The presented research focuses on the electrochemical determination of capsaicin, a lipophilic alkaloid which originates hotness in chili peppers. An electrochemical sensor based on epoxy‐graphite composite with the modification of titanium dioxide (TiO2) nanoparticles is developed for the determination of this alkaloid. The measurements were carried out in glycine buffer at pH 2.5 using cyclic voltammetry. Two linear concentration ranges were obtained from 6 to 75 μM (R=0.99) and from 12 to 138 μM, with a detection limit of 5.34 μM and 11.3 μM capsaicin, for 1st and 2nd oxidation peak, respectively. The main advantage of developed sensor is its repeatability and robustness against fouling; the relative standard deviation (RSD) value was 2.53 % (n=10). This voltammetric sensing procedure has successfully been applied to quantify capsaicin in various real samples such as hot chili sauce and pharmaceutical preparations.
Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.
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