Taste Sensor: Electronic Tongue with Lipid Membranes

Abstract

Taste is of five basic types, namely, sourness, saltiness, sweetness, bitterness and umami. In this review, we focus on a potentiometric taste sensor that we developed and fabricated using lipid polymer membranes. The taste sensor can measure the taste perceived by humans and is called an electronic tongue with global selectivity, which is the property to discriminate taste qualities and quantify them without discriminating each chemical substance. This property is similar to the gustatory system; hence, the taste sensor is a type of biomimetic device. In this paper, we first explain the sensing mechanism of the taste sensor, its application to beer evaluation and the measurement mechanism. Second, results recently obtained are introduced; i.e., the application of the senor to high-potency sweeteners and the improvement of the bitterness sensor are explained. Last, quantification of the bitterness-masking effect of high-potency sweeteners is explained using a regression analysis based on both the outputs of bitterness and sweetness sensors. The taste sensor provides a biomimetic method different from conventional analytical methods.

Development of Taste Sensor to Detect Non-Charged Bitter Substances

“…Up to now, electronic tongues (e-tongues) and taste sensors are known as the devices which can qualify taste [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. A taste sensor equipped with sensor electrodes, the surface of which is partially pasted with lipid/polymer membranes, measures taste electrically, and it has a characteristic of the “global selectivity” [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The sensor with this property does not discriminate each substance but discriminate and quantify each taste quality, as really found in the taste sense of humans.…”

“…The sensor with this property does not discriminate each substance but discriminate and quantify each taste quality, as really found in the taste sense of humans. As a result, the taste sensor using lipid/polymer membranes can quantify the five basic tastes and astringent taste [ 1 , 2 , 5 , 6 ], and furthermore various kinds of foodstuffs such as coffee [ 14 ], tea [ 15 ], and beer [ 1 , 5 ], as well as pharmaceuticals [ 16 , 17 ]. It can also detect an inhibitory effect appearing in coexistent sweetness and bitterness [ 18 ] and a saltiness enhancement effect [ 19 ].…”

“…These sensors have high sensitivity and selectivity and can measure bitter substances. For example, BT0 can measure bitter substances of strong hydrophobic pharmaceutical products such as the quinine hydrochloride without responding to other taste qualities [ 1 , 2 , 5 , 6 ], and C00 can measure the acid bitter substances such as iso-α acid included in beer. However, the sensitivity of bitterness sensor BT0 and C00 for non-charged substances is too low, because the basic principle of the taste sensor is the membrane potential measurement mainly caused by change in the surface electric charge density accompanied by interactions with charged substances.…”

Correlation between skeletal muscle fiber type and responses of a taste sensing system in various beef samples

“…The taste sensor imitated the human tongue, and simulated the taste perception of living organisms using artificial lipids as transducers for the multichannel taste sensors. It consisted of six lipid membrane sensor probes that distinguished among sweetness (GL1), saltiness (CT0), sourness (CA0), umami (AAE), bitterness (C00), and astringency (AE1), thereby imitating the function of the human caliculus gustatorius cells, while taste was distinguished by the pattern recognition of the potential change (Wu et al., 2020). According to the INSENT manufacturer's manual, the sensor signals of a sample are different from those of the standard solution (30 mM KCl and 0.3 mM L‐(+)‐Tartaric Acid) and convert to the value corresponding to the individual tastes (Wu et al., 2020).…”

“…The correspondence between the sensor outputs and the tastes is shown in Table 2. In general, the converted value 1 corresponded to a 20% difference from the standard sample in the sensory evaluation (Wu et al., 2020).…”