Taste sensors

In order to better describe the utilization purposes, some authors proposed a distinction between electronic tongues and taste sensors the former term should have a wider meaning, embracing all the possible applications, while the latter should exclusively refer to sensory-like evaluations. 

The first utilization of the term artificial tongue dates to 1978, when H.W. Harper and M. Rossetto presented an apparatus based on conductance measurements able to mimic the taste stimulus delivery systems (Harper and Rossetto, 1978). This pioneering work has represented an isolated study for several years. The first example of a factual taste sensor was developed, in fact, by Toko and coworkers in 1990 (Hayashi et ah, 1990 Toko et ah, 1990). It was based on ion-sensitive lipid membranes and it was claimed to be able to respond to the basic tastes of the human tongue sour, sweet, bitter, salt, and umami. 

Hayashi, K., Yamanaka, M., Toko, K., and Yamafuji, K. (1990). Multichannel taste sensor using lipid membranes. Sens. Actmtors B 2, 205-213. 

Ivarsson, P., Kikkawa, Y., Winquist, F., Krantz-Ruelcker, C., Hoejer, N.-E., Hayashi, K., Toko, K., and Lundstroem, I. (2001). A comparison of a voltammetric electronic tongue and a lipid membrane taste sensor with respect to separation of detergent. Chem. Sens. 17 (Suppl. B), 101-103. 

Krishna Kumar, P. T. (2006). Design of a discriminating taste sensor using mutual information. Sens. Actuators B 119(1), 215-219. 

Riul, A., Malmegrim, R. R., Fonseca, F. J., and Mattoso, L. H. C. (2003b). An artificial taste sensor based on conducting polymers. Biosens. Bioelectron. 18(11), 1365-1369. 

Use of bilayer lipid membranes as a generic electrochemical transducer is an exciting future for food biosensors. A taste sensor with multichanneled lipid membrane electrode was recently developed (93). The electric patterns generated from the sensor are similar to human response. The sensor can distinguish different brands of beer. More details on the taste sensor can be found in Chapter 16 of this book. 

Development of this sensor was based on a concept very different from that of conventional chemical sensors, which selectively detect specific chemical substances such as glucose or urea. However, taste cannot be measured in those terms even if all the chemical substances contained in foodstuffs are measured. Humans do not distinguish each chemical substance, but express the taste in itself the relationship between chemical substances and taste is not clear. It is also not practical to arrange so many chemical sensors with respect to the number of chemical substances, which amounts to over 1000 in one kind of foodstuff. Moreover, there exist interactions between taste substances, such as the synergistic effect or the suppression effect. A taste sensor should measure these effects the intention is not to measure the amount of each chemical substance but to measure the taste itself, and to express it quantitatively. The recently developed sensor satisfies this request. In fact, this sensor could detect the interactions between saltiness and sourness. 

We here mention the principle of the taste sensor and applications to aqueous solution constructed of five basic taste substances and several foodstuffs such as beer, coffee and tomatoes. Quantification of the taste is possible using such a taste sensor, and hence we can discuss the taste objectively. 

One method to realize the taste sensor may be the utilization of similar materials to biological systems as the transducer. The biological membrane is composed of proteins and lipids. Proteins are main receptors of taste substances. Especially for sour, salty, or bitter substances, the lipid-membrane part is also suggested to be the receptor site [6]. In biological taste reception, taste stimulus changes the receptor potentials of taste cells, which have various characteristics in reception [7,8]. Then the pattern constructed of receptor potentials is translated into the excitation pattern in taste neurons (across-fiber-pattem theory). 

In the present study, therefore, lipid membranes were used as transducers of taste information. Artificial lipid materials, such as dioleyl phosphate (DOPH) or dioctadecyl-dimethyl-ammonium, were used to construct a lipid membrane and responses of electrical potential and resistance of the membranes were measured [9-15]. It was confirmed that the lipid membranes could discriminate five primary taste substances. Moreover, they could detect the interactions between taste substances observed in biological systems. The response properties were different in different types of lipids. If a hydrophobic part of a lipid was different, taste substances which can be detected were different. These facts indicate that the taste sensor can be realized by the use of various kinds of lipid membranes as transducers. 

The DOPH membrane and the ammonium salt membrane responded to taste substances in different ways. The above results suggest that taste substances can be perceived satisfactorily using various kinds of lipid materials. Furthermore, we must improve the sensing reproducibility. As a second step, we have developed a multichannel lipid membrane taste sensor. Taste substances can be discriminated by the output pattern from several lipid membranes. 

The pattern of each taste substance is different, and hence each taste substance can be easily discriminated The reproducibility is very high, because the standard deviations are smaller than 1 % or so. The taste sensor shows similar response patterns to the same group of taste. As examples of sour substances, HC1, citric acid and acetic acid show similar response patterns. Bitter substances such as quinine, MgS04 and phenylthiourea show similar patterns. 

Therefore, we can conclude that this taste sensor can respond to the taste in itself. This fact is very important. We must measure the taste (not chemical substances), because we want to develop the taste sensor. 

