AI Article Synopsis
- Researchers utilized three non-targeted methods (H NMR, LC-HRMS, and HS-SPME/MS-eNose) with chemometrics to classify two grape cultivars, Italia and Victoria, into five quality levels.
- The MS-eNose method yielded the highest prediction abilities, achieving up to 99% accuracy in distinguishing marketable from non-marketable grapes for both cultivars.
- The study also highlighted the identification of key compounds that influenced the quality classifications, validating their models with a multi-cross-validation approach.
Article Abstract
Three non-targeted methods, i.e. H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified.
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| http://dx.doi.org/10.1016/j.foodchem.2020.126247 | DOI Listing |
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