Yohko Miyanaga

Evaluation of the bitterness of antibiotics using a taste sensor

The bitterness of nine commercial antibiotics (clarithromycin, erythromycin, cefdinil, doxycycline, vancomycin, tetracycline, minocycline, oxytetracycline and bacampicillin) was evaluated in human gustatory sensation tests with nine volunteers. The bitterness of 0.1–0.3 mM solutions (or suspensions in the case of clarithromycin) of the antibiotics was then measured using an artificial multichannel taste sensor. In the sensor measurements, three variables were used to predict estimated bitterness in single and multiple regression analysis and principal component analysis: sensor output as relative value (R), the change of membrane potential caused by adsorption (C) and C/R. Particularly good correlation was obtained between obtained bitterness scores and predicted scores using C from channel 2 of the sensor (r2 = 0.870, P < 0.005) and C/R values for channels 2 and 3 (r2 = 0.947, P < 0.005). The taste sensor was also successful in assessing the bitterness intensity of clarithromycin powder suspensions of various concentrations. Clarithromycin has a low aqueous solubility but is the most bitter of the nine antibiotics. Sensory data from channel 3 of the sensor predicted the bitterness of clarithromycin powder suspensions and their filtered solutions well. Finally, the bitterness intensity of a commercial clarithromycin dry syrup product (Clarith dry syrup, Taisho Pharmaceutical Co. Ltd, Tokyo, Japan) was evaluated in gustatory sensation tests and using the taste sensor. In Clarith dry syrup the drug is coated with aminoalkyl methacrylate polymer using a spray congealing method. The taste sensor results confirmed that the polymer was successful in almost completely masking the bitter taste of the dry syrup product.

Prediction of the bitterness of single, binary- and multiple-component amino acid solutions using a taste sensor

The purpose of this study was to develop a quick, quantitative, prediction method for the determination of the bitterness of solutions containing one or more of five amino acids (L-isoleucine, L-leucine, L-valine, L-phenylalanine, and L-tryptophan), using an artificial taste sensor. The bitterness of various solutions containing different concentrations (1, 3, 10, 30, and 100 mM) of five amino acids, singly and in combination, was estimated using a multichannel taste sensor and compared with the results of human gustatory sensation tests with nine volunteers. The relative response electric potential patterns were similar for all five amino acids. Large sensor outputs were observed in channels 1-4 (which are negatively charged) while there were no responses in channels 5-8 (positively charged). The sensor output for channel 1, which was the largest output value, was used for prediction of bitterness. The change of membrane potential caused by adsorption (CPA), which corresponds to aftertaste, could not be used as an explanatory variable since the adsorption of the amino acids to the sensor membrane was weak and CPA values were small. The bitterness intensity scores for single, binary, and multi-component amino acid solutions, could be easily predicted on the basis of the sensor output value of channel 1 using regression analysis. Principal component analysis of the sensor output data suggested that the sourness, astringency and/or smell of the solutions also played a role in the perception of bitterness.

Quantitative Prediction of the Bitterness Suppression of Elemental Diets by Various Flavors Using a Taste Sensor

AbstractPurpose. The purpose of the study was to develop a method for the quantitative prediction of the bitterness suppression of elemental diets by various flavors and to predict the optimum composition of such elemental diets for oral administration using a multichannel taste sensor. Methods. We examined the effects of varying the volume of water used for dilution and of adding varying quantities of five flavors (pineapple, apple, milky coffee, powdered green tea, and banana) on the bitterness of the elemental diet, Aminoreban EN®. Gustatory sensation tests with human volunteers (n = 9) and measurements using the artificial taste sensor were performed on 50 g Aminoreban EN® dissolved in various volumes (140, 180, 220, 260, 300, 420, 660, 1140, and 2100 ml) of water, and on 50 g Aminoreban EN® dissolved in 180 ml of water with the addition of 3-9 g of various flavors for taste masking. Results. In gustatory sensation tests, the relationship between the logarithmic values of the volumes of water used for dilution and the bitterness intensity scores awarded by the volunteers proved to be linear. The addition of flavors also reduced the bitterness of elemental diets in gustatory sensation tests; the magnitude of this effect was, in decreasing order, apple, pineapple, milky coffee, powdered green tea, and banana. With the artificial taste sensor, large changes of membrane potential in channel 1, caused by adsorption (CPA values, corresponding to a bitter aftertaste), were observed for Aminoreban EN® but not for any of the flavors. There was a good correlation between the CPA values in channel 1 and the results of the human gustatory tests, indicating that the taste sensor is capable of evaluating not only the bitterness of Aminoreban EN® itself but also the bitterness-suppressing effect of the five flavors, which contained many elements such as organic acids and flavor components, and the effect of dilution (by water) on this bitterness. Using regression analysis of data derived from the taste sensor and from human gustatory data for four representative points, we were able to predict the bitterness of 50 g Aminoreban EN® solutions diluted with various volumes of water (140-300 ml), with or without the addition of a selected flavor. Conclusions. Even though this prediction method does not offer perfect simulation of human taste sensations, the artificial taste sensor may be useful for predicting the bitterness intensity of elemental diets containing various flavors in the absence of results from full gustatory sensation tests.