Linear discriminant analysis with binary response is considered when the predictor is a functional random variable X = {Xt, t ∈ [0, T ]}, T ∈ R. Motivated by a food industry problem, we develop a methodology to anticipate the prediction by determining the smallest T * , T * ≤ T , such that X * = {Xt, t ∈ [0, T * ]} and X give similar predictions. The adaptive prediction concerns the observation of a new curve ω on [0, T * (ω)] instead of [0, T ] and answers to the question "How long should we

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... serve ω (T * (ω) =?) for having the same prediction as on [0, T ] ?". We answer to this question by defining a conservation measure with respect to the class the new curve is predicted.