approach was extrapolated to obtain a classification based on solid state NMR data processing, including insoluble lignin in this classification. 1. “Size of real spectrum” value was set to 2048 points for each 13 C-NMR spectrum in TopSpin software in order to unify the data for mathematical processing. 2. The chemical shifts range of 3-180 ppm, containing the target lignin signals, was selected and stored as a dataset in a text document. In this way, data sets for each sample of 1204 points (3-180 ppm region) were obtained. 3. These data sets were lined up in rows and transferred to Origin® Pro 2021 software for mathematical processing. 4. Data were clustered with use of the Multivariate Analysis and Hierarchical Cluster Analysis tools, using the Ward method with the Euclidean distance type. Ward's method is a hierarchical clustering algorithm that is used to minimize the sum of squares within each cluster. This means that it seeks to form clusters in such a way that the total variation within a cluster is minimized [22]. Ward's distance estimation function (1) is usually expressed as the sum of squares of the differences between elements within a cluster: $$W\left(C\right)= {\sum }_{i=1}^{n}{({x}_{i}-{\mu }_{C})}^{2}$$ 1 where W(C) is the sum of squares within cluster C, x i are the elements of the cluster, µ c is the mean value of the elements in cluster C. 5. As a result of the transformations, clustering is represented as a dendrogram that displays similarities in mathematical datasets. The HCA dendrogram (Fig. 3 ) shows that the proposed method of classification from solid state 13 C NMR spectra works as well as for 13 C NMR spectra of dissolved samples. Two main clusters can be seen in the diagram. Moreover, these clusters and subclusters are the same for the lignin structures clustered for both solid state and dissolved sample spectra. The first cluster (Fig. 3 , red line) includes data obtained from the dissolved sample spectra, and the second cluster (Fig. 3 , blue line) includes data from the solid state spectra. In the blue cluster, a sample of insoluble lignin (hydrolyzed lignin) is added and is assigned to the group with lignin isolated by dimethylsulfoxide. This indicates that this lignin has the same processing prospects as lignin isolated by dimethylsulfoxide. Conclusions At the current stage of research, the comparison of integral intensities in corresponding ranges did not allow confirming the presence of direct correlations between intensities of NMR signals in liquid and solid lignins samples because of the presence of carbohydrates. However, the HCA showed that lignins can be classified on the basis of solid-state spectra. We have shown that lignins are grouped in the same way both when processing high-resolution spectra and solid-state spectra using the HCA method. It can indicate both the similarity of the structures of these samples, and the supposing presence of the correlations of spectral data for samples in dissolved and solid state. The approach to classify lignins was proposed. This approach is based on their structural features using hierarchical cluster analysis from solid-state 13 C NMR spectra. This approach allows to classify insoluble lignins. In particular, it is shown that hydrolyzed lignin belongs to the group of highly modified lignins together with lignin isolated by dimethylsulfoxide. Declarations Funding and Acknowledgments This research was funded by the Russian Science Foundation, grant number 22-13-20015. This research was performed using an instrumentation of the Core Facility Center “Arktika” of the Northern (Arctic) Federal University named after M.V. Lomonosov. Author Contribution All authors obtainted and processed experimental data. I.G., A.K. and S.S. wrote the main manuscript text. S.S. , I.G. and Y.S. prepared figures 1-3. All authors reviewed the manuscript. References Calvo-Flores F. G. et al. in Lignin and lignans as renewable raw materials, ed. By John Wiley & Sons (2015). Faleva, A. V., Kozhevnikov, A. Y., Pokryshkin, S. A., Falev, D. I., Shestakov, S. L., & Popova, J. A., J. Wood Chem. Technol. (2020) https://doi.org/10.1080/02773813.2020.1722702 Haq I., Mazumder P., Kalamdhad A. S., Bioresour. 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Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2024 Read the published version in Applied Magnetic Resonance → Version 1 posted Editorial decision: Revision requested 20 Jun, 2024 Reviews received at journal 20 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers agreed at journal 09 Jun, 2024 Reviewers invited by journal 08 Jun, 2024 Editor assigned by journal 07 Jun, 2024 Submission checks completed at journal 06 Jun, 2024 First submitted to journal 06 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4540243","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317006219,"identity":"30f03736-ccde-4776-b955-4211277ff92a","order_by":0,"name":"Ilya Grishanovich","email":"","orcid":"","institution":"Northern (Arctic) Federal