Production of food and beverages is often subject to variation in starting materials. In addition, things are happening during the production process itself that may impact on quality. To manage this situation, it is important that all critical production parameters have been identified and understood during the early stages of product development. It is equally important that the process is monitored appropriately throughout production, from starting material to the final quality measurement.
Both these tasks are part of what the
Food and Drug Administration
(FDA) is stressing in their
Process Analytical Technology
(PAT)) initiative and therefore it is important to have the right tools in place to achieve them.
Design of experiments
will detect the critical parameters, which will ensure a robust and stable process in the future.
Multivariate data analysis
will provide an understanding of the process data gathered during production allowing future production to be monitored with confidence.
These data may be conventional process measurements (e.g. flow rates, temperatures and pressures) or spectroscopic measurements (e.g. NIR), but both need multivariate techniques to provide the right interpretation. When measurements are collected on-line,
will transfer the multivariate models to the process control room, giving the engineers a new and powerful tool for timely and cost-effective fault detection.
Read more about advanced multivariate modeling with an example of multivariate calibration from a sugar production line: