Machine learning methods for wine IR spectra analysis
At a glance
Infrared (IR) spectra of wine from two datasets have been
analyzed. Categories were created
automatically via machine learning methods. These categories group the wine by specific
type 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 include
tannins, alcohol, pH, AcOH, and density. The project report also includes discussions about the
overall context of wine IR spectroscopy and its applications. A full evaluation was performed
of the OPUS software offered by Bruker. A detailed list of possible improvements to the
software is provided.