05-02-2015, 06:50 AM
I want detail description regarding electronic tongue..can you please provide it..
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electronic toungue seminars report
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I want detail description regarding electronic tongue..can you please provide it..
04-04-2017, 01:00 PM
The electronic language is an instrument that measures and compares tastes. The chemical compounds responsible for taste are detected by the receptors of human taste, and the seven sensors of the electronic instruments detect the same dissolved organic and inorganic compounds. Like human receptors, each sensor has a different spectrum of reactions from the other. The information given by each sensor is complementary and the combination of all sensor results generates a unique fingerprint. Most sensor detection thresholds are similar or better than those of human receivers.
In the biological mechanism, taste signals are transduced by the nerves of the brain into electrical signals. The process of the tab sensors in E is similar: they generate electrical signals as potentiometric variations. The perception and recognition of taste quality is based on the construction or recognition of patterns of sensory nerves activated by the brain and the fingerprint of the product. This step is achieved by the statistical software of the electronic language that interprets the sensor data in taste patterns. A variation was developed by Professor Fredrik Winquist of the University of Linköping, Sweden.
A flavor sensor instrument (electronic language) was evaluated to determine its utility in the development of an improved taste liquid formulation. To form the electronic language, human sensory panel data were collected for two prototype formulations, a solution of the drug in water and various marketed products. We conducted studies using the electronic language to determine the effectiveness of flavor masking of formulations compared to a matching placebo, to correlate with human sensory data and to evaluate unknown formulations and to predict their bitterness scores.
In the first experiment, the effectiveness of a proposed flavor masking strategy was determined by comparing formulation prototypes containing a bitter active pharmaceutical ingredient (API) against corresponding placebos (ie, formulations without an active ingredient) using electronic language data . The analysis of the electronic language data was based on the assumption that the drug was well masked if the placebo matched the formulation with API. In a second set of experiments, electronic language data were compared to existing data from a human flavor panel for various marketed products and prototype formulations. A good correlation (r2 = 0.99) was obtained from this comparison, and the relative taste of the prototypic formulations not tested by humans was predicted.
In the biological mechanism, taste signals are transduced by the nerves of the brain into electrical signals. The process of the tab sensors in E is similar: they generate electrical signals as potentiometric variations. The perception and recognition of taste quality is based on the construction or recognition of patterns of sensory nerves activated by the brain and the fingerprint of the product. This step is achieved by the statistical software of the electronic language that interprets the sensor data in taste patterns. A variation was developed by Professor Fredrik Winquist of the University of Linköping, Sweden.
A flavor sensor instrument (electronic language) was evaluated to determine its utility in the development of an improved taste liquid formulation. To form the electronic language, human sensory panel data were collected for two prototype formulations, a solution of the drug in water and various marketed products. We conducted studies using the electronic language to determine the effectiveness of flavor masking of formulations compared to a matching placebo, to correlate with human sensory data and to evaluate unknown formulations and to predict their bitterness scores.
In the first experiment, the effectiveness of a proposed flavor masking strategy was determined by comparing formulation prototypes containing a bitter active pharmaceutical ingredient (API) against corresponding placebos (ie, formulations without an active ingredient) using electronic language data . The analysis of the electronic language data was based on the assumption that the drug was well masked if the placebo matched the formulation with API. In a second set of experiments, electronic language data were compared to existing data from a human flavor panel for various marketed products and prototype formulations. A good correlation (r2 = 0.99) was obtained from this comparison, and the relative taste of the prototypic formulations not tested by humans was predicted.
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