Machine learning methods for wine IR spectra analysis
At a glance
Infrared (IR) spectra of wine from two datasets have been analyzed. Categories were createdautomatically via machine learning methods. These categories group the wine by specifictype as well as color. The classification methods successfully achieved less than 5% error.Specific parameters were also quantified via regression methods, also with less than 5% error.Some parameters were not previously documented via IR spectroscopy for wine and includetannins, alcohol, pH, AcOH, and density. The project report also includes discussions about theoverall context of wine IR spectroscopy and its applications. A full evaluation was performedof the OPUS software offered by Bruker. A detailed list of possible improvements to thesoftware is provided.