BettaSensing

BettaSensing

Flavor recognition technology for food and beverages

About us

BettaSensing is developing a sensing technology for use in R&D and quality control, relying on a hardware/software combination that can identify food and beverage items based on their chemical and flavor profile

The Team

  • Dr. Pernille Ollendorff Hede: CEO

    Dr. Pernille Ollendorff Hede: CEO

    Expertise: business development, operations, finances

  • Dr. Noga Gal: CTO and Founder

    Dr. Noga Gal: CTO and Founder

    Scientific Expertise: Sensing, colloidal science, microfluidics, material science, chemistry

  • Board of Director

  • Dr. Jan Mousing: Chair of the management board.

    Dr. Jan Mousing: Chair of the management board.

    Expertise: Business development. Bioresources. Food. Food safety. Biotechnology.

  • Assoc. Prof. Brigitte St├Ądler: Board member, advisor and co-founder

    Assoc. Prof. Brigitte St├Ądler: Board member, advisor and co-founder

    Scientific Expertise: Chemistry and Nanotechnology

  • Dr. Peter Dybdahl Ollendorff Hede: Board member

    Dr. Peter Dybdahl Ollendorff Hede: Board member

    Expertise: Business development. incorporation, equity investments

Activities

  • EIT Food SeedBed incubator participant 05-11/2020

  • Finalist in the Emerging Technologies competition 2020 in Food & Drink category 08/2020

  • Top 5 in the product Category of the Venture Cup competition 09/2020

  • 1 year partnership and mentoring program with Dell for entrepreneurs 10/2020-09/2021

What we do

Our sensor grids are based on a library of patented, environmentally sensitive polymer variants, that change color in response to stimuli, in which its responsiveness can be adapted for different needs. The sensitivity of the polymer can vary from highly specific entities that react with selected molecules in the test solution (e.g. beverages) to chemical groups that only allow for weak non-specific interactions.

In contrast to the current sensing paradigm, the color change of each individual sensor in our array does not have any discriminative power, but the combined response of the entire grid can be analyzed using ML methods in order to obtain food/beverage specific profile (fingerprint). As a result, complex mixtures can be separated based on their variations in their flavor profile. The platform itself can either be a paper-based dipstick with an array of spotted sensors or as a well plate filled with nano-particulate sensors made from the responsive polymers.

Partners & Advisors