Taste of amino acids was studied using the taste sensor [23]. Taste of amino acids has had the large attention so far because each of them elicits complicated mixed taste itself, e.g., L-valine produces sweet and bitter tastes at the same time. Thus, there exist detailed data on taste intensity and taste quality of various amino acids by sensory panel tests [26]. The response of the sensor to amino acids was compared with the results of the panel tests, and response potentials from the eight membranes were transformed into five basic tastes by multiple linear regression. This expression of five basic tastes reproduced human taste sensation very well. 

Amino acids are generally classified into several groups which correspond to each characteristic taste. Since amino acids show mixed tastes as above, the taste interactions such as synergistic effect and repression effect are automatically included in their taste. The taste sensor should express this situation. The present study is the first trial to study the taste of amino acids using artificial sensing devices. 

Figure 9(b) shows the data points plotted in the scattering diagram on PC 1 and PC 2 by the principal component analysis. The first principal axis reflects bitterness and sweetness. The second principal axis reflects sourness and umami. Amino acids are classified clearly into five groups by the taste sensor. 

Transducer materials of L and D forms of chiral lipids are also necessary for further development of the taste sensor. Development of the taste sensor may contribute to the study of reception mechanisms of taste sensing. 

We attempted to make an artificial taste solution, which shows a similar taste to some commercial aqueous drinks, by combination of basic taste substances by comparing electrical potential patterns of the taste sensor [22],... 

The above-mentioned 256 mixed solutions were measured with the multichannel taste sensor. Therefore, data on the output electrical potential pattern were taken for the 256 solutions. While the data on each channel output were dispersed discretely in the four-dimensional space constructed from four different concentrations, we approximated them by a quadratic function of the concentrations. As a result, eight quadratic functions were obtained. The data can be regarded as expressed by a set of eight different functions (corresponding to 8 channels) of concentrations of four taste substances. 

The taste of every drink can be quantified using the multichannel taste sensor. In other words, quantitative measurements of taste are possible by the sensor more accurately than the sense of humans because the present taste sensor has higher reproducibility, durability and sensitivity. 

Now let us discuss the sensitivity of the taste sensor. The sensor had detection errors ( in the unit of logarithmic concentration) 0.73% for saltiness, 0.65% for sourness and 3.4% for bitterness in the mixed aqueous solution [27]. Humans usually cannot distinguish two tastes with a concentration difference below 20% [4], Here, 20% means the error of 7.9% (=log 1.2). Therefore, ability of detection of the sensor is superior to that of... 

The present sensor could easily discriminate between some kinds of commercial drinks such as coffee, beer and aqueous ionic drinks (Figure 11) [22], Since the standard deviations were 2 mV at maximum in this experimental condition, these three output patterns are definitely different. If the data are accumulated in the computer, any food can be easily discriminated. Furthermore, the taste quality can also be described quantitatively by the method mentioned below. In biological systems, patterns of frequency of nerve excitation may be fed into the brain, and then foods are distinguished and their tastes are recognized [4-8]. Thus, the quality control of foods becomes possible using the taste sensor, which has a mechanism of information processing similar to biological systems. 

The direct transformation from the output pattern to the taste quality was performed here as one trial of expressing the actual human sensation using the output electrical pattern. A similar trial was done for evaluation of the strengths of sourness and saltiness, which will be mentioned later. These two trials depend on the utilization of simple transformation equations by extracting typical properties of output patterns. This method is effective if some data on sensory tests, using humans as a standard, can be obtained to compare with the sensor outputs. However, the expressions for the tastes of beer are obscure because they are not described by the five basic taste qualities. The purpose of the application of the taste sensor is also to express these kinds of obscure terms of human sense in scientific terms. 

For quantification of the taste of tomatoes, the taste sensor was applied to commercial canned tomato juice, to which four basic taste substances had been added. Data were analyzed by means of principal component analysis. The taste of several brands of tomatoes was expressed in terms of four basic taste qualities by projecting the data obtained from these tomatoes onto the principal axes. This expression agreed with the human taste sensation. 

The taste sensor has the sensitivity, reproducibility and durability higher than those of humans. The taste quality was quantified and the taste interactions were reproduced. 

The taste sensor will be applicable for quality control in food industry and help automation of the production. The sense of taste is vague and largely depends on subjective factors of human feelings. If we compare the standard index measured by means of the taste sensor with the sensory evaluation, we will be able to assess taste objectively. Moreover, the mechanism of information processing of taste in the brain as well as the reception at taste cells will also be clarified by developing a taste sensor which has output similar to that of the biological gustatory system. 

Y. Sasaki, Y. Kanai, H. Uchida and T. Katsube, Highly sensitive taste sensor with a new differential LAPS method, Sens. Actuators B Chem., 25(1-3) (1995) 819-822. 

K. Toko, A taste sensor. In S. Alegret (Ed.), Integrated Analytical Systems, Comprehensive Analytical Chemistry Series, Vol. 39, Elsevier, Amsterdam, 2003, pp. 487-511. 

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