Search

Abstract: The application of the potentiometric multisensor system (electronic tongue, ET) for quantification of the bitter taste of structurally diverse active pharmaceutical ingredients (API) is reported. The measurements were performed using a set of bitter substances that had been assessed by a professional human sensory panel and the in vivo rat brief access taste aversion (BATA) model to produce bitterness intensity scores for each substance at different concentrations. The set consisted of eight substances, both inorganic and organic – azelastine, caffeine, chlorhexidine, potassium nitrate, naratriptan, paracetamol, quinine, and sumatriptan. With the aim of enhancing the response of the sensors to the studied APIs, measurements were carried out at different pH levels ranging from 2 to 10, thus promoting ionization of the compounds. This experiment yielded a 3 way data array (samples×sensors×pH levels) from which 3wayPLS regression models were constructed with both human panel and rat model reference data. These models revealed that artificial assessment of bitter taste with ET in the chosen set of API''s is possible with average relative errors of 16% in terms of human panel bitterness score and 25% in terms of inhibition values from in vivo rat model data. Furthermore, these 3wayPLS models were applied for prediction of the bitterness in blind test samples of a further set of API''s. The results of the prediction were compared with the inhibition values obtained from the in vivo rat model. [Copyright &y& Elsevier]

Abstract: An electronic tongue (ET) comprising 18 potentiometric chemical sensors was applied to the quantitative analysis of beer. Fifty Belgian and Dutch beers of different types were measured using the ET. The same samples were analyzed using conventional analytical techniques with respect to the main physicochemical parameters. Only non-correlated physicochemical parameters were retained for further analysis, which were real extract, real fermentation degree, alcohol content, pH, bitterness, color, polyphenol and CO2 content. Relationship between the ET and physicochemical datasets was studied using Canonical Correlation Analysis (CCA). Four significant canonical variates were extracted using CCA. Correlation was observed between 6 physicochemical variables (real extract and fermentation degree, bitterness, pH, alcohol and polyphenols’ content) and 14 sensors of the ET. The feasibility of the ET for the quantification of bitterness in beer was evaluated in the aqueous solutions of isomerised hop extract and in the set of 11 beers with bitterness varying between 14 and 38EBU (European Bitterness Units). Sensors displayed good sensitivity to isomerised hop extract and good prediction of the bitterness in beer was obtained. Calibration models with respect to the physicochemical parameters using ET measurements in 50 Belgian and Dutch beer samples were calculated by Partial Least Square regression. The ET was capable of predicting such parameters as real extract, alcohol and polyphenol content and bitterness, the latter with Root Mean Square Error of Prediction (RMSEP) of 2.5. [Copyright &y& Elsevier]