University","correspondingAuthor":false,"prefix":"","firstName":"Ilya","middleName":"","lastName":"Grishanovich","suffix":""},{"id":317006220,"identity":"7b41fd52-ea60-4279-b842-ec2d337ed279","order_by":1,"name":"Yuliya Sypalova","email":"","orcid":"","institution":"Northern (Arctic) Federal University","correspondingAuthor":false,"prefix":"","firstName":"Yuliya","middleName":"","lastName":"Sypalova","suffix":""},{"id":317006221,"identity":"dc3066b0-b11b-45b9-805d-059bac88342c","order_by":2,"name":"Semyon Shestakov","email":"","orcid":"","institution":"Northern (Arctic) Federal University","correspondingAuthor":false,"prefix":"","firstName":"Semyon","middleName":"","lastName":"Shestakov","suffix":""},{"id":317006222,"identity":"006112b3-fd19-44cb-ad1a-07e4d674af50","order_by":3,"name":"Aleksandr Kozhevnikov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACHgYGZhDNBmQwfADy+UjSwjgDyGcjWguYAbKJoBb+nsOHPxdUHM7j4zl8TNo2x06GjYH3mQQ+LRJn2xKMZ5w5XMzG25YmnbstGegwdjO8WhjO8xgkA1UntvHzGBvnbmMGamFjw6tFHqjlMO8/qBbLbfWEtRic7TFs5m2wSWzj7TF8zLjtMGEthmeOJTPzHANq4TmW+LB323EeNmY2Zgt8WuTOJB/+zFMjkTi/J/nAgZ/bqu352dsYb+DTggUwk6h+FIyCUTAKRgEmAAD6+Tkqs8KbLgAAAABJRU5ErkJggg==","orcid":"","institution":"Northern (Arctic) Federal University","correspondingAuthor":true,"prefix":"","firstName":"Aleksandr","middleName":"","lastName":"Kozhevnikov","suffix":""}],"badges":[],"createdAt":"2024-06-06 12:09:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4540243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4540243/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00723-024-01686-4","type":"published","date":"2024-07-22T00:33:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58786982,"identity":"33c30057-ffc9-4918-a4bf-91e600f7ea50","added_by":"auto","created_at":"2024-06-21 06:21:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003csup\u003e13\u003c/sup\u003eC-NMR spectra of solid (a) and dissolved (b) lignin sample (Na-L)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4540243/v1/2bfa394ec57df87012f5d354.png"},{"id":58786993,"identity":"ce738fb0-8131-4ced-9c88-de484720093c","added_by":"auto","created_at":"2024-06-21 06:21:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219165,"visible":true,"origin":"","legend":"\u003cp\u003eFragment of HSQC NMR spectrum of Na-L\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4540243/v1/4f8858c9ceb9bb001e6d39d5.png"},{"id":58786983,"identity":"560914e4-7f4f-4cfa-8c0f-43bd6973b863","added_by":"auto","created_at":"2024-06-21 06:21:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10100,"visible":true,"origin":"","legend":"\u003cp\u003eHCA for obtained\u0026nbsp;\u003csup\u003e13\u003c/sup\u003eC-NMR spectra. Red line – dissolved samples, blue line – solid samples. DMSO-L – lignin isolated by dimethylsulfoxide, EtOH – soda-ethanol lignin, Na-L – alkali lignin, DL – dioxanelignin, Hydr-L – hydrolyzed lignin, SO4-L – kraft lignin\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4540243/v1/59ac91b836954fcf29926990.png"},{"id":60858372,"identity":"ff59525c-ee04-4544-a449-47270e8bb45e","added_by":"auto","created_at":"2024-07-23 00:34:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4540243/v1/418c6d0c-8307-4c10-8382-17bd85c03849.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe application of hierarchical cluster analysis to lignins classification based on data of high resolution NMR and solid-state NMR spectra on \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC nuclei\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLignin is a complex organic polymer that is one of the main components of the cell walls of plants, especially woody plants. It gives plants strength and rigidity and protects them from pathogens and mechanical damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lignin makes up 15\u0026ndash;30% of wood weight and is one of the most abundant biopolymers on Earth along with cellulose [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study of the structure and properties of lignin is important for the development of effective methods of its processing and utilization. Lignin is a potential source of aromatic compounds that can be used in the production of biofuels, chemicals, and other materials [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Knowledge of its structure will make it possible to optimize depolymerization processes, improve catalytic processes to convert lignin into valuable products, and develop new materials such as bioplastics, carbon fibers, and others [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModern methods of lignin study are aimed at investigating its properties and chemical structure. Despite this, the structure of lignin has not been fully studied, since each lignin molecule is unique, and the structure of lignin depends both on the botanical origin of the raw material [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and on the conditions of the procedure for its production [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To find ways to utilize and process lignin, it is important to know not so much the structure of lignin as the degree of its modification [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], due to the fact that each lignin molecule has a unique structure.