Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities
In 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 co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/26/19/5752 |
| _version_ | 1797516000531316736 |
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| author | Matthias Templ Barbara Templ |
| author_facet | Matthias Templ Barbara Templ |
| author_sort | Matthias Templ |
| collection | DOAJ |
| description | In 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. |
| first_indexed | 2024-03-10T06:55:08Z |
| format | Article |
| id | doaj.art-8d7870b0a1134bbd965d7aaf3e56cba7 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T06:55:08Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-8d7870b0a1134bbd965d7aaf3e56cba72023-11-22T16:32:07ZengMDPI AGMolecules1420-30492021-09-012619575210.3390/molecules26195752Statistical Analysis of Chemical Element Compositions in Food Science: Problems and PossibilitiesMatthias Templ0Barbara Templ1Institute of Data Analysis and Processe Design, Zurich University of Applied Sciences, Rosenstrasse 3, CH-8401 Winterthur, SwitzerlandData-Analysis OG, AT-1110 Vienna, AustriaIn 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.https://www.mdpi.com/1420-3049/26/19/5752composition of foodlog-ratio analysisPCAclassificationartificial neural networksadulteration |
| spellingShingle | Matthias Templ Barbara Templ Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities Molecules composition of food log-ratio analysis PCA classification artificial neural networks adulteration |
| title | Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities |
| title_full | Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities |
| title_fullStr | Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities |
| title_full_unstemmed | Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities |
| title_short | Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities |
| title_sort | statistical analysis of chemical element compositions in food science problems and possibilities |
| topic | composition of food log-ratio analysis PCA classification artificial neural networks adulteration |
| url | https://www.mdpi.com/1420-3049/26/19/5752 |
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