BackgroundSoft sensors, also known as inferentials, play a crucial role in industrial processes by predicting important lab values or other slow-sampled variables. In many manufacturing environments, the process involves monitoring various parameters that are critical for quality control or process optimization. However, some of these parameters, such as lab values, are slow to sample due to the time required for testing. This delay can hinder real-time decision-making and potentially lead to inefficiencies or quality issues. Soft sensors offer a solution to this challenge by using fast-sampled inputs as inputs to a machine learning model. This model can then predict the slow-sampled variables, providing real-time insights without the need to wait for hours for the next lab sample to be tested. Operators can monitor these predictions and make timely adjustments to the process, optimizing operations and ensuring quality standards are met.