LAUSR

BACKGROUND The chemical compounds of coffee are important indicators of quality. This composition varies according to several factors related to planting and processing of coffee. Thus, this study proposed to use the near infrared spectroscopy (NIR) associated with partial least squares regression (PLS) for quickly estimate of some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, a total of 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded. RESULTS The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, RMSECV, RMSEP and RPD and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar and reducing sugars, respectively. CONCLUSION The statistics associated with these models indicate that NIR technology has potential to be routinely applied for predicting green coffee chemical properties, in particular, for moisture analysis. However, the content of soluble solids and total sugars did not show high correlations with the spectroscopic data and need to be improved. This article is protected by copyright. All rights reserved.