  The sorption curve is an essential feature for the modelling of heat and mass
transfer in porous building materials. Several models have been proposed in the
literature to represent the amount of moisture content in the material
according to the water activity (or capillary pressure) level. These models are
based on analytical expressions and few parameters that need to be estimated by
inverse analysis. This article investigates the reliability of eight models
through the accuracy of the estimated parameters. For this, experimental data
for a wood fibre material are generated with special attention to the stop
criterion to capture long time kinetic constants. Among five sets of
measurements, the best estimate is computed. The reliability of the models is
then discussed. After proving the theoretical identifiability of the unknown
parameters for each model, the primary identifiability is analysed. It
evaluates whether the parameters influence on the model output is sufficient to
proceed the parameter estimation with accuracy. For this, a continuous
derivative-based approach is adopted. Seven models have a low primary
identifiability for at least one parameter. Indeed, when estimating the unknown
parameters using the experimental observations, the parameters with low primary
identifiability exhibit large uncertainties. Finally, an Approximation Bayesian
Computation algorithm is used to simultaneously select the best model and
estimate the parameters that best represent the experimental data. The
thermodynamic and \textsc{Feng}--\textsc{Xing} models, together with a proposed
model in this work, were the best ones selected by this algorithm.