\u003c/p\u003e \u003cp\u003eSince the study of lignins by chemical methods is difficult due to the variability of their structure, various physical methods are successfully used to study lignins. Nuclear magnetic resonance (NMR) spectroscopy is used to determine the structure and composition of lignin fragments at the molecular level [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Infrared spectroscopy (FTIR) helps to identify functional groups in lignin molecules. Mass spectrometry (MS) is used to analyze the molecular weight and structure of lignin fragments. X-ray photoelectron spectroscopy (XPS) is used to study the surface chemistry of lignin. Gel permeation chromatography (GPC) is used to determine the molecular weight distribution of lignin [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is worth noting that NMR spectroscopy is currently the main and most promising tool for such objects as lignin.\u003c/p\u003e \u003cp\u003eSoluble lignins make it possible to study their structure by methods of one-dimensional and two-dimensional high-resolution NMR spectroscopy, both with and without prior derivatization [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, many lignins isolated by industrial methods are practically insoluble in standard solvents used for NMR, which leads to the impossibility of experiments with liquid samples. At the same time, the application of solid-state NMR is limited by the fact that the quality of solid state \u003csup\u003e13\u003c/sup\u003eC-NMR spectra of lignin are much worse than \u003csup\u003e13\u003c/sup\u003eC-NMR spectra of dissolved samples. In addition, CP/MAS method distorts the quantitative ratio of integral intensities of signals in \u003csup\u003e13\u003c/sup\u003eC-NMR spectra, due to the transferring of magnetization from hydrogen nuclei to carbon nuclei linked to them [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe degree of alteration of lignin is rather difficult to trace from solid-state NMR data. This problem can be solved by applying deep mathematical analysis of the data, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA). These methods can discard most of the same data and focus on the differences in spectral data [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the main idea of this study is to determine the presence of possible correlations between the signal intensities in \u003csup\u003e13\u003c/sup\u003eC-NMR spectra of lignins registered in dissolved and solid state and to develop an approach to the classification of lignins using solid state \u003csup\u003e13\u003c/sup\u003eC-NMR spectra\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Lignin isolation\u003c/h2\u003e \u003cp\u003eLignin of birch (\u003cem\u003eB\u0026eacute;tula p\u0026eacute;ndula\u003c/em\u003e), isolated by various methods, was used as the object of study. Birch wood was chosen due to several reasons, shown in our previous works. First, it is a traditional raw material in the pulp and paper industry [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Second, birch lignin has a more diverse structure compared to softwood lignins and other hardwood lignins. As evidence of diversity, it is possible to note a rather high ratio of the number of syringyl and guaiacyl aromatic rings of lignin, which is one of the most important characteristics of the lignin macromolecule [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor delignification, xylem chips from a 40-year-old birch tree that grew in a temperate climatic zone (Arkhangelsk region, Russia) were used. The chips were obtained the same as those used for delignification in industrial methods of pulp production: the length of the chips is from 15 to 25 mm, width is about 20 mm, thickness is 3\u0026ndash;5 mm, cutting angle is 45 degrees, which provides good impregnation with chemical reagents. Extractives were removed from the chips by acetone extraction, pre-drying the raw material to air-dry condition. Pre-extraction allows to remove from birch xylem such compounds as aldehydes such as furfurols, terpenoids, lignans and other compounds with low molecular weight, which can contribute to NMR signals and hinder the accurate determination of lignin. The amount of extractives was of the order of 8%.\u003c/p\u003e \u003cp\u003eThe delignification of birch wood was carried out both by industrial methods and by methods that allow obtaining low-altered lignin, where the lignin structure undergoes minimal changes, as far as possible.