Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints uri icon

abstract

  • The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial J.A.P. All authors have read and agreed to the published version of the manuscript. support by national funds FCT/MCTES to CIMO (UIDB/00690/2020) and to CEB (UIDB/04469/2020) units, as well as BioTecNorte operation (NORTE-01-0145-FEDER-000004), FUNDEDE BY European Regional Development Fund under the scope of Norte2020-Programa Operacional Regional do Norte. Nuno Rodrigues also thanks support by national funds FCT/MCTES to CIMO (UIDB/00690/2020) and to CEB (UIDB/04469/2020) units, as the national funding by FCT–Foundation for Science and Technology, P.I., through the Institutional scientific well as BioTecNorte operation (NORTE-01-0145-FEDER-000004), funded by the European Regional employment program contract.
  • © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Sweet cherry is highly appreciated by its characteristic flavor, which conditions the consumer's preference. In this study, four sweet cherry cultivars (Durona, Lapins, Summit, and Van cultivars) were characterized according to biometric (fruit and stone weights, length, maximum and minimum diameters, pulp/stone mass ratio), physicochemical (CIELAB color, penetration force, titratable acidity, and total soluble solids), and potentiometric profiles (recorded by a lab-made electronic tongue with lipid polymeric membranes). Biometric and physicochemical data were significantly cultivar-dependent (p-value 0.0001, one-way ANOVA). Summit cherries had higher masses and dimensions. Lapins cherries had the highest penetration force values having, together with Summit cherries, the highest CIELAB values. Van cherries showed the highest total soluble solids contents. No significant differences were found for fruits' acidity (similar titratable acidities). The possibility of discriminating cherry cultivars was also evaluated using a linear discriminant analysis/simulated-annealing algorithm. A discriminant model was established based on nine non-redundant biometric-physicochemical parameters (using a low-level data fusion), with low sensitivity (75 ± 15% for the repeated K-fold cross-validation). On the contrary, a discriminant model, based on the potentiometric fingerprints of 11 selected sensors, allowed a better discrimination, with sensitivities of 88 ± 7% for the repeated K-fold cross-validation procedure. Thus, the electronic tongue could be used as a practical tool to discriminate cherry cultivars and, if applied by fruit traders, may reduce the risk of mislabeling, increasing the consumers' confidence when purchasing this high-value product.

publication date

  • January 2020