Getting “sampling” right for reliable process monitoring: main challenges and solutions

In modern scientific and industrial environments, processes are increasingly monitored through dense networks of sensors generating high-dimensional and often, continuous data streams. Process Analytical Technology (PAT), originally developed in the pharmaceutical industry but now widely applied across biotechnology, food production, environmental monitoring, cultural heritage preservation, and healthcare, provides an empowering framework with which to link real-time measurements to critical quality attributes and process parameters, enabling optimal process understanding, control, and optimization. Despite continuing, impressive advances in sensor technology and chemometric modelling, a persistent misconception is that data quantity alone ensures representativity and model reliability. While data quality is important, remaining a critical success factor for PAT, material heterogeneity, sensor placement, sampling frequency, and process sampling interface design together determine how well observed process data variability and the collected data reflect the true system variability without being unduly compromised by the total measurement system errors and uncertainties. This article emphasizes the central role of the Theory of Sampling (TOS) in ensuring representative increment, sample and data acquisition, illustrating the most common sampling pitfalls including over-reliance on data augmentation, poor field-of-view selection, and neglect of temporal dynamics and demonstrates how these factors impact chemometric models across diverse applications. Emerging approaches, such as automated adaptive sampling and digital twins, offer potential to optimize data collection, but their effectiveness still relies fundamentally on informative primary sensors, which must be TOS-compliant, no exceptions. Through article reviews and examples including Raman and near infrared datasets from materials like rocks, coffee, and beer fermentation, we highlight practical strategies for integrating sampling design with sensor deployment. Overall, this study emphasizes the critical need to prioritize representative sampling, as advanced sensor technology and powerful data modelling cannot alone compensate for systematic measurement system bias, thereby ensuring reliable, interpretable, and actionable process insights.