Prediction of high-biomass sorghum quality using near infrared spectroscopy to monitoring Calorific value, Moisture, and Ash content

High-biomass sorghum is a crop that has great potential as a source of biomass for energy generation, due to its high productivity, drought tolerance and for being mechanizable. Thus, culture is an alternative to vegetable biomass to be used in electric energy cogeneration processes. The objective of the work was to develop multivariate calibration models, using the near infrared spectroscopy, for analysis of gross calorific value, moisture, and ash content in high-sorghum biomass. At samples were analyzed by reference methods and the results associated with the near infrared spectrum of each sample. Then they were developed for each parameter, multivariate calibration models using the partial least square (PLS) algorithm. A high correlation was obtained between the values predicted by the model and the values obtained by reference method for all properties evaluated. Ratio of prediction to deviation (RPD) and range error ratio (RER) values, respectively, above 3 and 10, for all the models constructed, thus being considered adequate for carrying out quantitative analyzes of chemical composition in the qualification of the sorghum biomass as a source of raw material for energy cogeneration and optimization of biomass conversion technologies.