Classification of Apples (Malus x domestica borkh.) According to Geographical Origin, Variety and Production Method Using Liquid Chromatography Mass Spectrometry and Random Forest

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Autor/in:
Erscheinungsjahr:
2025
Medientyp:
Text
Schlagworte:
  • Apples
  • Feature selection
  • Food authentication
  • Machine learning
  • non-targeted LC-HRMS
  • Omics
  • Random forest
Beschreibung:
  • Apples are one of the most popular fruits in Germany, valued for their regional availability and health benefits. When deciding which apple to buy, several characteristics are important to consumers, including the taxonomic variety, organic cultivation and regional production. To verify that these characteristics are correctly declared, powerful analytical methods are required. In this study, ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-ToF-MS) is applied in combination with random forest to 193 apple samples for the analysis of various authentication issues. Accuracies of 93.3, 85.5, 85.6 and 90% were achieved for distinguishing between German and non-German, North and South German, organic and conventional apples and for six different taxonomic varieties. Since the classification models largely use different parts of the data, which is shown by variable selection, this method is very well suited to answer different authentication issues with one analytical approach.
Lizenz:
  • info:eu-repo/semantics/openAccess
Quellsystem:
Forschungsinformationssystem der UHH

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oai:www.edit.fis.uni-hamburg.de:publications/184023ab-612c-4236-96a5-fa9c7c180458