Bitterness prediction of H1-antihistamines and prediction of masking effects of artificial sweeteners using an electronic tongue

Abstract

The study objective was to quantitatively predict a drug's bitterness and estimate bitterness masking efficiency using an electronic tongue (e-Tongue). To verify the predicted bitterness by e-Tongue, actual bitterness scores were determined by human sensory testing. In the first study, bitterness intensities of eight H(1)-antihistamines were assessed by comparing the Euclidean distances between the drug and water. The distances seemed not to represent the drug's bitterness, but to be greatly affected by acidic taste. Two sensors were ultimately selected as best suited to bitterness evaluation, and the data obtained from the two sensors depicted the actual taste map of the eight drugs. A bitterness prediction model was established with actual bitterness scores from human sensory testing. Concerning basic bitter substances, such as H(1)-antihistamines, the predictability of bitterness intensity using e-Tongue was considered to be sufficiently promising. In another study, the bitterness masking efficiency when adding an artificial sweetener was estimated using e-Tongue. Epinastine hydrochloride aqueous solutions containing different levels of acesulfame potassium and aspartame were well discriminated by e-Tongue. The bitterness masking efficiency of epinastine hydrochloride with acesulfame potassium was successfully predicted using e-Tongue by several prediction models employed in the study.