# Complementarity of two-dimensional gas chromatography and two-dimensional liquid chromatography for the analysis of depolymerised lignin

Eliise Tammekivi, Hugo Lilti, Magali Batteau, Chantal Lorentz, Christophe

Geantet, Dorothée Laurenti, Karine Faure

► To cite this version:

Eliise Tammekivi, Hugo Lilti, Magali Batteau, Chantal Lorentz, Christophe Geantet, et al.. Complementarity of two-dimensional gas chromatography and two-dimensional liquid chromatography for the analysis of depolymerised lignin. Journal of Chromatography A, 2024, 1736, pp.465401. 10.1016/j.chroma.2024.465401. hal-04727323

HAL Id: hal-04727323

https://hal.science/hal-04727323v1

Submitted on 30 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1

2

# Complementarity of two-dimensional gas chromatography and two-dimensional liquid chromatography for the analysis of depolymerised lignin

3

4

Eliise Tammekiviª, Hugo Liltiᵇ, Magali Batteauª, Chantal Lorentzᵇ, Christophe Geantetᵇ, Dorothée Laurentiᵇ, Karine Faureª*

5

ªUniversite Claude Bernard Lyon 1, ISA UMR 5280, CNRS, 5 rue de la Doua, 69100 Villeurbanne, France

6

ᵇUniversite Claude Bernard Lyon 1, IRCELYON UMR 5256, CNRS, 2 Av. Albert Einstein, 69626 Villeurbanne, France

7

*Corresponding author: karine.faure@isa-lyon.fr, Phone number +33-437423686

8

## Abstract

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Two-dimensional gas chromatography (GC × GC) and two-dimensional liquid chromatography (LC × LC) are nowadays widely used in academia and industry due to their high separation power. However, as far as we know, the complementarity of these two techniques has not yet been thoroughly studied based on the analysis of the same one sample. Therefore, this was undertaken here by analysing the liquid fraction obtained after depolymerising a natural waste – lignin – with GC × GC and off-line comprehensive LC × SFC (SFC: supercritical fluid chromatography). Using complementary techniques is also important for lignin valorisation, as thorough structural characterisation of the depolymerised product can aid with developing and improving valorisation processes. For the tentative identification, NIST library was used for GC × GC-MS results and MS-DIAL together with SIRIUS for LC × SFC-MS/MS data. This allowed to study which compounds are detectable with the different 2D methods but also to discuss the limitations of the data analysis processes. The previous knowledge that LC techniques are more suitable than GC × GC for the analysis of larger oligomers and other low volatility compounds was confirmed; however, it was seen that GC × GC enabled the analysis of smaller compounds, such as aliphatic alcohols and saturated compounds. Overall, the study demonstrates the complementarity of the two techniques but also draws attention to the different detectable compound groups and classifications that the two techniques can provide.

26

27

28

**Keywords:** lignin; two-dimensional liquid chromatography; two-dimensional gas chromatography; supercritical fluid chromatography; depolymerisation

29

30

31

32

33

34

**Abbreviations:** GC × GC: two-dimensional gas chromatography; LC × LC: two-dimensional liquid chromatography; SFC: supercritical fluid chromatography; ¹D, ²D: first and second dimension, respectively; 1D, 2D: one-dimensional and two-dimensional chromatography, respectively; HRMS: high resolution mass spectrometry; DIA: data-independent acquisition; MtBE: methyl *tert*-butyl ether; 1-AA: 1-aminoanthracene; BPC: base peak chromatograms; BPR: backpressure regulator; DBE: Double Bond Equivalent; AIF: all ions fragmentation
35

# 1. Introduction

Gas and liquid chromatography (GC and LC, respectively) are one of the most widely used chromatographic tools in analytical chemistry. Both methods can be used for the target and non-target analysis of various samples, ranging from pollutants in the environment to biological fluids.<sup>1,2</sup> However, analytes that have similar interactions with the stationary (and mobile) phase can have similar retention times, resulting in the co-elution of the peaks. This can lead to incorrect identification or failure to detect minor analytes, even when high-resolution mass spectrometry (HRMS) is used. To overcome this obstacle, multidimensional techniques such as two-dimensional (2D) chromatography are applied more and more as they can provide higher peak capacity and sensitivity compared to one- dimensional (1D) chromatography.<sup>3,4</sup> Thus, 2D chromatography can be especially beneficial for the analysis of complex samples consisting of hundreds or even thousands of compounds. One class of complex materials are various biomasses, organic natural products commonly derived from plants or other living organisms. Extensive research is currently underway to develop efficient procedures to valorise biomass as biofuel, isolated chemicals, or novel biomaterials. Here, 2D chromatography can aid with the thorough structural characterisation of the final or intermediate products, as the obtained information can be used to monitor and improve the valorisation processes.<sup>5</sup>

One type of biomass that needs to be valorised more is lignin. Globally, lignin is the most abundant resource for renewable aromatic compounds and ranks second, after cellulose, as the most abundant natural material overall. In the paper and pulp industries, huge amounts of lignin are obtained as the by-product and around 98 % of it is burned mainly to power the same industries. However, this is a low-value application, while lignin has the potential to be converted into high-value products that would be a green alternative to petroleum-based products. For example, lignin could be used to isolate valuable chemicals (e.g., vanillin), converted into biofuel, or cooperated in the production of novel materials (e.g., lignin-based carbon fibres, bio-based adhesives). Structurally, lignin is an aromatic heteropolymer formed by the radical polymerisation of phenylpropanoid units, whose type and ratio depend on the exact lignin source. Generally, the most common building blocks are *p*-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol.<sup>6,7</sup>

Depending on the exact valorisation process, lignin can be used after isolation as technical lignin or chemically modified by functionalisation or depolymerisation to obtain molecules more suitable for the subsequent valorisation steps. In terms of depolymerisation, the process yields a complex mixture consisting of numerous monomers and oligomers, including isomers which can be especially difficult to separate with only one-dimensional chromatography.<sup>8</sup> In case of depolymerised lignin, mainly the monomeric part has been analysed with 2D chromatography.<sup>9–11</sup> The oligomeric part is much more understudied because of the larger and more complex molecules, which require specific sample preparation but also modified chromatographic and data analysis methods. For example, thanks to applying fractionation, derivatisation, and high-temperature GC × GC, Dao Thi *et. al.*<sup>12</sup> have been able to perform the analysis and even quantification of lignin oligomers up to trimers.

In terms of 2D chromatography techniques, GC × GC is much more widely applied for the analysis of various biomass derivatives than 2D LC.<sup>5,13</sup> The numerous studies (<sup>14–17</sup> to name only a few here) demonstrating the complementarity of 1D GC and 1D LC suggests that also the corresponding 2D methods should provide valuable complementary information. However, as far as we know, any type of LC × LC together with GC × GC have not yet been applied for the analysis of the same sample in one study. Therefore, this was undertaken in this work by analysing the liquid fraction obtained from the catalytic depolymerisation of lignin. Both 2D techniques were performed in comprehensive mode and in terms of separation, orthogonal dimensions were applied. In GC × GC, a reversed setup was employed by using a mid-polar column in the first dimension and a non-polar column in the second dimension. For the 2D LC analysis, a method implementing reversed phase LC (RPLC) as the first
dimension and supercritical fluid chromatography (SFC) as the second dimension was selected based on our recent work⁸.

