Estimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods

Caliari, Ítalo Pelição; Barbosa, Márcio Henrique Pereira; Ferreira, S. O.; Teófilo, Reinaldo F.

doi:10.1016/j.carbpol.2016.12.005

Cited by 58 publications

(45 citation statements)

References 32 publications

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“…The use of chemometric methods to extract information from multivariate data, such as spectra, can significantly reduce the time, cost, and environmental impact of chemical analysis. 5,[13][14][15] In this sense, the use of spectroscopy in the region of the near infrared (NIR), which covers the range of 12 800-4000 cm À1 , has been widely and successfully applied in the nondestructive determination of lignin composition in various agricultural products. [16][17][18][19][20][21][22][23][24][25][26][27] However, few studies have been conducted with spectra obtained directly from the leaf and/or stem, which require no sample preparation procedure.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…For the organic matter constituent, the raw spectra presented the highest RER value. Caliari et al (2017) when comparing different spectral treatment, found that the best choice to be used was taking the first derivative with SG5 in sugarcane biomass when building regression models to estimate the cellulose crystallinity. Xie et al (2018) found the multiplicative scatter correction (MSC) + D2 as the best pre-treatment when modeling ash, FC and VM of biochar from very different feedstocks.…”

Section: Pre-treatmentsmentioning

confidence: 99%

Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

et al. 2020

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The real-time prediction in biorefinery industries has become essential. Models using partial least square regression (PLS) were developed to predict moisture, ash, volatile matter, fixed carbon and organic matter of coconut and coffee residues. On this study, 49 samples were collected and near infrared spectroscopy were applied to predict moisture, ash, volatile matter, fixed carbon and organic matter. For external validation 25% of the set samples were used. Moisture and volatile matter were predicted with coefficients of determination (R 2 cal) above 0.90, and standard errors (RSD) of the estimate of 14.4% and 2.26%, respectively. Models of ash and organic matter show R 2 cal > 0.77 and RSD values < 20.4%. For the external validation, the low deviations show the approximation between reference and predicted values and good prediction with R 2 pred > 0.70. All calibration models were acceptable for sample screening. This study demonstrates that PLS can be used to predict biomass composition of different species, with very low costs and time.

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“…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. However, most of such studies used o ine calibration strategy that necessitates certain time-consuming steps for NIRS scanning.…”

Section: Introductionmentioning

confidence: 99%

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

Liang

et al. 2020

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Background: The improvement of sugarcane cell wall structure is a promising strategy to enhance the bagasse digestibility to improve its prospects as a bioenergy crop. In this context, cellulose crystallinity (CrI) and lignin are the key parameters that influence the saccharification efficiency. Therefore, this study was conducted to develop a high-throughput assay for online characterization of these cell wall features in sugarcane. Results: A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of near-infrared spectroscopy (NIRS) was observed among the sugarcane samples. Due to significant diversity of the cell wall features in the sampled population of the crop, seven high quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination (R2) values higher than 0.8 and high ratio performance deviation (RPD) values over 2.0 in calibration, internal cross validation, and external validation. Particularly, the equations for CrI and the total lignin content exhibited the RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. Furthermore, the offline NIRS assay was also performed. A comparable calibration was observed between the offline and online NIRS analyses, suggesting that both of the two strategies would be applicable for estimating cell wall characteristics. Nevertheless, as online NIRS assay offers greater advantages for large-scale screening jobs, it could be implied as a better option for high-throughput cell wall features prediction. Conclusions: This study, as a foremost attempt, explored an online NIRS assay for high-throughput assessment of key sugarcane cell wall attributes in terms of CrI, lignin content, and its proportion in sugarcane. Consistent and precise calibration results were obtained in NIRS modeling; insinuating this strategy as a reliable approach for large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.

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Estimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods

Cited by 58 publications

References 32 publications

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

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

Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

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

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