High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

Abstract

BackgroundIn order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS).ResultsThe PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks.ConclusionEucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Regulated Lignin Structural Units and Soil Organic Carbon Content by Cowpea Peroxidase

“…Therefore, the peroxidase V max values mean that cowpea lignins have equal or are richer in guaiacyl (G) than in p-hydroxyphenyl (H) units. This is in agreement with the general observation which is based on lignin chromatographic analyses (Lupoi et al, 2014;Nunes et al, 2010;Spark, 2006) that syringyl (S) units are as abundant as G units in angiosperm lignins, with the H units being in smaller amounts (Kirk & Farrell, 1987). Peroxidase activity is suitable for determination of the chemical units of lignin.…”

“…The structural units of lignin, a complex biomaterial, is analyzed via oxidative degradation followed by spectroscopic characterization of the resultant syringyl and guaiacyl units (Lupoi et al, 2014;Nunes et al, 2010;Spark, 2006). But chemical degradation of lignin does not reveal the biological control of the structural composition.…”

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

“…The sampling techniques used for the acquisition of the wood samples used in this study have been described in a previous manuscript [8]. In addition to the 245 samples used for the construction of the PLS model, 269 diverse Acacia and eucalypt samples comprised the unknown sample matrix.…”

“…Regardless of the exact effects S/G ratios have on the saccharification of biomass, this parameter has proven to be important in developing a better understanding of lignin structure and degradation. In a previous study, mid-infrared (MIR), near-infrared (NIR), and FT-Raman spectra were coupled with lignin S/G ratios obtained using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the construction of multivariate analysis (MVA) models [8]. Various iterations were performed to determine the spectral processing techniques that provided the most robust calibration models.…”

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

“…Assessment of PLS model performance is often through the root-mean-square error (RMSE) associated with the model prediction and is sometimes defined as a predicted residual sum of squares (PRESS) when applied to a validation data set and provides an absolute assessment of the error associated with a predicted variable [61]. In the present work, R 2 terms are employed consistently as this metric can provide comparisons between variables of differing magnitudes (e.g., Fig.…”