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1.
Sensors (Basel) ; 15(9): 22439-50, 2015 Sep 04.
Article in English | MEDLINE | ID: mdl-26404301

ABSTRACT

It is possible to evaluate the taste of foods or medicines using a taste sensor. The taste sensor converts information on taste into an electrical signal using several lipid/polymer membranes. A lipid/polymer membrane for bitterness sensing can evaluate aftertaste after immersion in monosodium glutamate (MSG), which is called "preconditioning". However, we have not yet analyzed the change in the surface structure of the membrane as a result of preconditioning. Thus, we analyzed the change in the surface by performing contact angle and surface zeta potential measurements, Fourier transform infrared spectroscopy (FTIR), X-ray photon spectroscopy (XPS) and gas cluster ion beam time-of-flight secondary ion mass spectrometry (GCIB-TOF-SIMS). After preconditioning, the concentrations of MSG and tetradodecylammonium bromide (TDAB), contained in the lipid membrane were found to be higher in the surface region than in the bulk region. The effect of preconditioning was revealed by the above analysis methods.


Subject(s)
Lipids/chemistry , Membranes, Artificial , Polymers/chemistry , Taste , Amines/chemistry , Photoelectron Spectroscopy , Sodium Glutamate/chemistry , Spectrometry, Mass, Secondary Ion , Spectroscopy, Fourier Transform Infrared , Static Electricity , Water/chemistry
2.
Sensors (Basel) ; 10(4): 3411-43, 2010.
Article in English | MEDLINE | ID: mdl-22319306

ABSTRACT

Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets.


Subject(s)
Biosensing Techniques/methods , Fat Substitutes/analysis , Taste/physiology , Beverages , Humans , Multivariate Analysis , Neural Networks, Computer
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