Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"

TL;DR

This paper discusses the development of classification models to determine cow diets from milk mid-infrared spectra, highlighting various statistical and machine learning approaches used in a recent data competition.

Contribution

It provides a comparative analysis of different modeling techniques applied by participants in a spectral data challenge for cow diet classification.

Findings

Multiple modeling approaches were employed with varying pre-processing steps.

The competition demonstrated the effectiveness of machine learning in spectral classification.

Results highlight the potential for spectral data to authenticate dairy product origins.

Abstract

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.