Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice there are several ways to prepare and conserve QC samples. The most common in untargeted metabolomics is to pool samples after or before extraction, in order to obtain pooled QC samples accounting respectively for analytical variance or for both analytical and sample preparation variances. In this study, focusing on untargeted analysis of tea (Camellia sinensis) leaves, we compared three ways of preparing pooled QC samples (two usual and one unusual QC sample preparations) and their efficiency to improve data quality in terms of inter-batch correction, measurement precision, and VIP candidates selection on datasets obtained using two mass spectrometry (MS) technologies (Orbitrap and time of flight (QToF)). We also investigated the effect of data processing modalities, based on the different QC preparations, on data loss and on the global structure of the datasets. Generally, our results show that usual QC sample preparation leads to comparable datasets quality in terms of precision and dispersion on both MS instruments. They also show that QC preparation is crucial for VIP selection, in fact, up to 54% of biomarkers candidates were specific of the QC preparation type used for data processing.