| Abstract 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... |
