Thomas G. Mayerhofer

Thomas G. Mayerhofer

Thomas G. Mayerhofer earned his diploma in chemistry from the University of Regensburg in 1996 and his PhD in physical chemistry from Friedrich Schiller University Jena in 1999. He completed his habilitation in 2006, specializing in the optics and infrared spectroscopy of polydomain materials. Since 2007, he has been a researcher at the Leibniz Institute of Photonic Technology, where he has worked to unify and advance infrared spectroscopy by integrating wave optics and dispersion theory. More recently, his research has focused on refining the theoretical foundations of Attenuated Total Reflection and pioneering the introduction of complex-valued chemometrics in spectroscopy. Mayerhöfer has published over 120 peer-reviewed papers—more than half as first author—primarily on spectroscopic theory. He is also the author of Wave Optics in Infrared Spectroscopy: Theory, Simulation, and Modeling.

Articles by Thomas G. Mayerhofer

Human body wireframe on glowing platform undergoing futuristic body scan. © sergray(noAIelemens) -chronicles-stock.adobe.com

Jurgen Popp, Thomas Mayerhofer, and colleagues at Leibniz IPHT and Friedrich Schiller University Jena introduce the Personalized Optical Digital Twin (PODT), a Photonics21 contribution to Europe's Virtual Human Twin ecosystem that connects molecular photonics—Raman blood analysis, coherent Raman tissue imaging, and multimodal endomicroscopy—with longitudinal physiology and clinical data. Drawing on the published multicenter INTELLIGENCE trials, the authors argue that technical feasibility and clinical utility must be evaluated separately as the field moves toward Europe's FP10 research agenda.

Thomas Mayerhöfer and Jürgen Popp

In this tutorial, Thomas G. Mayerhöfer and Jürgen Popp introduce complex-valued chemometrics as a more physically grounded alternative to traditional intensity-based spectroscopy measurement methods. By incorporating both the real and imaginary parts of the complex refractive index of a sample, this approach preserves phase information and improves linearity with sample analyte concentration. The result is more robust and interpretable multivariate models, especially in systems affected by nonlinear effects or strong solvent and analyte interactions.