Biomass sorting

13 February 2017 - Analysis of infrared spectra to classify lignocellulosic biomass samples

13 February 2017 - Analysis of infrared spectra to classify lignocellulosic biomass samples

Mid‐infrared (MIR) and near‐infrared (NIR) spectroscopies are rapid and non-destructive methods to characterize lignocellulosic biomass and transformation in biorefineries and environmental domains.

Combining a fuzzy C‐means clustering method with bootstrapping, we were able to classify a small set of maize roots in soil according to genotype or period of their biodegradation process based on their NIR and MIR spectra. For raw samples, without soluble extraction, the MIR spectra produce better classification than NIR spectra, while improved results are obtained using MIR spectra acquired on cell wall residues. This shows the relevance of MIR spectroscopy in the multivariate analysis of a small number of samples of lignocellulosic biomass. These results come from a collaboration between FARE lab and CRESTIC lab from URCA.

Read: Rammal A, Perrin E, Vrabie V, Bertrand I, Chabbert B. Classification of lignocellulosic biomass by weighted‐covariance factor fuzzy C‐means clustering of mid‐infrared and near‐infrared spectra. Journal of Chemometrics 2017, e2865 DOI

Contact: Dr Brigitte Chabbert, brigitte.chabbert@inra.fr

Modification date : 06 June 2023 | Publication date : 13 February 2017 | Redactor : B. Chabbert / G. Paës