Adding value to by-products: Unraveling the complex structure of lignin with SIRIUS

Despite being one of Earth's most abundant polymeric organic compounds, lignin is often considered a lower-value byproduct in industrial processes. Converting lignin into valuable chemicals or biomaterials requires a thorough structural characterisation of depolymerised products. This non-targeted analysis method involving 2D liquid chromatography and high-resolution tandem mass spectrometry uses SIRIUS in versatile ways to unravel the complex structures of depolymerized lignin.
A forest bathed in sunlight. Lignin is essential for stability of wood.
Lignin is essential for stability of plant tissue and is one of the most abundant organic compounds on earth. (Photo by Johannes Plenio on Unsplash.)

Lignin: The key to plant stability

Lignin is a biopolymer synthesised in the cells of perennial plants. It is essential for the compressive strength and gravitational stability of plant tissues. It is also involved in several physiological processes in plants, including defence mechanisms against pathogens. Lignin is composed of different monomeric building blocks that form a robust three-dimensional polymer structure. These are incorporated into the plant cell wall and cause the cell to become woody. Lignin is one of the most abundant organic compounds on earth.

Lignin is a by-product in the early stages of the pulp or cellulose ethanol industry. It is often considered a low-value residue and is usually burned for energy. In pursuit of a circular economy, there’s growing interest in increasing the value of lignin by converting it into valuable chemicals or biomaterials. The isolated lignin can be depolymerised into smaller oligomers and monomers​1​. A thorough structural characterisation of the resulting product and/or intermediates is necessary for the valorisation of lignin​2,3​.

Challenges in the structural characterization of lignin

Lignin is composed of three aromatic subunits that are randomly linked. The structural analysis of lignin and its depolymerised products is very challenging​1​ for several reasons. The proportions of monomeric subunits and the type and amount of linkages vary depending on the origin of the lignin. Furthermore, it is extremely difficult to isolate lignin without changing its structure through condensation and repolymerisation. In addition, the separation and identification of isomeric mono- and oligomers can be particularly problematic.

Non-targeted analysis of depolymerized lignin 

Researchers led by Karine Faure at the Universite Claude Bernard Lyon 1 have developed a method for the non-target analysis of depolymerised lignin that combines two-dimensional liquid chromatography with high-resolution tandem mass spectrometry​4​.  The improved separation by 2D chromatography not only provides more information about the compounds, but also reduces the complexity caused by co-elution. This results in cleaner mass spectra and simplifies identification.

One, two, three, many

They analysed a sample derived from the partial depolymerization of lignin under a hydrogen atmosphere. The LC×SFC-HRMS/MS approach resulted in 471 pseudo-molecular ions with a Double Bond Equivalent (DBE) of ≥ 4. The sample contained no oligomers higher than tetramers. SIRIUS was used to check, whether or not potential structural candidates suggested by SIRIUS belong to the same class implied by the DBE value. For example, additional double bonds in the structure of a dimer may result in a DBE value classically corresponding to a trimer​5​. SIRIUS was also used to identify compounds that were unlikely to be lignin compounds as their structural candidates and compound classes in SIRIUS were far from potential lignin compounds.

Identifying the compounds

Due to the complexity of depolymerised samples, obtaining reference standards for identification is challenging. 33 phenolic standards were selected to represent monomeric compounds that can be produced by lignin depolymerisation. However, only two of these standards were actually identified in the lignin sample. To address this, SIRIUS was employed to identify additional compounds, resulting in 25 tentatively annotated monomers and dimers proposed by the researchers.

Triple purpose of SIRIUS

This study is a good example of showcasing the versatile applications of SIRIUS. The scientists used SIRIUS to

  • identify which compounds did not belong to the desired compound class,
  • validate class assignment by DBE value,
  • annotate compounds for which there are no reference standards for identification.


  1. 1.
    Bertella S, Luterbacher JS. Lignin Functionalization for the Production of Novel Materials. Trends in Chemistry. Published online May 2020:440-453. doi:10.1016/j.trechm.2020.03.001
  2. 2.
    Constant S, Wienk HLJ, Frissen AE, et al. New insights into the structure and composition of technical lignins: a comparative characterisation study. Green Chem. Published online 2016:2651-2665. doi:10.1039/c5gc03043a
  3. 3.
    Tammekivi E, Geantet C, Lorentz C, Faure K. Two-dimensional chromatography for the analysis of valorisable biowaste: A review. Analytica Chimica Acta. Published online December 2023:341855. doi:10.1016/j.aca.2023.341855
  4. 4.
    Tammekivi E, Batteau M, Laurenti D, Lilti H, Faure K. A powerful two-dimensional chromatography method for the non-target analysis of depolymerised lignin. Analytica Chimica Acta. Published online February 2024:342157. doi:10.1016/j.aca.2023.342157
  5. 5.
    Prothmann J, Spégel P, Sandahl M, Turner C. Identification of lignin oligomers in Kraft lignin using ultra-high-performance liquid chromatography/high-resolution multiple-stage tandem mass spectrometry (UHPLC/HRMSn). Anal Bioanal Chem. Published online October 10, 2018:7803-7814. doi:10.1007/s00216-018-1400-4

The easy way to comprehensive structure elucidation​

SIRIUS is proven to be the best computational method for identifying molecules from tandem mass spectrometry data. SIRIUS is the umbrella application comprising molecular formula identification (ZODIAC), structure database search (CSI:FingerID), confidence score assignment (COSMIC) and compound class prediction (CANOPUS).​