Data generated by analytical instruments, such as spectrometers, may contain unwanted variation due to measurement mode, sample state and other external physical, chemical and environmental factors. Preprocessing is required so that the property of interest can be predicted correctly. Different correction methods may remove specific types of artefacts while still leaving some effects behind. Using multiple preprocessing in a complementary way can remove the artefacts that would be left behind by using only one technique. This article summarizes the recent developments in new data preprocessing strategies and specifically reviews the emerging ensemble approaches to preprocessing fusion in chemometrics. A demonstration case is also presented. In summary, ensemble preprocessing allows the selection of several techniques and their combinations that, in a complementary way, lead to improved models. Ensemble approaches are not limited to spectral data but can be used in all cases where preprocessing is needed and identification of a single best option is not easily done. •New developments in the domain of data pre-processing are summarized.•Several new approaches to pre-processing optimization are discussed and compared.•Different preprocessings such as scatter correction methods carries complementary information.•Ensemble fusion allows the use of complementary information to boost chemometrics models.•Multi-block data analysis-based ensemble approaches are superior to other ensemble approaches.