The BSF and the SFC are delighted to welcome Peter Schieberle and Joel Mainland for our inaugural joint lecture.
The Sensomics Approach: Elucidating the Importance of Single Chemical Constituents in Food Aroma Profiles and Beyond
Univ. Prof. (em.) Dr. Peter Schieberle Faculty of Chemistry, Technical University Munich
Numerous consumer surveys confirm that the main drivers of food acceptance are aroma and taste, thus being the main attributes in any given food determining differences in the flavor signatures. Let’s focus on aroma: What makes a food smell good? It is well established today that during food consumption, a certain set of volatile constituents induces a pattern of neural activity in the olfactory bulb located in the nasal cavity. The complex neural patterns generated at the odorant receptor sites are finally “translated” by our brain into a simple perception telling us, for example, the aroma quality of fruit juices. However, since the overall aroma profile of juices is significantly influenced by (i) the fruit variety, (ii) the processing conditions or (iii) the storage, there is a clear need to first understand the aroma signature of the respective fresh product(the gold standard) on the molecular level. Therefore, only analytical methods based on bioactivity guided approaches will finally be able to suggest key aroma molecules for a reliable assessment of food quality, and more important to improve the overall aroma profile of a given product e.g., by optimizing industrial processes. In the past four decades, the Sensomics approach, formerly called molecular sensory science, was developed by our group aimed at decoding the aroma signature of foods, i.e., the exact quantitative ratio of the set of key aroma compounds causing the aroma perception at the odorant receptor level. As part of the approach, the analytical data are finally confirmed by re-engineering the respective food aroma on the basis of the quantitative data exactly displaying the natural concentrations in the food itself. Using pineapple juice as an example, in the first part of the talk the approach how to characterize complex aromas by breaking down the overall aroma sensation into single “molecular” odor responses will be presented. Clarification of a thermally induced off flavor formation in pineapple juice and molecular reasons for aroma losses during storage of NFC orange juice will be shown. In the second part of the talk, data on systematic structural modifications in selected key food odorants aimed at characterizing important odotopes, and results on the fate of aroma compounds in the human body beyond perception in the nasal cavity will be discussed. In the last part of the lecture, briefly a new Sensomics based method, assigned as “machine smelling”, will be presented on red wine aroma compounds without the need of GC/olfactometry. This method will make it possible in the future to detect, quantitate and select key food odorants with a single analytical platform.
Digitizing Olfaction: Predicting Odor Character from Molecular Structure
Joel D. Mainland, Adjunct Assistant Professor, Department of Neuroscience, University of Pennsylvania
If you have a modern phone you can capture a visual scene as a photograph, alter it, send it to a relative in another country in an instant, and store it so you can look at it for years to come. None of this is currently possible in olfaction. In vision and audition we know how to map physical properties to perception: wavelength translates into color and frequency translates into pitch. By contrast, the mapping from chemical structure to olfactory percept is poorly understood, limiting our ability to describe and control odors. This, in turn, limits our ability to understand how the olfactory system encodes perception. Olfaction has a higher dimensionality than the other senses, but recent models have shown that with enough data, machine learning techniques can predict human perception from molecular structure. We hypothesized that the rate-limiting step for building a model that predicts human perception from molecular structure is the collection of high-quality psychophysical data. Here I will discuss our work towards predicting the intensity and character of both single molecules and complex mixtures. This will allow us to predict the odor of novel molecules and mixtures and paves the way toward digitizing odors.
Dr. Joel Mainland earned a Ph.D. in neuroscience from UC Berkeley, where he studied the effects of sniffing on olfactory perception. He then worked at Duke University where he studied the molecular biology of human olfactory receptors.
Dr. Mainland is now an Associate Member at the Monell Chemical Senses Center, where his laboratory examines the relationship between molecular structure and olfactory perception.