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Sugars’ Quantifications Using a Potentiometric Electronic Tongue with Cross-Selective Sensors: Influence of an Ionic Background

机译:糖的量化使用具有交叉选择性传感器的电位电子舌:离子背景的影响

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Glucose, fructose and sucrose are sugars with known physiological effects, and their consumption has impact on the human health, also having an important effect 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 coefficients equal to or greater than 0.986. No significant differences 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.
机译:葡萄糖,果糖和蔗糖是具有已知生理效果的糖,它们的消费对人类健康产生影响,对食物感官属性也具有重要影响。常规用于食品中糖的分析方法,如液相色谱和可见分光光度法,如液相色谱和可见分光光度法,具有若干缺点,即更长的分析时间,化学品的高消耗以及对样品预处理的需要。为了克服这些缺点,在这项工作中,用两个交叉选择性聚合物传感器的两个相同的多传感器系统构成的电位电子舌,施加与具有特征选择(模拟退火算法)的多变量校准(模拟退火算法)进行量化以量化葡萄糖,果糖和蔗糖和糖的总含量也是如此。根据具有或没有离子背景(KCl Solution)的正交实验设计(多级分数阶段设计),使用三元三个糖的三元混合物的标准溶液用于多变量校准目的。使用用于使用测试数据的外部验证,通过用k折叠(内部验证)的交叉验证来评估定量模型的预测性能。总体而言,对于基于包括16或17个传感器的子集来实现所有糖和总糖含量的令人满意的预测量化。测试数据允许我们比较模型的预测值和相应的糖实验值,显示斜坡在0.95和1.03之间变化,截障值统计上等于零(P-value≥0.05),并且确定系数等于或大于0.986。在使用具有或没有KCl(1molL≥1)的合成溶液的预测性能之间没有显着差异(1mol L 1),尽管离子背景的调节允许更好地均匀化溶液的矩阵,并且可能导致增强在所有校准工作范围内对分析工作的信心。

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