For the MS data analysis, in view of compound identification, different approaches are typically used in GC × GC-MS and 2D LC-MS. As 70 eV electron ionisation (EI) is the benchmark method for GC × GC, the comparison of mass spectra to the spectra present in the NIST libraries is a convenient approach. However, as depending on the MS supplier the parameters vary for electrospray ionisation (ESI) used commonly after the LC or SFC separation, the utilisation of MS libraries is not so straightforward. One option is to use in-house built MS libraries. However, in case of complex natural mixtures, such as lignin, it is impossible to have all corresponding standards to do so. Another option is to use novel annotation softwares like SIRIUS¹⁸,¹⁹ or MS-FINDER²⁰,²¹ that provide formula and predict structure based on MS² fragmentation data. Thus, within this study, we present and discuss the results of one depolymerised lignin sample analysed with two 2D chromatography techniques: GC × GC-MS and RPLC × SFC-HRMS/MS.

## 2. Materials and methods

### 2.1 Chemicals and sample preparation

For the GC × GC analysis, HPLC grade methanol (MeOH) from Sigma-Aldrich (Darmstadt, Germany) was used to dissolve the sample with a concentration of 8 mg mL⁻¹. The obtained solution was then filtered through a 0.45 µm pore syringe filter.

For the off-line LC × SFC-Q-TOF-MS/MS analysis, the following products were used. LC-MS grade MeOH and acetonitrile (ACN) were purchased from Supelco (Bellefonte, PA, USA). HPLC grade methyl *tert*-butyl ether (MtBE) with a purity of ≥ 99.8 % was purchased from Sigma-Aldrich and formic acid (FA) with 95-97 % purity from Aldrich. For the SFC separation, pressurised liquid carbon dioxide (CO₂, 99.995 %) from Air Liquid (Pierre Bénite, France) was used. The ELGA Purelab Classic water purification system (Veolia water STI, Le Plessis Robinson, France) was used to produce water suitable for MS analyses.

The same depolymerised lignin sample was used for the GC × GC and LC × SFC analyses. In short, previously dried SODA P1000 lignin (wheat straw lignin) was mixed with CoMoS/Al₂O₃ catalyst and isobutanol in a batch reactor and kept at 280 °C under 60 bar of H₂ for 2h. After reaction and cooling, tetrahydrofuran was used to recover the produced bio-oil from the reactor. The obtained product, filtered and dried, was dissolved in acetone and *n*-heptane was added to precipitate the heavier fraction containing the larger oligomers. The lighter fraction in *n*-heptane was recovered and evaporated to dryness. The solid was in turn dissolved in MeOH to yield a concentration of 30 mg mL⁻¹, sonicated for 10 min, and part of the supernatant was carefully removed for the LC × SFC analysis. More details on the depolymerisation process will be published in future publications focusing on the catalytic depolymerisation of lignin.

### 2.2 Apparatus

#### 2.2.1 GC × GC-TOF

The GC × GC setup consisted of an Agilent 6890N gas chromatograph equipped with a ZOEX I thermal modulator with liquid nitrogen. This instrument was coupled to a Benchtof II TI TOF mass
123

spectrometer (Sepsolve, Peterborough, UK) allowing the simultaneous acquisition of fragmentation spectra at hard and soft ionisation (70 eV and 14 eV).

124

125

Chromspace software from Sepsolve was used to control the TOF-MS coupled to the GC × GC system.

126

127

## 2.2.2 Off-line comprehensive LC × SFC-Q-TOF-MS/MS

128

An Agilent 1260 Infinity II HPLC instrument with all parts from Agilent Technologies (Santa Clara, CA, USA) was used for the first dimension RPLC separation. This system consisted of an autosampler (G7129A) with a loop of 900 µL, a binary solvent delivery pump (G7111A), a thermostated column compartment (G7116A), and a diode array detector with a 1 µL flow-cell (DAD, G7117C). The dwell volume of the system was experimentally determined to be 2.66 mL. For the fraction collection, a collector (G1364F) with a capacity to collect 120 fractions was used together with chromatographic vials containing 250 µL inserts. The collected fractions were evaporated to dryness using the miVac centrifugal vacuum concentrator from Genevac (Ipswich, UK). The Agilent OpenLab CDS Chemstation software was used to control the system. A Kinetex F5 core-shell column (5 µm, 4.6 mm × 150 mm) with pentafluorophenylpropyl stationary phase from Phenomenex (Torrance, CA, USA) was used for the separation.

129

130

131

132

133

134

135

136

137

138

139

For the second dimension SFC separation, a Waters UPC² instrument with all parts from Waters (Milford, MA, USA) was used. The instrument consisted of an autosampler with a loop of 10 µL, a binary solvent delivery pump, a make-up solvent pump, a thermostated column compartment, a DAD with an 8.4 µL flow-cell, and a backpressure regulator (BPR). The Waters Empower CDS software was used to control the instrument. For the separation, a Torus 1-aminoanthracene (1-AA) column (1.7 µm, 3 mm × 50 mm) from Waters was used.

140

141

142

143

144

145

For the HMRS detection, the SFC was connected to an Agilent Jet stream electrospray ionisation (ESI) source installed on an Agilent 6560 Ion Mobility Quadrupole Time-of-Flight (Q-TOF) instrument, which was used in QTOF only acquisition mode. For the extraction of base peak chromatograms (BPC), the Agilent MassHunter Qualitative Analysis 10.0 software was used. To visualise the BPCs of all the fractions as an LC × SFC contour plot, an in-house Matlab (Mathworks) code developed by S. Heinisch and F. Rouvière was used.

146

147

148

149

150

151

152

## 2.3 Methods

153

### 2.3.1 GC × GC-TOF conditions

154

For the GC × GC separation, helium with 99.9999% purity from Messer (France) was used as the carrier gas with a flow rate of 1.8 mL min⁻¹. The first column was a VF-1701ms (30 m × 0.25 mm × 0.25 µm) from Restek (Centre County, PA, USA) and the second column a DB-1ms (1.5 m × 0.1 mm × 0.1 µm) from Agilent. The sample injection volume was 0.2 µl and the injection temperature was 280 °C with a split ratio of 1:10. The temperature program of the first oven started at 50 °C and followed a ramp of 1.75 °C min⁻¹ to reach 300 °C with a hold time 0 min. The second oven temperature program began at 50 °C and increased 1.8 °C min⁻¹ to 307°C with a hold time of 0 min. The modulation period was 11.63 seconds with the total run time of 157 minutes. The TOF-MS detection was performed in scan mode with tandem ionisation at 70 eV and 14 eV and an *m/z* range of 30 to 600 (40 Hz). The transfer line and source temperatures were set to 280 °C.

155

156

157

158

159

160

161

162

163
164 2.3.2 Off-line LC × SFC-Q-TOF-MS/MS conditions

165 The off-line LC × SFC-Q-TOF-MS/MS method developed previously by our research group for the
166 analysis of depolymerised lignin⁸ was also applied here with minor modifications. For the LC
167 separation, the injection volume was 10 µL, flow rate 2.1 mL min⁻¹, and column temperature 30 °C.
168 Mobile phase ¹A was H₂O with 0.1 vol% of FA and mobile phase ¹B was ACN with 0.1 vol% of FA.
169 The elution started with a gradient of 5 % – 100 % of ¹B in 9.0 min, decreased back to 5 % of ¹B
170 during 0.77 min, and equilibrated for 3.58 min. The total run time was 13.35 min. Starting after the
171 column dead time (0.77 min), 120 fractions were automatically collected every 0.085 min. The
172 fractions were evaporated to dryness, redissolved in the equivalent volume of MtBE, and vortexed a
173 few seconds. These fractions were injected one-by-one to the SFC with supercritical CO₂ (²A) and
174 methanol (²B) as the mobile phases. The injection volume was 7 µL, flow rate 1.4 mL min⁻¹, and
175 column temperature 40 °C. The BPR pressure was 140 bar and temperature 70 °C. The gradient started
176 with 5 % – 50 % of ²B in 3.57 min, decreased back to 5 % of ²B during 0.18 min, and equilibrated for
177 1.03 min. The total run time was 4.78 min.

