Plant cell wall biomass is an abundant and renewable organic resource. Of the polymers it encloses, cellulose and hemicellulose are regarded as a raw material for the production of fuels and other products (Klemm et al., 2005; Slavov et al., 2013). Nonetheless, current usage of lignocellulosic biomass is still below its full potential due to a series of limiting factors mainly related to the cell wall recalcitrance to saccharification, a severe constraint to maximum biomass usability in downstream processing (Pauly and Keegstra, 2008).
As a strategy to optimise bio-energy and bio-refining applications, an increasing amount of effort is being put into the advancement of our knowledge concerning the cell wall compositional roots of recalcitrance. Fourier transform mid-infrared spectroscopy (FTIR) represents a very useful tool on this enterprise, as it allows for a high-throughput, non-destructive and low unit cost procedure for the examination of cell wall biomass (Allison et al., 2009; Carpita and McCann, 2015). Furthermore, the use of Attenuated Total Reflection (ATR) in conjunction with infrared spectroscopy (IR) enables cell wall biomass samples to be examined in solid state without extensive preparation. Nonetheless, the analysis of purified cell wall preparations instead of the intact plant biomass is highly recommended, as it minimises or even eradicates interference from biomass components which are not part of the cell wall. Further information regarding the fundamentals of FTIR may be found elsewhere (Smith, 2011).
Datasets generated from FTIR spectroscopy can be extensive and complex. In these situations, data-driven modelling techniques are often used as exploratory approaches to identify the most distinctive features of the collected spectra. Here we suggest the use of Principal Component Analysis (PCA), a frequently employed method to transform a large set of variables into a smaller set of new variables (principal components), effectively reducing dataset dimensionality.
When the aim is a complete and detailed biomass characterisation, the FTIR-PCA method here described does not exclude the need for parallel wet gravimetric and analytical procedures. However, it does lead to a rapid identification of the major compositional shifts across large sets of samples; thus contributing to steer research pathways, minimise time-draining analytical procedures and reduce overall research costs.
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