  In April 2022, the Vistamilk SFI Research Centre organized the second edition
of the "International Workshop on Spectroscopy and Chemometrics - Applications
in Food and Agriculture". Within this event, a data challenge was organized
among participants of the workshop. Such data competition aimed at developing a
prediction model to discriminate dairy cows' diet based on milk spectral
information collected in the mid-infrared region. In fact, the development of
an accurate and reliable discriminant model for dairy cows' diet can provide
important authentication tools for dairy processors to guarantee product origin
for dairy food manufacturers from grass-fed animals. Different statistical and
machine learning modelling approaches have been employed during the workshop,
with different pre-processing steps involved and different degree of
complexity. The present paper aims to describe the statistical methods adopted
by participants to develop such classification model.
