Advisor(s)
Abstract(s)
Glucose, 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 L1), 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.
Description
Keywords
Experimental design K-means algorithm Multiple linear regression Potentiometric electronic tongue Simulated annealing algorithm Sugar analysis
Pedagogical Context
Citation
Arca, 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. 1-16