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Sugars' quantifications using a potentiometric electronic tongue with cross-selective sensors: Influence of an ionic background

2019Journal article Open access
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dc.contributor.authorArca, Vinicius C.
dc.contributor.authorPeres, António M.
dc.contributor.authorMachado, Adélio A.S.C.
dc.contributor.authorBona, Evandro
dc.contributor.authorDias, L.G.
dc.date.accessioned2018-02-19T10:00:00Z
dc.date.accessioned2020-01-03T17:07:04Z
dc.date.available2018-01-19T10:00:00Z
dc.date.available2020-01-03T17:07:04Z
dc.date.issued2019
dc.description.abstractGlucose, fructose and sucrose are sugars with known physiological e ects, and their consumption has impact on the human health, also having an important e ect on food sensory attributes. The analytical methods routinely used for identification and quantification of sugars in foods, like liquid chromatography and visible spectrophotometry have several disadvantages, like longer analysis times, high consumption of chemicals and the need for pretreatments of samples. To overcome these drawbacks, in this work, a potentiometric electronic tongue built with two identical multi-sensor systems of 20 cross-selectivity polymeric sensors, coupled with multivariate calibration with feature selection (a simulated annealing algorithm) was applied to quantify glucose, fructose and sucrose, and the total content of sugars as well. Standard solutions of ternary mixtures of the three sugars were used for multivariate calibration purposes, according to an orthogonal experimental design (multilevel fractional factorial design) with or without ionic background (KCl solution). The quantitative models’ predictive performance was evaluated by cross-validation with K-folds (internal validation) using selected data for training (selected with the K-means algorithm) and by external validation using test data. Overall, satisfactory predictive quantifications were achieved for all sugars and total sugar content based on subsets comprising 16 or 17 sensors. The test data allowed us to compare models’ predictions values and the respective sugar experimental values, showing slopes varying between 0.95 and 1.03, intercept values statistically equal to zero (p-value 0.05) and determination coe cients equal to or greater than 0.986. No significant di erences were found between the predictive performances for the quantification of sugars using synthetic solutions with or without KCl (1 mol L􀀀1), although the adjustment of the ionic background allowed a better homogenization of the solution’s matrix and probably contributed to an enhanced confidence in the analytical work across all of the calibration working range.
dc.description.sponsorshipThis research work was funded by strategic project CIMO–PEst-OE/AGR/UI0690/2014 and Associate Laboratory LSRE-LCM–UID/EQU/50020/2019, financially supported by the FEDER—Fundo Europeu de Desenvolvimento Regional through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI); and by national funds through FCT—Fundação para a Ciência e a Tecnologia, Portugal
dc.description.versioninfo:eu-repo/semantics/publishedVersionen_EN
dc.identifier.citationArca, Vinicius da Costa; Peres, António M.; Machado, Adélio A.S.C.; Bona, Evandro; Dias, Luís G. (2019). Sugars' quantifications using a potentiometric electronic tongue with cross-selective sensors: Influence of an ionic background. Chemosensors. ISSN 2227-9040. 7. p. 1-16en_EN
dc.identifier.doi10.3390/chemosensors7030043en_EN
dc.identifier.isbn2227-9040
dc.identifier.urihttp://hdl.handle.net/10198/20088
dc.language.isoeng
dc.peerreviewedyesen_EN
dc.relationPOCI
dc.subjectExperimental designen_EN
dc.subjectK-means algorithmen_EN
dc.subjectMultiple linear regressionen_EN
dc.subjectPotentiometric electronic tongueen_EN
dc.subjectSimulated annealing algorithmen_EN
dc.subjectSugar analysisen_EN
dc.titleSugars' quantifications using a potentiometric electronic tongue with cross-selective sensors: Influence of an ionic backgrounden_EN
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/PEst-OE%2FAGR%2FUI0690%2F2014/PT
oaire.fundingStream5876
person.familyNamePeres
person.familyNameDias
person.givenNameAntónio M.
person.givenNameLuís G.
person.identifier107333
person.identifier.ciencia-idCF16-5443-F420
person.identifier.ciencia-id2F11-9092-FAAF
person.identifier.orcid0000-0001-6595-9165
person.identifier.orcid0000-0002-1210-4259
person.identifier.ridI-8470-2012
person.identifier.scopus-author-id7102331969
person.identifier.scopus-author-id23569169900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccessen_EN
rcaap.typearticleen_EN
relation.isAuthorOfPublication7d93be47-8dc4-4413-9304-5b978773d3bb
relation.isAuthorOfPublicationeac8c166-4056-4ed0-8d8d-7ecb2c4481a5
relation.isAuthorOfPublication.latestForDiscovery7d93be47-8dc4-4413-9304-5b978773d3bb
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relation.isProjectOfPublication.latestForDiscoveryf3fc3f7e-17b1-488f-abea-2601a44654b1

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