\u003c/p\u003e \u003cp\u003eThe following processes were used as industrial lignin samples, where structures undergo significant changes during the processes of lignin dissolution and its subsequent condensation from solution:\u003c/p\u003e \u003cp\u003eKraft lignin (SO4-L). This is the most widely used method in industry at present. The lignin was obtained according to the protocols described in [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Briefly, the delignification process of birch chips was carried out with an aqueous solution containing sodium hydroxide and sodium sulfide. The delignification process is carried out with successive stepwise increase in temperature for more effective impregnation of wood. The total pulping time was 2 hours and 40 minutes, and the temperature ranged from 115\u0026deg;C to 160\u0026deg;C. Next, an alkaline lignin solution was obtained, from which lignin was extracted by acidifying the solution with sulfuric acid to pH\u0026thinsp;=\u0026thinsp;3. The precipitated lignin was washed thoroughly with clean water. The yield of kraft lignin was 60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4% of the total amount of lignin in birch xylem.\u003c/p\u003e \u003cp\u003eHydrolyzed lignin (Hydr-L). This is one of the oldest processes used in the pulp and paper industry. Birch chips were treated with 1% sulfuric acid at 180\u0026deg;C, and the duration of the whole process was 4.5 hr. The obtained Hydr-L and hydrolysates were separated and the lignin was washed thoroughly with clean water. The yield of Hydr-L was 95.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5% of the amount of lignin in birch xylem.\u003c/p\u003e \u003cp\u003eAlkali lignin (Na-L). The lignin dissolution process uses an aqueous solution of sodium hydroxide at elevated temperature. The process runs for 4 hours at temperatures up to 150\u0026deg;C. Then lignin is precipitated from the solution in the same way as in other processes, by acidification with sulfuric acid and further washing. The sample yield was 62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0% of the amount of lignin in birch xylem.\u003c/p\u003e \u003cp\u003eSoda-ethanol lignin (EtOH-L). Lignin was prepared according to the protocols described in [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The process is similar to the preparation of alkali lignin, but with the addition of ethanol. Birch xylem chips were heated in 40% aqueous ethanol solution, with the addition of 5% NaOH. The process is carried out for 4 hours at temperatures up to 150\u0026deg;C. Then lignin is precipitated from the solution in the same way as in other processes, by acidification with sulfuric acid and further washing. The yield of soda-ethanol lignin was 45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1% of the amount of lignin in birch xylem.\u003c/p\u003e \u003cp\u003eLignin isolated by dimethylsulfoxide (DMSO-L). Лигнин получали согласно протоколов, описанных в [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Данный процесс также относится к органосольвентным процессам и характеризуется меньшими изменениями структуры лигнина. Для получения лигнина чипсы березы обрабатывали водным раствором ДМСО (85%) с кислотным катализатором \u0026ndash; серная кислота в концентрации 0.2%. Общая продолжительность процесса составила 2 часа при 156 \u0026deg;С. Выход lignin isolated by dimethylsulfoxide составил 80,7\u0026thinsp;\u0026plusmn;\u0026thinsp;0,7% от количества лигнина в ксилеме березы.\u003c/p\u003e \u003cp\u003eLignin isolated by dimethylsulfoxide (DMSO-L). Lignin was obtained according to the protocols described in [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This process also refers to organosolvent processes and is characterized by smaller changes in lignin structure. To obtain lignin, birch chips were treated with an aqueous solution of DMSO (85%) with acid catalyst - sulfuric acid at a concentration of 0.2%. The total duration of the process was 2 hours at 156\u0026deg;C. The yield of lignin isolated by dimethylsulfoxide was 80.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7% of the amount of lignin in birch xylem.\u003c/p\u003e \u003cp\u003eIn addition to investigating lignins that are produced by industrial processes, we have investigated low-altered lignins.\u003c/p\u003e \u003cp\u003eDioxanelignin (DL). DL is the most typical representative of low-altered lignins and is often used to characterize lignin in the cell wall. The procedure for separating wood into polymeric constituents and isolating the lignin sample was performed according to our previous protocols [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] based on Pepper's method [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Briefly, birch chips were brought to sawdust size. Then, they were suspended in an aqueous solution of dioxane (90%) with 1.8% HCl, and boiled with a reverse refrigerator for 2 h in a nitrogen current. The lignin was precipitated from the solution by acidification, filtered, neutralized with distilled water and concentrated using a rotary evaporator. It was further dried in a vacuum oven. The yield of DL was 41.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6% of the amount of lignin in birch xylem.