Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms

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...

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Veröffentlicht in:	Foods Jg. 10; H. 5; S. 1111
Hauptverfasser:	Spieß, Lukas, de Peinder, Peter, van den Bijgaart, Harrie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 18.05.2021 MDPI
Schlagworte:	Adulterants adulteration Algorithms Clusters Datasets Farms Food science Fourier transforms Fourier-transform infrared Infrared spectra Infrared spectroscopy Lactose liquids Mathematical models metadata Milk milk analysis milk composition Milk products Principal components analysis Proteins Quality assurance quality control Scientific imaging Screening spectra Spectrometry spectroscopy untargeted Netherlands cluster spectra Fourier-transform infrared chemometrics spectroscopy adulteration milk untargeted machine learning
ISSN:	2304-8158, 2304-8158
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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.
AbstractList	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.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. 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.
Author	de Peinder, Peter Spieß, Lukas van den Bijgaart, Harrie
AuthorAffiliation	1 Qlip B.V., P.O. Box 119, 7200 AC Zutphen, The Netherlands; bijgaart@qlip.nl 2 VibSpec, Haaftenlaan 28, 4006 XL Tiel, The Netherlands; info@vibspec.com
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Cites_doi	10.1111/1471-0307.12592 10.1016/j.foodchem.2015.02.077 10.1016/j.tifs.2018.04.001 10.3168/jds.2016-12102 10.1007/s11947-014-1455-y 10.1109/MCSE.2007.55 10.1038/s41586-020-2649-2 10.1007/s13197-017-2680-y 10.1111/1471-0307.12274 10.1016/j.foodres.2020.109543 10.17221/1708-CJAS 10.1016/j.talanta.2016.01.035 10.3168/jds.S0022-0302(97)75955-1 10.1093/jaoac/79.3.711 10.1079/9781845934590.0000 10.1038/s41592-019-0686-2 10.3168/jds.S0022-0302(80)82913-4 10.1016/j.foodres.2013.12.024 10.1016/j.idairyj.2004.01.005 10.1039/D0AN00062K
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Keywords	cluster spectra Fourier-transform infrared chemometrics spectroscopy adulteration milk untargeted machine learning
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SubjectTerms	Adulterants adulteration Algorithms Clusters Datasets Farms Food science Fourier transforms Fourier-transform infrared Infrared spectra Infrared spectroscopy Lactose liquids Mathematical models metadata Milk milk analysis milk composition Milk products Principal components analysis Proteins Quality assurance quality control Scientific imaging Screening spectra Spectrometry spectroscopy untargeted
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Title	Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms
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