178 The SFC was connected to the ESI-Q-TOF using the preBPR splitter with a sheath pump
179 configuration and MeOH (0.5 mL min⁻¹) as the make-up solvent. HRMS detection was performed in
180 the negative ESI mode with nitrogen as the nebuliser gas. The nebuliser pressure was 40 psi, drying
181 and sheath gas temperatures both 350 °C with a flow of 11 L min⁻¹. The capillary and fragmentor
182 voltages were 3.5 kV and 185 V, respectively. All ions fragmentation (AIF) with three experiments
183 using collision energies 0 eV, 20 eV, and 40 eV was applied. The scan range was 50-1500 m/z and
184 acquisition rate 5 spectra per second.

185
186 2.3.3 MS data analysis

187 GCImage software was used to process the GC × GC data. After performing baseline correction, the
188 peak (blob) detection was performed with a minimum threshold value to ensure the detection of both
189 high and low intensity peaks. The tentative identification of the compounds was done by matching the
190 experimental fragmentation spectra acquired at 70 eV with the library spectra present in the NIST
191 2020 MS Database. This procedure was performed and checked manually to ensure results with high
192 confidence. The likelihood of these matches was validated by checking that the retention times of the
193 detected compounds are consistent with the polarity of the used GC × GC column system. Using this
194 approach, a potential structural candidate was assigned for each peak separated by the GC × GC
195 technique.

196 For analysing the LC × SFC-HRMS data, MS-DIAL²² (version 4.9) developed by the RIKEN Centre
197 for Sustainable Resource Science (Kanagawa, Japan) was used for peak detection, deconvolution of
198 the data independent acquisition (DIA) data and blank subtraction. The results were imported to MS-
199 FINDER²⁰ for formula determination and to the SIRIUS software¹⁸ (version 5.6.3.) for the structural
200 analysis of the detected compounds. The data analysis with the MS softwares was performed similarly
201 to our previously published work; therefore, the details and parameters used in the current work are
202 presented in sections S1 and S2 of the Supplementary Information (SI).

203
## 204 3. Results and discussion

### 205 3.1 GC × GC

206 In Fig. 1A, the contour plot obtained from the GC × GC analysis of depolymerised lignin is presented.
207 One widely known advantage of using GC × GC is the classification of chemical families on the 2D
208 plot, which can aid with compound identification, rapid sample characterisation, or sample
209 comparison.⁵,²³ The classification of chemical families was also seen here, such as lignin monomers
210 with *p*-coumaryl (H) unit, lignin monomers with guaiacyl (G) unit, aliphatic compounds containing
211 ether/ketone/alcohol/furan function, lignin monomers with G unit and oxygenated side chain, lignin
212 dimers, and so on. The full list of the 335 tentatively identified compounds with a CₓHᵧO焉 formula is
213 shown in the S3 of the SI. In S3, also the assigned ID numbers are shown, which were used in the
214 main text to refer to the compounds. However, it must be kept in mind that the proposed structures are
215 the result of tentative identification, as a spectral database, not the corresponding standards, was used.
216 Nevertheless, the results can be used to characterise the GC × GC-MS analysis of lignin, which was
217 the aim of this study.

218 Based on the tentative structure, each compound was classified as a benzene ring containing
219 (benzenoid) or not containing (non-benzenoid) compound and replotted on the GC × GC plot in Fig.
220 1B. As the lignin structure is formed from the radical polymerisation of phenolic subunits,⁶ the
221 depolymerisation of lignin should also yield predominantly benzenoid compounds. Because the main
222 aim for the analysis of depolymerised lignin is lignin valorisation, the classification could aid with
223 separating the potential lignin compounds from other non-benzenoid compounds for further in depth
224 study.
<figure><img src="image_2.png" /><figcaption>Fig. 1. A: GC × GC-MS contour plot of the depolymerised lignin sample highlighting the main compound classes based on functional groups. B: The tentatively identified compounds replotted onto the GC × GC plot. The compounds classified as benzene ring containing compounds are shown with blue rectangles (200 compounds) while the non-benzenoid compounds are highlighted with grey circles (135 compounds).</figcaption></figure>

### 3.1.1 Characterisation of the non-benzenoid compounds

As can be seen on Fig. 1B, many of the non-benzenoid compounds eluted at the beginning (< 50 min) of the first dimension GC run, e.g., IDs 1, 2, 13, 19, 50, and 59. These compounds matched with various alkanes or small aliphatic compounds mainly with ester, ketone, ether, and/or alcohol functional groups. This is in correspondence with their earlier elution as these type of compounds have generally lower boiling points compared to phenolic compounds. These compounds likely originate from the solvent (isobutanol) used in the depolymerisation process, which can easily undergo condensation and esterification reactions. Another denser cluster of non-benzenoid compounds are in the range of 80-100 min in ¹GC and 6-8 sec in ²GC. Out of the eleven compounds in that area, nine matched with androstane or androstane derivatives (IDs 188, 192, 195, 197, 199, 205, 208, 214, 228) and one with an almost fully saturated phenanthrene derivative, ID 203. After 100 min in ¹GC and 5 sec in ²GC, several compounds matched with longer chain fatty acid esters (IDs 213, 254, 255, 265, 285, 298, 330) such as methyl stearate and isobutyl margarate. These steroidal and fatty acid derivatives likely originate from the lignin sample used in this work, as the lignin was obtained from wheat straw, which is known to contain various lipids.²⁴
Another type of non-benzenoid compounds that can be found in lignin samples but likely originate from residual hemicellulose are furan derivatives.²⁵ With GC × GC, 12 compounds (i.e., including IDs 21, 36, 44, 72, 77, 100, 115, 184, 210) matched with non-benzenoid furan derivatives. Interestingly, with the LC × SFC (section 3.2), only three compounds had structural candidates matching non-benzenoid furan derivatives. One reason could be that the EI applied after the GC × GC analysis is more suitable for the detection of these furan derivatives than ESI(-). However, without using furan standards, it is difficult to conclude if the reason is in the difference of the ionisation. Even though these non-benzenoid furan compounds unlikely originate from the main lignin structure, they can be still of interest for valorisation.

### 3.1.2 Characterisation of the benzenoid compounds

The benzenoid class contains the compounds of the primary interest – lignin monomers and oligomers. One way to study these compounds is to look at their Double Bond Equivalent (DBE) values, as monomers have one benzene ring in their structure (DBE 4-7), dimers two (DBE 8-11), trimers three (DBE 12-15), and so on.⁸ In the GC × GC analysis, most of the matches were obtained for potential monomers, while for lignin oligomers, 57 compounds matched the DBE range of dimers and only 4 with the DBE range of trimers. However, one must be cautious, as these results do not indicate that the sample contains mainly lignin monomers, as lignin oligomers might not be detected by using GC or GC × GC because of their lower volatility. Regarding the compounds with DBE 8-11, about half of the compounds (such as IDs 170, 276, 300, 305, 321, 328, 334) matched with potential lignin dimers. Some of the other compounds having DBE in the range of 8-11 matched with flavonoids (e.g., IDs 291, 283), which can originate from the sample. However, it must be kept in mind that the NIST database used for the tentative identification of the compounds has its limitations. NIST database contains compounds that have been isolated and analysed with GC-EI-MS; however, lignin oligomers are an understudied area and the database is lacking in spectral references needed for the identification of all kind of oligomers produced in the depolymerisation process. Therefore, lignin oligomers might be tentatively identified as other structurally similar compounds or remain unidentified altogether.