\u003c/p\u003e \u003cp\u003eThe Millipore Simplicity UV system (Merck KGaA, Germany) were used for purification and distillation of the water for all experiments. Other chemical solvents and reagents used for delignification were purchased from Aldosa (Russia). Chemical solvents and reagents for NMR analysis were used from Sigma-Aldrich (Merck KGaA, Germany). All experiments were repeated in three iterations to obtain reliable results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2. Nuclear magnetic resonance spectroscopy\u003c/h2\u003e \u003cp\u003eNMR spectrometer Bruker AVANCE III\u0026trade; 600 was used for the registration of NMR spectra. The obtained data were primary processed with software of spectrometer TopSpin 3.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHigh-resolution NMR\u003c/h2\u003e \u003cp\u003eThe prepared solution of Cr(AcAc)(III) in DMSO-d6 (concentration of 6 mg/mL) was used to dissolve the studied samples of soluble lignins. The sample weight was dissolved in 0.6 mL of the mentioned solution and transported the mixture to NMR sample tube (5 mm). The parameters of \u003csup\u003e13\u003c/sup\u003eC-NMR experiment are represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of \u003csup\u003e13\u003c/sup\u003eC-NMR spectra registration.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003e13\u003c/sup\u003eC-NMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse length, \u0026micro;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquisition time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral width, ppm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of scans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample temperature, K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition to \u003csup\u003e13\u003c/sup\u003eC-NMR, HSQC NMR spectra were registered. The point size was 1024\u0026times;256, number of scans was 36, delay was 2 s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSolid-state NMR\u003c/h2\u003e \u003cp\u003eThe powder of sample was placed into ceramic rotor with diameter of 3.2 mm. CP/MAS method was used to improve the signal-to-noise ratio and spectra resolution. The parameters of \u003csup\u003e13\u003c/sup\u003eC-NMR experiment are represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental parameters for solid-state NMR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003e13\u003c/sup\u003eC-NMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContact time, \u0026micro;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquisition time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral width, ppm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of scans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAS rate, kHz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3. Mathematical analysis of spectral data\u003c/h2\u003e \u003cp\u003eThe solid-state and high resolution spectra were converted into numerical data sets. These data sets contain information on signal intensity and chemical shift. All chemical shifts were then compared, resulting in a matrix of rows containing signal intensities for 11 lignin samples at the same chemical shift. The resulting dataset was examined using hierarchical cluster analysis (HCA).\u003c/p\u003e \u003cp\u003eHierarchical cluster analysis (HCA) is a method for investigating grouping of data. It is based on a procedure that maps relationships in the data using averaging. HCA finds and extracts features from the dataset as well as patterns of similarity and then estimates the distances between the datasets and then according to the distances obtained, it sorts the original datasets into groups and produces a dendrogram. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe parameters correlation matrix and listwise were selected for PCA analysis. HCA distances were compared using Ward classification based on Euclidean distance type.\u003c/p\u003e \u003cp\u003eThe mathematical processing methods were implemented using Origin\u0026reg; Pro 2021 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e1 Calculation of correlations between high-resolution and solid-state spectra\u003c/h2\u003e\n \u003cp\u003e\u003csup\u003e13\u003c/sup\u003eC-NMR spectra were registered of several lignin samples in dissolved and solid state in order to test the presence of correlations between the intensities of corresponding signals. This experiment was done for three technical lignins, which are soluble in standard solvents for NMR. The example of \u003csup\u003e13\u003c/sup\u003eC-NMR spectra of dissolved and solid lignin sample is presented at Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe signals on the spectra of the dissolved and solid sample correspond to each other, but the width of the signals in the solid state NMR spectrum is much larger, and the intensity distribution is significantly different from the high resolution spectrum. In the high resolution spectrum (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb) there is also a signal in the region of 39 ppm, corresponding to the signal of DMSO solvent.