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.
Original language | English |
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Article number | 104755 |
Journal | Chemometrics and Intelligent Laboratory Systems |
Volume | 234 |
Early online date | 20 Jan 2023 |
DOIs | |
Publication status | Published - 15 Mar 2023 |
Bibliographical note
Funding Information:This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland under grant number (16/RC/3835) , the SFI Insight Research Centre under grant number (SFI/12/RC/2289_P2) and the SFI Starting Investigator Research Grant “Infrared spectroscopy analysis of milk as a low-cost solution to identify efficient and profitable dairy cows” (18/SIRG/5562) .
Publisher Copyright:
© 2023
Keywords
- Chemometrics
- Food authenticity
- Fourier transform mid-infrared spectroscopy
- Machine learning
- Milk quality
ASJC Scopus subject areas
- Analytical Chemistry
- Software
- Computer Science Applications
- Process Chemistry and Technology
- Spectroscopy