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This work describes the development of a multivariate model based on near infrared reflectance spectroscopy (NIR) and partial least squares regression for the prediction of the moisture content in yerba mate samples. The multivariate model based on derivatized and multiplicative sign correction (MSC) spectral signals (4000-8500 cm-1) was elaborated with 3 latent variables, allowing the fast evaluation of the moisture content with average prediction errors of about 2.5%. The minimal manipulation of the samples permits a high analytical speed that facilitates the implementation of quality control operations

<b>Multivariate calibration and moisture control in yerba mate by near infrared spectroscopy<b>