Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/67672
Título: | Discrimination of sweet cherry cultivars based on electronic tongue potentiometric fingerprints |
Autor(es): | Rodrigues, Isabel Rodrigues, Nuno Marx, Ítala M. G. Veloso, Ana C. A. Ramos, Ana Cristina Pereira, José Alberto Peres, António M. |
Palavras-chave: | sweet cherry biometric data CIELAB color scale chemical data potentiometric taste sensor analysis of variance linear discriminant analysis simulated annealing algorithm cultivar discrimination |
Data: | 2020 |
Editora: | MDPI |
Revista: | Applied Sciences |
Citação: | Rodrigues, Isabel; Rodrigues, Nuno; Marx, Ítala M. G.; Veloso, Ana C. A.; Ramos, Ana Cristina; Pereira, José Alberto; Peres, António M., Discrimination of sweet cherry cultivars based on electronic tongue potentiometric fingerprints. Applied Sciences-Basel, 10(20), 7053, 2020 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/67672 |
DOI: | 10.3390/app10207053 |
e-ISSN: | 2076-3417 |
Versão da editora: | https://www.mdpi.com/2076-3417/10/20/7053 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_53946_1.pdf | 1,2 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons