Chemometrics as a powerful quality tool for OCM

Strategies for managing complex systems often draw on tools from non-chemical scientific disciplines such as statistics, mathematics, computer science and artificial intelligence. Modern analytical methodologies and multivariate data analysis techniques help extract important information from generated signals.

Chemometrics is the chemical discipline that uses mathematical methods to extract useful information from chemical data. Integrating chemometrics with other analytical techniques, methodologies and sensors, as well as chromatographic and spectroscopic techniques or machine learning methods, improves the quality of analytical results in OCM.

Chemometrics can be applied in an OCM laboratory to detect measurement errors, confirm results or replace “Wet chemical analysis”. Integrating it into LIMS improves workflow, reduces TAT and carbon footprint, and so on.

However, successfully implementing chemometrics in a modern laboratory involves more than just mathematics; it requires a complex operational ecosystem. This process relies on three key figures: the technician, who ensures high-quality sample preparation and data acquisition; the IT specialist, who integrates machine learning (ML) pipelines into the laboratory infrastructure; and the diagnostician, who interprets the artificial intelligence (AI)-generated results to make critical decisions.

Real cases of chemometrics integration in OCM are shown, highlighting actual problems and how they should be addressed.