The potential structures of the lignin monomers seemed mostly to match with various *p*-coumaryl alcohol and coniferyl alcohol derivatives (e.g., IDs 68, 69, 89, 103, 135, 162), but not with sinapyl alcohol derivatives. In the opposite, several sinapyl alcohol derivatives were tentatively identified when using LC × SFC (e.g., IDs 6, 20, 23, 19, 33, 209, see section 3.2 for the LC × SFC analysis). However, with GC × GC several monomers without any OH group in their structure were identified, such as IDs 80, 90, 92, 99, 107, 126, 141. In LC × SFC analysis, only one compound, ID 163, had a structural match over 80% with a monomer structure without any OH group. This could suggest that GC × GC could be more suitable than LC × SFC for the analysis of some lignin compounds, such as compounds without any OH groups.

Besides typical lignin compounds, the benzenoids include compounds that are unlikely to originate from the main lignin polymer structure. For example, dehydrogenated abietane structures (IDs 240, 256, 292, and 314) were detected, which are chemical markers for coniferous resins derived from trees of the Pinaceae family. This was further supported by the detection of calamenene (degradation product, IDs 122 and 138) and an isomer of retene (ID 282), commonly known as a marker for pine pitch. These sesquiterpenes could have been produced during the catalytic depolymerisation process from diterpenoids²⁶,²⁷ or be already present in the wheat straw.

### 3.2 LC × SFC
In Fig. 2A, the LC × SFC-HRMS contour plot (MS¹) for the depolymerised lignin sample is presented.
For peak detection and mass spectral deconvolution, the data was analysed with MS-DIAL and then
transferred to the MS-FINDER software for chemical formula determination. In total, the process
yielded 622 compounds with CₓHᵧOz formulas for which the details (ID numbers, measured exact
mass, retention times, etc.) are presented in S4 of the SI.

As the first result of the LC × SFC-HRMS analysis was a list of chemical formulas, the compounds
were replotted on the 2D graph shown in Fig. 2B using the same LC and SFC retention times and their
DBE value. We can see that there is a significant number of compounds with DBE value less than 4
(DBE 1-3 in Fig. 2B) that largely elute at the end of the RPLC separation but at the start of the SFC
separation. Based on the interactions taking place in the two dimensions and the DBE value, we can
say that these compounds have lower aromaticity and, in general, are less polar than the benzenoid
lignin compounds. Therefore, these could be the aliphatic compounds detected also with the GC × GC
analysis. However, for more structural information, the study of the fragmentation data is needed.
Therefore, to gain more insight into the type and structures of the compounds detected in the
LC × SFC-HRMS/MS analysis, SIRIUS annotation software was applied next.

<figure><img src="image_3.png" /><figcaption>Fig. 2. A: LC × SFC-HRMS (MS¹) contour plot of the depolymerised lignin sample. B: The detected compounds replotted onto the LC × SFC plot using their corresponding DBE values and retention times. The compounds with DBE 1-3 are shown with yellow diamonds while compounds with DBE ≥ 4 are highlighted with pink circles.</figcaption></figure>

Out of the 622 compounds, 601 had unique fragmentation spectra and were exported into the SIRIUS
software. From these, 35 compounds had only a few MS² fragments and were therefore excluded as
the structure prediction based on only a few fragments is not reliable. Thus, for 566 compounds
structural candidates were found using the CSI:FingerID²⁸ Structure Database Search and two of the
highest matches for each compound are presented in S5 in the SI. In addition, the CANOPUS²⁹⁻³¹
function was used as it can predict Classyfire or Natural Product compound classes. For each
compound, two classes with probability of at least 70 % are presented in S5. However, as was with
GC × GC-MS, it must be kept in mind that the predicted structures and classes are potential/tentative
candidates and solely used to characterise the compound classes detected in the depolymerised lignin
sample with the LC × SFC-HRMS/MS method.

Combining the information of structural candidates and potential classes, the 566 compounds were
classified as benzenoids (406), non-benzenoids (146), or possibly benzenoids (14). Based on this
classification, the compounds were replotted on the LC × SFC graph shown in Fig. 3A. In Fig. 3B,
only the benzenoids are shown and reclassified based on their DBE value. For this sample, the highest
DBE values corresponding to potential lignin compounds were in the range of 12-15, suggesting that
the largest lignin oligomers in this sample were trimers.
<figure><img src="image_4.png" /><figcaption>Fig. 3. A: LC × SFC plot of the 566 CₓHᵧOz compounds classified based on SIRIUS results as benzenoids (blue squares), non-benzenoids (grey circles), and possibly benzenoids (orange triangles). B: LC × SFC plot showing only the benzenoid compounds classified based on their DBE values – DBE 4-7 (blue circles), DBE 8-11 (purple triangles), and DBE 12-15 (green squares).</figcaption></figure>

### 3.2.1 Characterisation of the benzenoid compounds

Most of the compounds classified as benzenoid compounds had high predicted class matches characteristic to the main lignin structure, such as lignans (e.g., IDs 57, 93, 109, 116, 130, 133, 143, 159, 178, 241, 244, 258, 277, 307, 350, 364, 438, 451, 468), cinnamic acids and derivatives (e.g., IDs 3, 6, 11, 12, 25, 42, 321, 330, 442, 452), methoxyphenols (e.g., IDs 1, 5, 13, 20, 23, 32, 33, 34, 79, 123, 129, 138, 200, 243, 245, 306, 461). In addition, aromatic compounds that are not commonly associated with the main lignin structure, such as stilbenes/stilbenoids (e.g., IDs 108, 177, 198, 230, 257, 300, 453), flavonoids (e.g., IDs 76, 87, 158, 219, 487), and chromones (IDs 249, 466, 467) resulted from the class prediction. These phenolic compounds may be residuals from the lignin separation process, as the material was extracted from wheat straw, or originate from the main lignin polymer, as studies have found that in some plants, these compounds can also behave as lignin monomers during the radical coupling of the lignification.³² Because of their structural similarities to the main lignin compounds, they are fall to the same classes with the lignin compounds on the 2D plot in Fig. 3B.

