Hmdb loader
Welcome to the BevTestDB
BevTestDB is a next-generation database designed to power modern beverage testing, quality control, and innovation. Built on the foundation of our globally recognized food constituent databases, BevTestDB brings together detailed chemical, sensory, safety, and concentration data for hundreds of compounds commonly found in beverages. Using a combination of expert curation, scientific literature mining, and machine-learning–driven text analysis, BevTestDB links each compound to its taste, aroma, safety profile, and typical concentration ranges. The database is tightly integrated with BevTest analytical kits, allowing results to be instantly interpreted and transformed into actionable insights. Together, the BevTest kits and BevTestDB enable beverage producers, testers, and manufacturers to compare products, monitor quality, detect contaminants, control batch variability, and optimize flavor, safety, and health attributes with confidence.

Citing the BevTestDB Papers:

  1. Brian L. Lee, Alyaa Selim, Alanne Tenório Nunes, Prashanthi Kovur, Rupasri Mandal and David S. Wishart (2025). Automatic NMR Spectral Profiling of Commercial Cow’s Milk. ACS Food Science & Technology 2025 5(8), 2989-2999. DOI: 10.1021/acsfoodscitech.5c00268
  2. Brian L. Lee, Fatemeh Shahin, Alyaa Selim, Mark Berjanskii, Claudia Torres-Calzada, Prashanthi Kovur, Rupasri Mandal, and David S. Wishart (2024). Automated Beer Analysis by NMR Spectroscopy. ACS Food Science & Technology 2025, 5(1), 378-388. DOI: 10.1021/acsfoodscitech.4c00896
  3. Brian L. Lee, Manoj Rout, Ying Dong, Matthias Lipfert, Mark Berjanskii, Fatemeh Shahin, Dipanjan Bhattacharyya, Alyaa Selim, Rupasri Mandal, and David S. Wishart (2024). Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy. ACS Food Science & Technology 2024, 4(8), 1937-1949. DOI: 10.1021/acsfoodscitech.4c00298