Advances in atypical ft-ir milk screening: Combining untargeted spectra screening and cluster algorithms

Lukas Spieß*, Peter de Peinder, Harrie van den Bijgaart

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis.

Original languageEnglish
Article number1111
JournalFoods
Volume10
Issue number5
DOIs
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Adulteration
  • Chemometrics
  • Cluster
  • Fourier-transform infrared
  • Machine learning
  • Milk
  • Spectra
  • Spectroscopy
  • Untargeted

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