The benzenoid compounds also included compounds where the benzene ring was fused together with medium or long chain carboxylic acid moieties, such as IDs 98, 149, 196, 227, 233, 276, 281, 323, 387, 414, 494. These compounds may originate from cutin or suberin, polymeric materials present in the cuticle on the surface of various plants to protect them from water damage.³³ Structurally similar to these compounds are also zearalenones for which 4 compounds had a predicted class match (ID 144, 211, 415, 428). Zearalenones have been found in cereal crops, which is consistent to the fact that the analysed lignin sample originates from wheat straw.³⁴

### 3.2.2 Characterisation of the non-benzenoid compounds

In terms of non-benzenoid compounds, we can see in Fig. 3A that after 7.7 min in the RPLC dimension, there are only non-benzenoid compounds eluting. Although some of these compounds have DBE ≥ 4 as was seen in Fig. 2B, these are presumably not the main lignin compounds, as their structure does not contain benzene ring. In large, these compounds matched with various low-polarity lipids and lipid-like molecules. For example, several fatty acids with varying chain lengths were detected: C18:2 (ID 529), C18:1 (ID 542), C19:1 (ID 549), C19:0 (ID 555), C20:1 (ID 556), C20:0
(ID 567), C22:0(-OH) (ID 568), C21:0 (ID 575), and C23:0 (ID 580). These fatty acids eluted all before 0.5 min in the SFC dimension because without a benzene ring (DBE < 4), high number of double bonds or OH groups they have weak interactions with the 1-AA stationary phase. From these compounds, the highest retention time of 0.483 min was seen for C22:0(-OH), which is explained by the presence of a free OH group causing stronger interaction with the 1-AA stationary phase. These compounds were left undetected with $GC \times GC$ likely because of their poor volatility; however, as free acids, they are easily detectable with the ESI(-) ionisation. Another type of lipids that were detected in the sample were compounds originating from the terpenoids pathway. These included various steroids and diterpenoids (e.g., IDs 525, 528, 536, 557, 560, 571, 591) with DBE ≥ 4 and $t_R$ more than 0.5 min in the SFC dimension due to the $π-π$ interactions and interactions between the stationary phase and the OH groups. Compounds belonging to these classes were also detected with $GC \times GC$ and as was discussed under section 3.1, these lipids and lipid-like molecules likely originate from wheat straw,²⁴ the source of the lignin sample analysed in this work.

375

## 3.3 Complementarity of $GC \times GC$ and $LC \times SFC$ analyses

Fig. 4A demonstrates the overlay of the $GC \times GC$ (orange diamonds) and $LC \times SFC$ (grey diamonds) results using the molecular mass and DBE values of the detected compounds. The red and grey triangles represent the overall average DBE and average molecular mass for the $GC \times GC$ and $LC \times SFC$ results, respectively. As it was expected, with the $LC \times SFC$ method the average mass of the detected compounds was higher (around 300 m/z) than with $GC \times GC$ (around 200 m/z). This is because for a compound to be analysable with GC, and therefore also with $GC \times GC$, it has to be sufficiently volatile. However, most lignin oligomers are low-volatile compounds and therefore commonly only lignin monomers and a few dimers have been detected in $GC \times GC$ analyses.¹²,³⁵ More specific approaches like high temperature $GC \times GC$ and/or derivatisation would be needed to detect more lignin oligomers.¹²,³⁶ In terms of $LC \times SFC$ analysis of lignin oligomers, volatility is not an issue. However, the aspect that several lower molecular mass compounds were detected with $GC \times GC$ but not with $LC \times SFC$ also amplified the higher average DBE and molecular mass value for the $LC \times SFC$ analysis.

<figure><img src="image_5.png" /><figcaption>Fig. 4. A: DBE vs molecular mass plot for the $GC \times GC$ (orange diamonds) and $LC \times SFC$ (grey diamonds) results. The red and grey triangles represent the average DBE and average molecular mass for the $GC \times GC$ and $LC \times SFC$ data, respectively. B: The Van Krevelen diagram for the $GC \times GC$ (orange diamonds) and $LC \times SFC$ (grey diamonds) results.</figcaption></figure>

From Fig. 4A it is also visible, that only with $GC \times GC$ it was possible to detect compounds with DBE of 0. Based on the results of the NIST database, these saturated compounds corresponded to aliphatic molecules, such as ethers, alcohols, and hydrocarbons (e.g., IDs 3, 35, 54, 167) that possibly originate from the solvent – isobutanol – of the depolymerisation process. On the Van Krevelen diagram in Fig.

390

391

392

393

394

395

396

397

398
4B it is visible that only with GC × GC (orange diamonds) it was possible to detect compounds without any oxygen atom in the molecule (e.g., IDs 14, 46, 122, 133, 188). The compounds were left undetected in the LC × SFC analysis (grey diamonds), because they are not ionisable with ESI, the ionisation source used after the LC × SFC separation. If the aim of the analysis would be to detect compounds without any heteroatoms, then other ionisation source like APPI could be more suitable.³⁷ However, for the analysis of a depolymerised lignin sample, it is of outmost importance to be able to detect phenolic compounds, for which ESI(-) ionisation is often preferred.⁸,³⁸,³⁹

### 3.3.1 Isomers

One important advantage in using 2D chromatography instead of 1D chromatography is the improved separation of analytes, including isomers.⁸,⁴⁰ In the graph of Fig. 5, the number of isomers detected in the GC × GC and LC × SFC analyses are presented. The full list of unique formulas with the detected number of isomers for each formula is presented in S6 of the SI. Based on the results of the LC × SFC analysis, almost 68% of compounds had at least one isomer detected in the same sample. For the GC × GC analysis, the corresponding value was also quite high - 61%. This demonstrates the high number of isomers in depolymerised lignin and the need for powerful chromatographic techniques to enable the detection of those isomers. In the table of Fig. 5, the formulas with the highest number of isomers for the GC × GC and LC × SFC results is shown. In case of LC × SFC, all except $C_{18}H_{34}O_4$ have a DBE value ≥ 4 and corresponded to compounds classified as lignin monomers or dimers on Fig. 3B.

<figure><img src="image_6.png" /><figcaption>Fig. 5. The graph represents the number of isomers detected for each unique formula. With GC × GC and LC × SFC, respectively 214 and 350 unique formulas were detected, from which 64 were in common. The table shows the formula, number of isomers, DBE, and molecular mass (M) for the formulas for which the highest number of isomers were detected with the 2D techniques.</figcaption></figure>

For the GC × GC results, all except $C_8H_{16}O$ and $C_8H_{16}O_2$ have again DBE ≥ 4 and correspond to lignin monomers or even dimers ($C_{17}H_{16}O_2$). However, overall the formulas with the highest number of isomers have lower molecular mass than the ones detected with LC × SFC. For example, for the formula $C_{11}H_{16}O_2$, six isomers were found with the GC × GC analysis but only one with the LC × SFC analysis. From the other side, for $C_{18}H_{20}O_5$, eight isomers were detected with LC × SFC but only one with the GC × GC analysis. Therefore, both 2D chromatographic techniques enabled the detection of numerous isomers, however, GC × GC for lower and LC × SFC for higher molecular weight compounds, which correlates to the average molecular mass obtained overall with the two techniques (section 3.3). In addition, only 64 formulas were found in common for the 2D techniques, which is close to 30% and 18% of all the detected unique formulas for the GC × GC and LC × SFC analysis, respectively. Again, this demonstrates the complementary of the two techniques.