\u003c/p\u003e\n \u003cp\u003eWe selected and fixated the chemical shifts ranges for integration of signals intensities in all spectra. The reference of spectral signals to atom groups in substances was implemented according to the literature data (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. All intensities were normalized on mass with the purpose to exclude its influence on calculation results.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntegrated ranges of chemical shifts and atom groups, corresponding to them.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange, ppm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.8\u0026ndash;142.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAr-CH\u0026thinsp;=\u0026thinsp;CH-CHO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.6\u0026ndash;120.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csub\u003e2,6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.0\u0026ndash;95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003csub\u003e2\u003c/sub\u003e; S\u003csub\u003e2,6\u003c/sub\u003e; PhGly; GlcU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.1\u0026ndash;67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;-5 (phenylcoumarone); S \u0026beta;-O-4 E; S \u0026beta;-O-4 T; \u0026beta;-\u0026beta; (resinol); G \u0026beta;-O-4 E\u0026thinsp;+\u0026thinsp;T; \u0026alpha;-CO/\u0026beta;-O-4; BE; \u0026beta;-O-4/\u0026alpha;-OH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.8\u0026ndash;45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-ethers; OMe; Hk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe integration of spectra signals was implemented with the tools of spectrometer software. After integration of signals and their normalization on samples masses we calculated the ratio of integral intensities of signals in liquid (I\u003csub\u003el\u003c/sub\u003e) and solid (I\u003csub\u003es\u003c/sub\u003e) samples in corresponding chemical shifts ranges (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe ratio of integral intensities of corresponding ranges of chemical shifts in \u003csup\u003e13\u003c/sup\u003eC-NMR spectra of analyzed lignins.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eChemical shift, ppm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eI\u003csub\u003es\u003c/sub\u003e/I\u003csub\u003el\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEtOH-L\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDMSO-L\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNa-L\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.8\u0026ndash;142.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.6\u0026ndash;120.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.0\u0026ndash;95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.1\u0026ndash;67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.8\u0026ndash;45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAt the current stage of research, the comparison of integral intensities in corresponding ranges did not allow confirming the presence of correlations between intensities of NMR signals in liquid and solid lignins samples. The possible reason could be a presence or absence of lignins fragments, as well as contaminants of extraneous substances, which presence in sample depends on the method of isolation. In particular, technical lignins contain large amounts of sugars as impurities. The registered HSQC spectra of studied lignins allow to determine the correlations, which can be assigned to the groups of carbohydrates (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the presented spectrum it is possible to note presence of characteristic cross-peak for \u0026beta;-D-xylopyranose 97.2/4.22, and also other cross-peaks proceeding from structure of this sugar in a range 70\u0026ndash;80/2.8\u0026ndash;3.7, high intensity of these cross-peaks speaks about high content of these impurities in a sample. We attempted to separate carbohydrates signals from signals of lignin fragments and calculate their fraction in general integral intensity with application of HSQC NMR spectroscopy, but the subtraction of carbohydrates fraction of intensity did not change the resulting ratio.\u003c/p\u003e\n \u003cp\u003eNative low-altered lignins contain less sugars; however, in such lignins sugars are linked to lignin by covalent bonds, forming LCC lignocarbohydrate complexes [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a consequence, on the one hand, the macromolecules of LCC and technical lignins behave differently in solution; on the other hand, the effect of cross-polarization on the intensities of carbon signals of lignin and residual sugars in undissolved samples of technical lignins becomes uncertain, which limits the possibility of comparing the signal intensities of the same sample in the dissolved and undissolved states. The molecular weight distribution of lignins also contributes to the uncertainty. Low-altered native lignins have smaller molecular masses, while technical lignins have large molecular masses due to condensation processes during their isolation, which has an unpredictable effect on the signal intensities of solid-state spectra. Also, one of the possible reasons for the lack of correlations could be the influence of relaxant, which is used to shorten the time of the high-resolution NMR experiment.