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Machine learning methods for wine IR spectra analysis

At a glance

Description

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.