A new study published in Food Chemistry demonstrates a standardized, non-targeted metabolomics workflow designed to generate comparable food metabolomics data across laboratories, instruments, and analytical platforms. The research addresses a longstanding challenge in food and nutrition science: the difficulty of comparing metabolomic datasets generated by different organizations and research groups. By developing an end-to-end workflow that incorporates a novel internal retention time standard and robust alignment methods, the authors show that diverse laboratories can produce highly consistent metabolomic profiles across multiple food matrices. This represents an important step toward creating interoperable food composition datasets and improving confidence in large-scale metabolomics studies.
For UC Davis and the Innovation Institute for Food and Health (IIFH), the work highlights the leadership of Professor Oliver Fiehn and the UC Davis metabolomics ecosystem in advancing the scientific infrastructure needed to connect food composition, health outcomes, and innovation. Fiehn’s research has long focused on developing standardized metabolomics methods, databases, and analytical tools that enable researchers to generate reliable, comparable data at scale. This latest study reinforces the importance of common standards as the food industry, researchers, and policymakers increasingly seek evidence-based approaches to understand how food composition influences nutrition, health, and sustainability. By helping establish a shared analytical foundation for food metabolomics, UC Davis researchers are contributing critical capabilities that can accelerate food innovation and support future discoveries across food and health systems.
