Journal
FOODS
Volume 10, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/foods10051111
Keywords
Fourier-transform infrared; spectroscopy; milk; adulteration; spectra; untargeted; cluster; chemometrics; machine learning
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The study evaluates the potential of using Fourier-transform mid-infrared spectrometry combined with cluster algorithms to screen adulterated liquid milk products. The combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra, providing valuable complementary quality assurance in routine FTIR milk analysis. Spectral information and meta-data associated with their acquisition can be used to form hypotheses about the underlying causes of atypical milk composition.
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.
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