We study the problem of online state estimation during wine fermentation. This problem becomes relevant when trying to control the alcoholic fermentation process with a control law relying on our capacity to estimate the full state, but with partial measurements of the system (which is the case in an industrial framework). We focus on studying observability properties of an alcoholic fermentation model in wine-making conditions. We implement an algorithm of state estimation based on optimality principles on expanding time windows (Full Information Estimator). In order to test the developed algorithms, we compare the results obtained on simulated data as well as experimental data obtained in wine fermentations performed at a laboratory scale.