While triplicate measurement can largely improve the result, it is still statistically speaking a relatively low number of repetitions. Yet, for practical reasons more repeats as standard setting is not advisable for economic reasons. Thus, at the end of an experimental series, the control software performed an outlier detection based on the intensity weighted particle size distributions and replicates per each experiment using a modified z-score method. If a triplicate set of data showed an inconsistency (hence large variation in data), then the detected outlier experiment was reinitialized and repeated again twice for further clarification. The outlier detection is iterative, hence further duplicate experiments would be carried out automatically if still required. This automated data cleaning step was very important as a manual correction would be highly timeconsuming, and also require an intervention step that would interrupt the continuous measurement.