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. In 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 (R2cal) above 0.90, and standard errors (RSD) of the estimate of 14.4% and 2.26%, respectively. Models of ash and organic matter show R2cal > 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 R2pred > 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.

In search for renewable energy sources, the Brazilian residual biomasses stand out due to their favorable physical and chemical properties, low cost, and their being less pollutant. Therefore, they are likely to be used in biorefineries in the production of chemical inputs to substitute fossil fuels. This substitution is possible due to the high contents of carbohydrates (>40%), low contents of extractives (<20%), ashes (<8%) and moisture (<8%) found in these residual biomasses. High calorific values of all residues also offer them the chance to be used in combustion. A principal components analysis (PCA) was performed for better understanding of the samples and their hysic-chemical properties. Thus, this study aimed to characterize biomasses from the north (babassu residues, such as mesocarp and endocarp; pequi and Brazil nut) and northeast (agave and coconut) regions of Brazil, in order to contribute to the preservation of the environment and strengthen the economy of the region.