Process understanding and monitoring: A glimpse into data strategies for miniaturized NIR spectrometers

Background: The implementation of process analytical technologies (PAT) has gained attention since 2004 when
its formal introduction through the U.S. Food and Drug Administration was introduced. Manufacturers that need
to evaluate the employment of new monitoring systems could face different challenges: identification of suitable
sensors, verification of data meaning, evaluation of several statistical strategies to obtain insights about data and
achieve process understanding and finally, the actual possibilities for monitoring. Kefir fermentations were
chosen as an example because of the chemical and physical transformations that occurred during the process,
which could be common to several other fermentation processes. In order to pave the way for monitoring
establish the information contained in the data and find the right tools for extracting them is of extreme
importance. Strategies to identify different experimental conditions in the spectra acquired with a miniaturized
NIR (1350-2550 nm) during process occurrence were addressed.
Results: The study aims to offer insights into good practices and steps to pave the way for process monitoring with
handheld NIR data. The main aspects of interest for batch processes in preliminary evaluations were investigated
and discussed. On the one hand, process understanding and, on the other, the possibilities for process monitoring
and endpoint determination were examined. The combination of different statistical tools allowed the extraction
of information from the data and the identification of the link between them and the chemical and physical
changes during the process. In addition, insights into the spectra characteristics in the studied spectroscopic
range for kefir fermentation were reported.
Significance: The capabilities for miniaturized NIR spectra to represent and statistical strategies to characterize
different experimental conditions in a real case fermentation occurrence were proved. The strengths and limitations of some of the common approaches to catch changes in fermentation condition were highlighted. For the
various statistical approaches, the chances offered in the research and development stages and to set the scene for
monitoring and end-point detection were explored