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2 Mathematical analysis of spectral data.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHCA method was used to classify technical and native birch lignins from high-resolution and solid-state \u003csup\u003e13\u003c/sup\u003eC NMR spectra data. In the current study, birch lignins are classified according to the degree of alteration as measured by \u003csup\u003e13\u003c/sup\u003eC NMR spectra in dissolved lignins. This is important to characterize the macromolecule structure to determine the future use of lignins. But some technical lignins are insoluble, so we decided to use high-resolution spectra for classification at the first stage. Then, based on the obtained data, the approach was extrapolated to obtain a classification based on solid state NMR data processing, including insoluble lignin in this classification.\u003c/p\u003e\n \u003cp\u003e1. \u0026ldquo;Size of real spectrum\u0026rdquo; value was set to 2048 points for each \u003csup\u003e13\u003c/sup\u003eC-NMR spectrum in TopSpin software in order to unify the data for mathematical processing.\u003c/p\u003e\n \u003cp\u003e2. \u0026nbsp;The chemical shifts range of 3-180 ppm, containing the target lignin signals, was selected and stored as a dataset in a text document. In this way, data sets for each sample of 1204 points (3-180 ppm region) were obtained.\u003c/p\u003e\n \u003cp\u003e3. These data sets were lined up in rows and transferred to Origin\u0026reg; Pro 2021 software for mathematical processing.\u003c/p\u003e\n \u003cp\u003e4. Data were clustered with use of the Multivariate Analysis and Hierarchical Cluster Analysis tools, using the Ward method with the Euclidean distance type. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWard\u0026apos;s method is a hierarchical clustering algorithm that is used to minimize the sum of squares within each cluster. This means that it seeks to form clusters in such a way that the total variation within a cluster is minimized [22].\u003c/p\u003e\n \u003cp\u003eWard\u0026apos;s distance estimation function (1) is usually expressed as the sum of squares of the differences between elements within a cluster:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$W\\left(C\\right)= {\\sum }_{i=1}^{n}{({x}_{i}-{\\mu }_{C})}^{2}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere W(C) is the sum of squares within cluster C, x\u003csub\u003ei\u003c/sub\u003e are the elements of the cluster, \u0026micro;\u003csub\u003ec\u003c/sub\u003e is the mean value of the elements in cluster C.\u003c/p\u003e\n \u003cp\u003e5. As a result of the transformations, clustering is represented as a dendrogram that displays similarities in mathematical datasets.\u003c/p\u003e\n \u003cp\u003eThe HCA dendrogram (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) shows that the proposed method of classification from solid state \u003csup\u003e13\u003c/sup\u003eC NMR spectra works as well as for \u003csup\u003e13\u003c/sup\u003eC NMR spectra of dissolved samples.\u003c/p\u003e\n \u003cp\u003eTwo main clusters can be seen in the diagram. Moreover, these clusters and subclusters are the same for the lignin structures clustered for both solid state and dissolved sample spectra. The first cluster (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, red line) includes data obtained from the dissolved sample spectra, and the second cluster (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, blue line) includes data from the solid state spectra. In the blue cluster, a sample of insoluble lignin (hydrolyzed lignin) is added and is assigned to the group with lignin isolated by dimethylsulfoxide. This indicates that this lignin has the same processing prospects as lignin isolated by dimethylsulfoxide.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAt the current stage of research, the comparison of integral intensities in corresponding ranges did not allow confirming the presence of direct correlations between intensities of NMR signals in liquid and solid lignins samples because of the presence of carbohydrates.\u003c/p\u003e \u003cp\u003eHowever, the HCA showed that lignins can be classified on the basis of solid-state spectra. We have shown that lignins are grouped in the same way both when processing high-resolution spectra and solid-state spectra using the HCA method. It can indicate both the similarity of the structures of these samples, and the supposing presence of the correlations of spectral data for samples in dissolved and solid state.\u003c/p\u003e \u003cp\u003eThe approach to classify lignins was proposed. This approach is based on their structural features using hierarchical cluster analysis from solid-state \u003csup\u003e13\u003c/sup\u003eC NMR spectra. This approach allows to classify insoluble lignins. In particular, it is shown that hydrolyzed lignin belongs to the group of highly modified lignins together with lignin isolated by dimethylsulfoxide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding and Acknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Russian Science Foundation, grant number 22-13-20015.