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An efficient formulation of the data reconciliation problem applied to batch reactor experiments is proposed and discussed in comparison with other works reported on this subject in the literature. Under the assumption that all components in the reaction are known experimentally and that their chemical compositions are defined, atomic balances are used as constraints in order to correct raw data values. The formulation is based on an a posteriori simultaneous treatment of all measurements over the whole time of experimentation. The main point of interest in this formulation is the preservation of the measurement identity in each sample. Even if component holdups are systematically involved in mass balances by pairs to estimate a difference in time, here we correct each component measurement and each sample independently. On this basis, it is shown how classical statistical tests can be used to discriminate gross errors. A case study built from simulated true and noisy data gives an overview of the performances of the method. The results state that the overall time formulation is more powerful than the classical one, which is essentially based on a sequential treatment by periods of time. Evaluation of the performance for three adapted statistical tests leads us to conclude that the generalized likelihood ratio (GLR) is not only the most appropriate but also the most efficient test.