<table><caption>Formulas with the highest number of isomers</caption><thead><tr><th colspan="4">GC × GC</th><th colspan="4">LC × SFC</th></tr><tr><th>Nr of isomers</th><th>Formula</th><th>DBE</th><th>M (g/mol)</th><th>Nr of isomers</th><th>Formula</th><th>DBE</th><th>M (g/mol)</th></tr></thead><tbody><tr><td>6</td><td>C<sub>10</sub>H<sub>12</sub>O<sub>2</sub></td><td>5</td><td>164.2</td><td>8</td><td>C<sub>18</sub>H<sub>20</sub>O<sub>5</sub></td><td>9</td><td>316.4</td></tr><tr><td>6</td><td>C<sub>11</sub>H<sub>16</sub>O<sub>2</sub></td><td>4</td><td>180.2</td><td>7</td><td>C<sub>16</sub>H<sub>22</sub>O<sub>5</sub></td><td>6</td><td>294.3</td></tr><tr><td>5</td><td>C<sub>8</sub>H<sub>16</sub>O</td><td>1</td><td>128.2</td><td>7</td><td>C<sub>19</sub>H<sub>22</sub>O<sub>5</sub></td><td>9</td><td>330.4</td></tr><tr><td>5</td><td>C<sub>9</sub>H<sub>12</sub>O<sub>2</sub></td><td>4</td><td>152.2</td><td>6</td><td>C<sub>13</sub>H<sub>16</sub>O<sub>3</sub></td><td>6</td><td>220.3</td></tr><tr><td>5</td><td>C<sub>10</sub>H<sub>14</sub>O<sub>2</sub></td><td>4</td><td>166.2</td><td>6</td><td>C<sub>12</sub>H<sub>16</sub>O<sub>4</sub></td><td>5</td><td>224.3</td></tr><tr><td>4</td><td>C<sub>8</sub>H<sub>16</sub>O<sub>2</sub></td><td>4</td><td>138.2</td><td>6</td><td>C<sub>12</sub>H<sub>16</sub>O<sub>4</sub></td><td>4</td><td>226.3</td></tr><tr><td>4</td><td>C<sub>8</sub>H<sub>16</sub>O<sub>2</sub></td><td>1</td><td>144.2</td><td>6</td><td>C<sub>13</sub>H<sub>16</sub>O<sub>4</sub></td><td>5</td><td>238.3</td></tr><tr><td>4</td><td>C<sub>17</sub>H<sub>16</sub>O<sub>2</sub></td><td>10</td><td>252.3</td><td>6</td><td>C<sub>18</sub>H<sub>34</sub>O<sub>4</sub></td><td>3</td><td>314.5</td></tr></tbody></table>
436 3.3.2 DBE vs molecular mass plots for benzenoid compounds

437 The data shown in Fig. 4A was replotted based on the benzenoid/non-benzenoid classification and
438 demonstrated here in Fig. 6. Interestingly, in Fig. 6A that shows the LC × SFC results, an area was
439 seen and highlighted with blue dashed lines that included almost all of the benzenoid compounds. The
440 only benzenoid compound that is fully out of the quadrangle is ID 387 (M = 410; DBE = 6), which
441 corresponded to a structure, where the benzene ring is connected to a long aliphatic chain leading to
442 higher molecular mass than commonly main lignin compounds would have at low DBE values. The
443 same quadrangle was drawn on the GC × GC plot in Fig. 6B. The structures of the outliers with ID
444 264 (M = 420; DBE = 6), 295 (M = 428; DBE = 4), and 320 (M = 430; DBE = 7) consisted also of
445 benzene and long aliphatic chain(s), thus, the compounds may originate from cutin or suberin as
446 discussed in section 3.2.1. Therefore, this kind of visual representation can be used to rapidly find
447 outliers or potential lignin compound for further structural study, regardless of the type of the 2D
448 chromatography method.

449 However, as can be seen from Fig. 6, the quadrangle can also include compounds that are not
450 benzenoids. These non-benzenoid compounds matched with various compounds with a high number
451 of double bonds or cycles in their structure, for example, in the case of LC × SFC, IDs 210, 432, 445,
452 484. In addition, as was discussed under sections 3.1.2 and 3.2.1, the benzenoids can also contain non-
453 lignin compounds that would be located in the same quadrangle. Interestingly, almost all of the
454 compounds classified as possibly benzenoids based on the SIRIUS analysis of the LC × SFC data are
455 in the quadrangle.

<figure><img src="image_7.png" /><figcaption>Fig. 6. DBE vs molecular mass plots for the detected compounds, A: LC × SFC and B: GC × GC. The blue squares correspond to compounds classified as benzenoids, the orange triangles to possibly benzenoids, and the grey circles to non-benzenoids. The quadrangle formed by the blue dashed lines represents the area where most of the benzenoid compounds were located on the LC × SFC plot.</figcaption></figure>

461 With LC × SFC, much more lignin oligomers were detected compared to GC × GC as can be seen by
462 the presence of more compounds in the top half of the quadrangle. Besides the volatility limit, another
463 reason why some of the oligomers might have been left unidentified with GC × GC but not with
464 LC × SFC can be the different structural characterisation procedures. From the LC × SFC-ESI-HRMS
465 analysis, the formulas of the detected compounds were directly obtained due to the measurement of
466 the exact mass of the molecular ion in MS¹. To obtain the formula with GC × GC-EI-MS, the
467 compound had to be tentatively identified based on the fragmentation information and the NIST
468 database. However, as it was mentioned in section 3.1.2, lignin oligomers are lacking from NIST,
469 which could mean that structures similar to the detected compound are absent from the database.
470 Therefore, some of the formulas corresponding to lignin oligomers detected during the GC × GC
471 analysis are likely missing from the formula analysis; nevertheless, the overall conclusions about the
472 two 2D techniques could still be made.
Besides the different ionisation techniques, the different identification procedures can also lead to the
tentative identification of different compounds. For example, with GC × GC several compounds
matched with anthracene derivatives (e.g., IDs 249, 251, 268); however, none of the structures
proposed by the SIRIUS software for all of the detected compounds in LC × SFC had an anthracene
moiety. SIRIUS uses structural databases like PubChem to generate fingerprints and predict the
structural match. However, NIST database uses measured spectra to compare the spectral data and
obtain a match. Currently, PubChem has more than 111 million unique structures in its database,
which is a remarkably higher number than around 300 thousand compounds in the NIST 2020
database. Therefore, it is possible, that with SIRIUS there is a higher probably to find a structure that
is structurally similar to the detected compound. In the advantage of GC × GC, as the identification
with EI-MS and NIST database use real measured spectra both with 70 eV, the identification of small
compounds can be done with higher confidence. However, to confirm these hypotheses, additional
experiments with GC × GC and LC × LC focusing on the mass spectral analysis of standards would be
needed.

487

488 4. Conclusions

In this study, GC × GC-EI-MS and LC × SFC-ESI(-)-HRMS/MS were applied for the analysis of the
same depolymerised lignin sample. The techniques proved to be complementary to each other, as
depolymerised lignin is a complex sample consisting of compounds with different functionalities and
molecular weights for which different analytical techniques may be necessary. LC × SFC provided
more information on low volatile compounds such as polyphenols (including lignin oligomers) and
lipid-like molecules, especially long chain fatty acids. However, with GC × GC much more
information could be obtained about small aliphatic compounds, non-benzenoid furans, saturated
compounds, and compounds without any heteroatom in their structure. In addition, with both methods
high number of isomers were found – 61% with GC × GC and 68% with LC × SFC – supporting the
need to use 2D chromatography for the analysis of lignin. Also, with both methods, the obtained 2D
plots can be useful for data interpretation. With GC × GC, groups of chemical families are formed,
while with LC × SFC, most of the non-benzenoid compounds are separated from the benzenoids,
which in turn are sectioned based on their DBE value.

The different approaches in MS data analysis can also provide information useful for different
research questions. With SIRIUS, the compound class and structure could be predicted from the 2D
LC-MS/MS data by using structural databases. Combing the results, the detected compounds could be
easily characterised and classified, for example, as potential lignin monomer, lignin dimer, sterol,
terpenoid, or fatty acid. For GC × GC-MS data, the NIST spectral database was used, which is a more
conventional approach where the real measured spectra is used to find matches. In case of common
small molecules, this makes the structural matches more reliable than SIRIUS prediction. However,
for larger compounds such as lignin oligomers there might not be any close match as these compounds
are missing in the databases. Therefore, the further development of the MS databases or prediction of
structural matches would be necessary for even wider usage of GC × GC and 2D LC for the analysis
of depolymerised lignin.

