Uncertainty Estimation of Some Sorption Isotherms Used for the Moisture Conditioning of Grains

Abstract

Sorption isotherms relate the equilibrium condition between the moisture content of a solid material and the relative humidity of ambient where such material is placed. Isotherms are useful in many fields where the water vapor of ambient affects the properties of materials. In particular, the information given by the sorption isotherms is useful in the moisture conditioning of solid materials, which are used for the calibration of moisture meters for grains, cereals and wood, among others. There are many isotherm models (almost one for each material). However, most of them do not estimate the uncertainty or, in some cases, the estimation is incomplete. On the other hand, the most known method for uncertainty evaluation is given by the Guide to the Expression of Uncertainty in Measurement (GUM). However, this guide has some restrictions to be satisfactorily used; for example, the model of measurand must be linear and have a known probability distribution function for the model inputs and similar uncertainty values. So often, the sorption isotherm models used for grains are highly nonlinear. Therefore, the GUM could not provide reliable results. To overcome this, an alternative method is the use of Monte Carlo simulation method, which is suggested for nonlinear models in the GUM supplement. In this paper, the uncertainty estimation was done with the GUM and Monte Carlo methods applied to some sorption isotherms, which are used for grains and cereals. The results with both methods showed some discrepancies, which are due mainly to the nonlinearity of models.