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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24600
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dc.contributor.authorTempl, Matthias-
dc.contributor.authorTempl, Barbara-
dc.date.accessioned2022-03-17T08:06:50Z-
dc.date.available2022-03-17T08:06:50Z-
dc.date.issued2021-09-23-
dc.identifier.issn1420-3049de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24600-
dc.description.abstractIn recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofMoleculesde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectPCAde_CH
dc.subjectAdulterationde_CH
dc.subjectArtificial neural networkde_CH
dc.subjectChemical profilingde_CH
dc.subjectClassificationde_CH
dc.subjectComposition of foodde_CH
dc.subjectHoneyde_CH
dc.subjectLog-ratio analysisde_CH
dc.subjectSaffronde_CH
dc.subjectCrocusde_CH
dc.subjectFood contaminationde_CH
dc.subjectFood technologyde_CH
dc.subjectNeural network, computerde_CH
dc.subjectPrincipal component analysisde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc664: Lebensmitteltechnologiede_CH
dc.titleStatistical analysis of chemical element compositions in Food Science : problems and possibilitiesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.3390/molecules26195752de_CH
dc.identifier.doi10.21256/zhaw-24600-
dc.identifier.pmid34641296de_CH
zhaw.funding.euNode_CH
zhaw.issue19de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start5752de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume26de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.monitoring.costperiod2022de_CH
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Templ, M., & Templ, B. (2021). Statistical analysis of chemical element compositions in Food Science : problems and possibilities. Molecules, 26(19), 5752. https://doi.org/10.3390/molecules26195752
Templ, M. and Templ, B. (2021) ‘Statistical analysis of chemical element compositions in Food Science : problems and possibilities’, Molecules, 26(19), p. 5752. Available at: https://doi.org/10.3390/molecules26195752.
M. Templ and B. Templ, “Statistical analysis of chemical element compositions in Food Science : problems and possibilities,” Molecules, vol. 26, no. 19, p. 5752, Sep. 2021, doi: 10.3390/molecules26195752.
TEMPL, Matthias und Barbara TEMPL, 2021. Statistical analysis of chemical element compositions in Food Science : problems and possibilities. Molecules. 23 September 2021. Bd. 26, Nr. 19, S. 5752. DOI 10.3390/molecules26195752
Templ, Matthias, and Barbara Templ. 2021. “Statistical Analysis of Chemical Element Compositions in Food Science : Problems and Possibilities.” Molecules 26 (19): 5752. https://doi.org/10.3390/molecules26195752.
Templ, Matthias, and Barbara Templ. “Statistical Analysis of Chemical Element Compositions in Food Science : Problems and Possibilities.” Molecules, vol. 26, no. 19, Sept. 2021, p. 5752, https://doi.org/10.3390/molecules26195752.


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