\u003c/p\u003e\n\u003cp\u003eThis research was performed using an instrumentation of the Core Facility Center \u0026ldquo;Arktika\u0026rdquo; of the Northern (Arctic) Federal University named after M.V. Lomonosov.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors obtainted and processed experimental data. I.G., A.K. and S.S. wrote the main manuscript text. S.S. , I.G. and Y.S. prepared figures 1-3. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCalvo-Flores F. 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Jameel, Holzforschung (2016) https://doi.org/10.1515/hf-2014-0328. \u003c/li\u003e\n\u003cli\u003eJ.S. Mun, J.A. Pe III, S.P. Mun, Molecules (2021), https://doi.org/10.3390/molecules26164861.\u003c/li\u003e\n\u003cli\u003eFaleva, A. V., Belesov, A. V., Kozhevnikov, A. Y., Falev, D. I., Chukhchin, D. G., Novozhilov, E. V., International Journal of Biological Macromolecules (2021) https://doi.org/10.1016/j.ijbiomac.2020.10.248\u003c/li\u003e\n\u003cli\u003eWard Jr J. H., JASA (1963) https://doi.org/10.1080/01621459.1963.10500845\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-magnetic-resonance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apmr","sideBox":"Learn more about [Applied Magnetic Resonance](http://link.springer.com/journal/723)","snPcode":"723","submissionUrl":"https://submission.nature.com/new-submission/723/3","title":"Applied Magnetic Resonance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4540243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4540243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLignin is the second most abundant biological polymer on Earth with a complex chemical structure. A large amount of different technical lignins are produced as a waste product of the pulp and paper industry, and are not used rationally. The study of the structure of such lignins is relevant due to its potential applications. It is important to obtain comprehensive knowledge about the structure of lignin macromolecule and to classify lignins based on it.\u003c/p\u003e \u003cp\u003eHigh resolution NMR (nuclear magnetic resonance) experiments for dissolved samples are widely used to study this biopolymer. However, this approach does not allow studying insoluble technical lignins. Solid state NMR spectroscopy may become a solution of this problem.\u003c/p\u003e \u003cp\u003eIn this paper, we propose an approach to classify the degree of lignin alteration by clustering of solid state spectra with HCA (hierarchical cluster analysis) method. This approach is important because of the lack of direct correlations between the NMR spectra of lignins in the dissolved and solid states, that is based on experimental data.\u003c/p\u003e","manuscriptTitle":"The application of hierarchical cluster analysis to lignins classification based on data of high resolution NMR and solid-state NMR spectra on 13C nuclei","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-21 06:21:05","doi":"10.21203/rs.3.rs-4540243/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-20T18:14:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-20T13:12:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T15:49:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297570738842937278506536132928083058731","date":"2024-06-10T08:24:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66101609799226377780245286658682172525","date":"2024-06-09T11:49:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-08T16:19:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-07T05:43:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-07T02:40:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Magnetic Resonance","date":"2024-06-06T12:08:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-magnetic-resonance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apmr","sideBox":"Learn more about [Applied Magnetic Resonance](http://link.springer.com/journal/723)","snPcode":"723","submissionUrl":"https://submission.nature.com/new-submission/723/3","title":"Applied Magnetic Resonance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8dda0084-3e88-4474-9fbb-d281d7cb7239","owner":[],"postedDate":"June 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T00:33:55+00:00","versionOfRecord":{"articleIdentity":"rs-4540243","link":"https://doi.org/10.1007/s00723-024-01686-4","journal":{"identity":"applied-magnetic-resonance","isVorOnly":false,"title":"Applied Magnetic Resonance"},"publishedOn":"2024-07-22 00:33:55","publishedOnDateReadable":"July 22nd, 2024"},"versionCreatedAt":"2024-06-21 06:21:05","video":"","vorDoi":"10.1007/s00723-024-01686-4","vorDoiUrl":"https://doi.org/10.1007/s00723-024-01686-4","workflowStages":[]},"version":"v1","identity":"rs-4540243","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4540243","identity":"rs-4540243","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}