Prediction of Lignin Content in Different Parts of Sugarcane Using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)

Abstract

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000-4000 cm were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( R), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.

Near infrared spectroscopy (NIRS) based high-throughput online assay for key cell wall features that determine sugarcane bagasse digestibility

“…5C-E). Notably, the o ine NIRS models achieved better prediction performance than those reported previously [26,30], which could be attributed to the large population of diverse samples employed for NIRS modeling in this study.…”

“…It has been used for characterization of cell wall polymer features [17][18][19][20], analysis of biomass sacchari cation e ciency [17,18,21], and prediction of ethanol production via yeast fermentation [22][23][24][25]. Notably, in sugarcane, some studied also have applied NIRS for determining cell wall components or prediction of digestibility [26][27][28][29]. In one of such efforts, Caliari et al [30] explored the NIRS assay for estimating cellulose crystallinity index.…”

June 2020

“…Thus it brings greater ease to the analysis. Assis et al, (2017) successfully predicted lignin content of sugarcane fresh stalk samples using NIR spectroscopy, obtaining a higher accuracy than we found herein. Moreover, results obtained by Valderrama et al, (2007) using a NIR protocol to predict SC in sugarcane juice samples also demonstrate the possibility of developing NIR based models with higher accuracies.…”

“…Thus, it is fundamental to develop new phenotyping strategies. The emergence of new technologies has allowed the application of Near Infrared Spectroscopy (NIR), combined with multivariate statistical methods, to determine the biochemical composition of a wide range of plant species biomass feedstock, including sugarcane (Liu et al, 2010;Santchurn et al, 2012;Assis et al, 2017). NIR has an excellent potential application (Montes.…”

Phenotypic classification of sugarcane from near infrared spectra obtained directly from stalk using ordered predictors selection and partial least squares-discriminant analysis

“…Biofuels, such as biodiesel and bioethanol, are products that combine energy security and sustainability. 1 Sugarcane (Saccharum spp.) is an important alternative energy source and can be considered one of the most important crops in the current Brazilian national farming business scenario.…”