513

514 Acknowledgments

This work was funded by the French National Research Agency (project BIOPOLIOL, ANR-21-CE43-0026).
517 References

518 (1) López-Ruiz, R.; Romero-González, R.; Garrido Frenich, A. Ultrahigh-Pressure Liquid Chromatography-Mass Spectrometry: An Overview of the Last Decade. *TrAC Trends Anal. Chem. 2019*, 118, 170–181. https://doi.org/10.1016/j.trac.2019.05.044.

519 (2) Špáník, I.; Machyňáková, A. Recent Applications of Gas Chromatography with High-Resolution Mass Spectrometry. *J. Sep. Sci. 2018*, 41 (1), 163–179. https://doi.org/10.1002/jssc.201701016.

520 (3) van den Hurk, R. S.; Pursch, M.; Stoll, D. R.; Pirok, B. W. J. Recent Trends in Two-Dimensional Liquid Chromatography. *TrAC Trends Anal. Chem. 2023*, 166, 117166. https://doi.org/10.1016/j.trac.2023.117166.

521 (4) Zanella, D.; Focant, J.-F.; Franchina, F. A. 30th Anniversary of Comprehensive Two-Dimensional Gas Chromatography: Latest Advances. *Anal. Sci. Adv. 2021*, 2 (3–4), 213–224. https://doi.org/10.1002/ansa.202000142.

522 (5) Tammekivi, E.; Geantet, C.; Lorentz, C.; Faure, K. Two-Dimensional Chromatography for the Analysis of Valorisable Biowaste: A Review. *Anal. Chim. Acta 2023*, 341855. https://doi.org/10.1016/j.aca.2023.341855.

523 (6) Bertella, S.; Luterbacher, J. S. Lignin Functionalization for the Production of Novel Materials. *Trends Chem. 2020*, 2 (5), 440–453. https://doi.org/10.1016/j.trechm.2020.03.001.

524 (7) Margarida Martins, M.; Carvalheiro, F.; Gírio, F. An Overview of Lignin Pathways of Valorization: From Isolation to Refining and Conversion into Value-Added Products. *Biomass Convers. Biorefinery 2024*, 14 (3), 3183–3207. https://doi.org/10.1007/s13399-022-02701-z.

525 (8) Tammekivi, E.; Batteau, M.; Laurenti, D.; Lilti, H.; Faure, K. A Powerful Two-Dimensional Chromatography Method for the Non-Target Analysis of Depolymerised Lignin. *Anal. Chim. Acta 2024*, 1288, 342157. https://doi.org/10.1016/j.aca.2023.342157.

526 (9) Olcese, R.; Carré, V.; Aubriet, F.; Dufour, A. Selectivity of Bio-Oils Catalytic Hydrotreatment Assessed by Petroleomic and GC*GC/MS-FID Analysis. *Energy Fuels 2013*, 27 (4), 2135–2145. https://doi.org/10.1021/ef302145g.

527 (10) Djokic, M. R.; Dijkmans, T.; Yildiz, G.; Prins, W.; Van Geem, K. M. Quantitative Analysis of Crude and Stabilized Bio-Oils by Comprehensive Two-Dimensional Gas-Chromatography. *J. Chromatogr. A 2012*, 1257, 131–140. https://doi.org/10.1016/j.chroma.2012.07.035.

528 (11) Sun, M.; Sandahl, M.; Turner, C. Comprehensive On-Line Two-Dimensional Liquid Chromatography × supercritical Fluid Chromatography with Trapping Column-Assisted Modulation for Depolymerised Lignin Analysis. *J. Chromatogr. A 2018*, 1541, 21–30. https://doi.org/10.1016/j.chroma.2018.02.008.

529 (12) Dao Thi, H.; Van Aelst, K.; Van Den Bosch, S.; Katahira, R.; Beckham, G. T.; Sels, B. F.; Van Geem, K. M. Identification and Quantification of Lignin Monomers and Oligomers from Reductive Catalytic Fractionation of Pine Wood with GC × GC – FID/MS. *Green Chem. 2022*, 24 (1), 191–206. https://doi.org/10.1039/D1GC03822B.

530 (13) Grams, J. Chromatographic Analysis of Bio-Oil Formed in Fast Pyrolysis of Lignocellulosic Biomass. *Rev. Anal. Chem. 2020*, 39 (1), 65–77. https://doi.org/10.1515/revac-2020-0108.

531 (14) Xu, L.; Shang, Z.; Lu, Y.; Li, P.; Sun, L.; Guo, Q.; Bo, T.; Le, Z.; Bai, Z.; Zhang, X.; Qiao, X.; Ye, M. Analysis of Curcuminoids and Volatile Components in 160 Batches of Turmeric Samples in China by High-Performance Liquid Chromatography and Gas Chromatography Mass Spectrometry. *J. Pharm. Biomed. Anal. 2020*, 188, 113465. https://doi.org/10.1016/j.jpba.2020.113465.

532 (15) Golge, O.; Cinpolat, S.; Kabak, B. Quantification of Pesticide Residues in Gherkins by Liquid and Gas Chromatography Coupled to Tandem Mass Spectrometry. *J. Food Compos. Anal. 2021*, 96, 103755. https://doi.org/10.1016/j.jfca.2020.103755.

533 (16) Delgado-Povedano, M. M.; Sánchez-Carnerero Callado, C.; Priego-Capote, F.; Ferreiro-Vera, C. Untargeted Characterization of Extracts from Cannabis Sativa L. Cultivars by Gas and Liquid Chromatography Coupled to Mass Spectrometry in High Resolution Mode. *Talanta 2020*, 208, 120384. https://doi.org/10.1016/j.talanta.2019.120384.

534 (17) Shamsudin, S.; Selamat, J.; Sanny, M.; Jambari, N. N.; Sukor, R.; Salleh, N. A.; Aziz, M. F. A.; Khatib, A. Integrated Gas Chromatography–Mass Spectrometry and Liquid Chromatography–Quadruple Time of Flight-Mass Spectrometry-Based Untargeted Metabolomics Reveal Possible
571 Metabolites Related to Antioxidant Activity in Stingless Bee Honey. *Food Anal. Methods* **2022**, *15* (11), 3209–3224. https://doi.org/10.1007/s12161-022-02271-w.

572
573 (18)Dührkop, K.; Fleischauer, M.; Ludwig, M.; Aksenov, A. A.; Melnik, A. V.; Meusel, M.; Dorrestein, P. C.; Rousu, J.; Böcker, S. SIRIUS 4: A Rapid Tool for Turning Tandem Mass Spectra into Metabolite Structure Information. *Nat. Methods* **2019**, *16* (4), 299–302.
574
575
576 https://doi.org/10.1038/s41592-019-0344-8.

577 (19)Böcker, S.; Dührkop, K. Fragmentation Trees Reloaded. *J. Cheminformatics* **2016**, *8* (1), 5.
578 https://doi.org/10.1186/s13321-016-0116-8.

579 (20)Tsugawa, H.; Kind, T.; Nakabayashi, R.; Yukihira, D.; Tanaka, W.; Cajka, T.; Saito, K.; Fiehn, O.; Arita, M. Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software. *Anal. Chem.* **2016**, *88* (16), 7946–7958.
580
581
582 https://doi.org/10.1021/acs.analchem.6b00770.

