Predicting bitterness in foods
One of the five basic taste modalities, bitterness has long intrigued scientists and food experts alike. While bitterness is assumed to have a fundamental role in helping us avoid consuming toxic food, it is not always the case as many bitter compounds are associated with health benefits and in some food, such as coffee and wine, bitterness is a desired quality.
Now a promising novel tool could transform how bitterness is understood and managed in foods and beverages.
The BitterMasS tool was developed by a team led by PhD student Evgenii Ziaikin and Professor Masha Niv from Hebrew University and Dr Edisson Tello and Professor Devin Peterson from Ohio State University.
With wide-ranging applications in food science, pharmaceuticals and beyond, the tool uses the power of mass spectrometry to predict bitterness in compounds without requiring prior knowledge of their chemical structures. In contrast, traditional methods rely on structural data, which only cover a small fraction of the metabolome.
The novel tool is predicted to not only accelerate taste perception research but also holds potential for transforming food processing, health discoveries and safety monitoring.
Using a dataset of over 5400 experimental mass spectra of bitter and non-bitter compounds, BitterMasS achieved good precision and recall rates in internal tests. The findings have been published in ACS publication Journal of Agricultural and Food Chemistry.
“BitterMasS represents a critical shift in taste prediction,” said Niv, lead researcher. “By leveraging mass spectrometry data, we can now predict bitterness directly and efficiently, opening doors to new discoveries in health-promoting compounds and enhanced food processing techniques.”
Researchers envision BitterMasS as a versatile tool capable of monitoring bitterness changes over time, providing insights into food quality and safety. The approach could also be suitable for applications in drug development and metabolomics.
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