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Classification of Prunus Genus by Botanical Origin and Harvest Year Based on Carbohydrates Profile.

Marius Gheorghe MiricioiuRoxana-Elena IoneteDiana CostinelOana Romina Botoran
Published in: Foods (Basel, Switzerland) (2022)
The 1 H-NMR carbohydrates profiling was used to discriminate fruits from Rosaceae family in terms of botanical origin and harvest year. The classification was possible by application of multivariate data analysis, such as principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson analysis. Prior, a heat map was created based on 1 H-NMR signals which offered an overview of the content of individual carbohydrates in plum, apricot, cherry and sour cherry, highlighting the similarities. Although, the PCA results were almost satisfactory, based only on carbohydrates signals, the LDA reached 94.39% and 100% classification of fruits according to their botanical origin and growing season, respectively. Additionally, a potential association with the relevant climatic data was explored by applying the Pearson analysis. These findings are intended to create an efficient NMR-based solution capable of differentiating fruit juices based on their basic sugar profile.
Keyphrases
  • data analysis
  • magnetic resonance
  • machine learning
  • deep learning
  • high resolution
  • magnetic resonance imaging
  • mass spectrometry
  • risk assessment
  • computed tomography
  • climate change
  • big data