Application of Py-GC/MS coupled with PARAFAC2 and PLS-DA to study fast pyrolysis of genetically engineered poplars

Hilal E. Toraman, Victor Abrahamsson, Ruben Vanholme, Rebecca Van Acker, Frederik Ronsse, Gilles Pilate, Wout Boerjan, Kevin M. Van Geem, Guy B. Marin

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Field-grown genetically engineered and wild-type poplars were pyrolyzed in a micro-pyrolysis (Py-GC/MS) setup under fast pyrolysis conditions. Poplars (Populus tremula x P. alba) down-regulated for cinnamoyl-CoA reductase (CCR), which catalyzes the first step of the monolignol-specific branch of the phenylpropanoid biosynthetic pathway, were grown in field trials in France and harvested after a full rotation of 2 years. The effect of small compositional differences, specifically small shifts in lignin composition and their impact on the bio-oil composition, could not be identified using principal component analysis (PCA), necessitating the use of more advanced analysis techniques. The combination of parallel factor analysis 2 (PARAFAC2) and partial least squares-discriminant analysis (PLS-DA) for detailed characterization and classification of the pyrolysis data enabled the classification of the poplars with a success rate above 99% using the PARAFAC2 scores. This methodology proved to be extremely valuable to identify subtle information in complex datasets, such as the one used in this study. The obtained PLS-DA models were validated by cross-validation, jackknifing and permutation tests in order to ensure that the model was not overfitting the data. PLS-DA showed that down-regulation of CCR disfavored the relative amount of both guaiacyl and syringyl lignin-derived compounds. This study shows that lignin engineering can be a promising strategy to alter the lignin composition of the biomass for the production of high value-added phenolic compounds.

Original languageEnglish (US)
Pages (from-to)101-111
Number of pages11
JournalJournal of Analytical and Applied Pyrolysis
StatePublished - Jan 2018

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Fuel Technology


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