583 (21)Lai, Z.; Tsugawa, H.; Wohlgemuth, G.; Mehta, S.; Mueller, M.; Zheng, Y.; Ogiwara, A.; Meissen, J.; Showalter, M.; Takeuchi, K.; Kind, T.; Beal, P.; Arita, M.; Fiehn, O. Identifying Metabolites by Integrating Metabolome Databases with Mass Spectrometry Cheminformatics. *Nat. Methods* **2018**, *15* (1), 53–56. https://doi.org/10.1038/nmeth.4512.

587 (22)Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M. MS-DIAL: Data-Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis. *Nat. Methods* **2015**, *12* (6), 523–526. https://doi.org/10.1038/nmeth.3393.

590 (23)Staš, M.; Auersvald, M.; Vozka, P. Two-Dimensional Gas Chromatography Characterization of Pyrolysis Bio-Oils: A Review. *Energy Fuels* **2021**, *35* (10), 8541–8557.
591
592 https://doi.org/10.1021/acs.energyfuels.1c00553.

593 (24)del Río, J. C.; Prinsen, P.; Gutiérrez, A. A Comprehensive Characterization of Lipids in Wheat Straw. *J. Agric. Food Chem.* **2013**, *61* (8), 1904–1913. https://doi.org/10.1021/jf304252m.

595 (25)Zhu, X.; Bruijnaers, B.; Lourençon, T. V.; Balakshin, M. Structural Analysis of Lignin-Based Furan Resin. *Materials* **2022**, *15* (1), 350. https://doi.org/10.3390/ma15010350.

597 (26)Marković, M.; Mezzatesta, E.; Porcier, S.; Vieillescazes, C.; Mathe, C. Chemical Characterization of Embalming Materials of Four Ibis Mummies from the Musée Des Confluences, Lyon. *J. Archaeol. Sci. Rep.* **2020**, *34*, 102624. https://doi.org/10.1016/j.jasrep.2020.102624.

600 (27)Colombini, M. P.; Modugno, F. *Organic Mass Spectrometry in Art and Archaeology*, 1st ed.; John Wiley & Sons, Ltd: Chichester, UK, 2009.

602 (28)Dührkop, K.; Shen, H.; Meusel, M.; Rousu, J.; Böcker, S. Searching Molecular Structure Databases with Tandem Mass Spectra Using CSI:FingerID. *Proc. Natl. Acad. Sci.* **2015**, *112* (41), 12580–12585. https://doi.org/10.1073/pnas.1509788112.

605 (29)Djoumbou Feunang, Y.; Eisner, R.; Knox, C.; Chepelev, L.; Hastings, J.; Owen, G.; Fahy, E.; Steinbeck, C.; Subramanian, S.; Bolton, E.; Greiner, R.; Wishart, D. S. ClassyFire: Automated Chemical Classification with a Comprehensive, Computable Taxonomy. *J. Cheminformatics* **2016**, *8* (1), 61. https://doi.org/10.1186/s13321-016-0174-y.

609 (30)Kim, H. W.; Wang, M.; Leber, C. A.; Nothias, L.-F.; Reher, R.; Kang, K. B.; van der Hooft, J. J. J.; Dorrestein, P. C.; Gerwick, W. H.; Cottrell, G. W. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. *J. Nat. Prod.* **2021**, *84* (11), 2795–2807. https://doi.org/10.1021/acs.jnatprod.1c00399.

613 (31)Dührkop, K.; Nothias, L.-F.; Fleischauer, M.; Reher, R.; Ludwig, M.; Hoffmann, M. A.; Petras, D.; Gerwick, W. H.; Rousu, J.; Dorrestein, P. C.; Böcker, S. Systematic Classification of Unknown Metabolites Using High-Resolution Fragmentation Mass Spectra. *Nat. Biotechnol.* **2021**, *39* (4), 462–471. https://doi.org/10.1038/s41587-020-0740-8.

617 (32)del Río, J. C.; Rencoret, J.; Gutiérrez, A.; Lan, W.; Kim, H.; Ralph, J. Lignin Monomers Derived from the Flavonoid and Hydroxystilbene Biosynthetic Pathways. In *Recent Advances in Polyphenol Research*; John Wiley & Sons, Ltd, 2021; pp 177–206.
618
619
620 https://doi.org/10.1002/9781119545958.ch7.

621 (33)Canizares, D.; Angers, P.; Ratti, C. Flax and Wheat Straw Waxes: Material Characterization, Process Development, and Industrial Applications. *Biomass Convers. Biorefinery* **2020**, *10* (2), 555–565. https://doi.org/10.1007/s13399-019-00441-1.

624 (34)Mahato, D. K.; Devi, S.; Pandhi, S.; Sharma, B.; Maurya, K. K.; Mishra, S.; Dhawan, K.; Selvakumar, R.; Kamle, M.; Mishra, A. K.; Kumar, P. Occurrence, Impact on Agriculture, Human
626 Health, and Management Strategies of Zearalenone in Food and Feed: A Review. *Toxins* **2021**, *13*
627 (2), 92. https://doi.org/10.3390/toxins13020092.

628 (35) Nunes, V. O.; Silva, R. V. S.; Romeiro, G. A.; Azevedo, D. A. The Speciation of the Organic
629 Compounds of Slow Pyrolysis Bio-Oils from Brazilian Tropical Seed Cake Fruits Using High-
630 Resolution Techniques: GC × GC-TOFMS and ESI(±)-Orbitrap HRMS. *Microchem. J.* **2020**, *153*,
631 104514. https://doi.org/10.1016/j.microc.2019.104514.

632 (36) Joffres, B.; Nguyen, M. T.; Laurenti, D.; Lorentz, C.; Souchon, V.; Charon, N.; Daudin, A.;
633 Quignard, A.; Geantet, C. Lignin Hydroconversion on MoS2-Based Supported Catalyst:
634 Comprehensive Analysis of Products and Reaction Scheme. *Appl. Catal. B Environ.* **2016**, *184*,
635 153–162. https://doi.org/10.1016/j.apcatb.2015.11.005.

636 (37) Laaniste, A.; Leito, I.; Kruve, A. ESI Outcompetes Other Ion Sources in LC/MS Trace Analysis.
637 *Anal. Bioanal. Chem.* **2019**, *411* (16), 3533–3542. https://doi.org/10.1007/s00216-019-01832-z.

638 (38) Prothmann, J.; Li, K.; Hulteberg, C.; Spégel, P.; Sandahl, M.; Turner, C. Nontargeted Analysis
639 Strategy for the Identification of Phenolic Compounds in Complex Technical Lignin Samples.
640 *ChemSusChem* **2020**, *13* (17), 4605–4612. https://doi.org/10.1002/cssc.202000951.

641 (39) Prothmann, J.; Spégel, P.; Sandahl, M.; Turner, C. Identification of Lignin Oligomers in Kraft
642 Lignin Using Ultra-High-Performance Liquid Chromatography/High-Resolution Multiple-Stage
643 Tandem Mass Spectrometry (UHPLC/HRMSn). *Anal. Bioanal. Chem.* **2018**, *410* (29), 7803–
644 7814. https://doi.org/10.1007/s00216-018-1400-4.

645 (40) Fan, H.; He, K.; Wang, J. Study of Sewage Sludge Pyrolysis Liquids Using Comprehensive Two-
646 Dimensional Gas Chromatography/Time-of-Flight Mass Spectrometry. *Fuel* **2016**, *185*, 281–288.
647 https://doi.org/10.1016/j.fuel.2016.07.118.

648