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Chemical taste-sensing systems that are humanlike and intelligent can help the food industry maintain quality-specific tastes to satisfy consumer preferences.
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aste and smell are the two human senses that are chemical in nature, whereas hearing, touch, and sight are senses of a physical nature that have been partially replicated with sensors, such as microphones, thermometers, and CCDs. Taste and smell, however, have not been as successfully replicated with sensors, probably because of the complexity of the human system. Nevertheless, many attempts have been made to mimic the overall functionality of human taste, although not its structure and appearance. The goal is to try to transform a large number of chemical signals into a characteristic known as taste (1). In this article, we will discuss some approaches to artificial taste sensors and the philosophies behind them. We want to look at the role that taste sensors might play in the future for the food and beverage industry and for consumers. The purpose of this article is not to provide a general review (which can be found in Refs. 2–4). The terms “taste sensor” and “electronic tongue” are used here as synonyms to describe chemical sensor arrays com bined with a computerized data processing system for measure ments in liquid samples. However, the term “taste sensor” will also be defined more narrowly to mean a sensor array used to tar get the analysis of drinks and foodstuffs in terms of their taste.
© 2008 American Chemical Societ y
Daniel Citterio Koji Suzuki Keio University (Japan)
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The concept of taste as used here in this article relates to the gustatory perception of food and drink by the human or ganolept ic system. This is in contrast to other so-called taste sensors that have nothing to do with the concept of taste as it is perceived by a human being. Instead, they are general mul tianalyte sensing systems based on an array of chemical sensors in liquid samples.
Smart sensors
What is meant by a “smart sensor”? Does “smart” placed in front of “sensor” really imply a specific characteristic, or is it merely intended to catch the eye without having any more profound meaning? Finding a definition of a smart chemical sensor in the scientific literature is not easy, although the ex pression itself is used, especially in paper titles. In a standard English dictionary, “smart” is used to describe a device that is computer-controlled and therefore appears to act in an intel ligent way (5). In a 1993 paper, a toxic-vapor sensing system is described as “smart because it can discriminate among dif
tinuous quality monitoring. The time when naturally grown fruits, vegetables, and fish and meat products were processed by individuals or small commercial units to produce local varia tions of drinks, sauces, and soups has passed. One reason for these developments is the ever-increasing demand for such pro cessed food products, which can only be satisfied by industri al-scale production plants, often on a global scale. One conse quence of this globalization is that consumers get accustomed to a certain taste of a specific product and therefore expect the manufacturer to provide the product with identical quality and taste all year. Such foods will continue to be produced from naturally grown ingredients; however, seasonal differences in the sweet ness of a fruit or vegetable must be compensated for by food additives. The modern consumer seems not to accept natural taste variations, which our ancestors were very much used to. In addition, the overall increased health awareness, and in par ticular the concerns about the growing incidence of obesity, create further demands on the food and beverage industry. For example, sugars are increas ingly replaced by artificial sweeten ers to reduce calories consumed (7). Because of the foods movement, a return to more natural products might be expected, at least partially. At the same time, however, the call for more functional food products is increasing. For example, drinks that are promoted as improving performance in sports or increasing mental alertness are widely available (8). As food and beverage production becomes more global with high-tech manufacturing, the demand for analytical methods is also increasing. On one side, government regulations con cerning food additives require a large variety of precise chemi cal analyses; chromatographic methods are the most widely applied (9). On the other side, because the ultimate goal is to satisfy the consumer, adaptation to human taste is extremely important. Panels of professional tasters are found in every large industrial food and beverage processing company. Ap plying an artificial taste sensor instead of, or in support of, the work of human panelists is definitely an advantage. The taste sensor is not prone to moodiness and does not tire of tasting the same samples over and over again. Although routine qual ity control (QC) of known food samples without human panel ists might be feasible with commercialized taste sensors, more challenging tasks, such as designing a new energy drink or adapting the taste of a globally manufactured product to spe cific regional consumer preferences, are probably still beyond the limits of most artificial taste sensors.
Although the science behind taste is still not fully elucidated, we know that it relies on a series of taste cells densely packed in taste buds. ferent classes of vapors, it can identify more than one class of vapor, and it provides answers about the presence or absence of a hazard rather than simply providing raw sensor data” (6). Nowadays, though, all chemical sensing systems are connected to a computer for processing and are not limited to the output of raw data. In everyday language, “smart” is synonymous with “intel ligent” when describing a specific characteristic of a human. Therefore, the same adjective applied to modern chemical sens ing systems should imply a characteristic going beyond simple computer control and should describe a touch of humanlike intelligence in the sensing system. The more interesting aspect of the smartness definition for the toxic-vapor sensing system is that the smart sensor provides answers about the presence or absence of a hazard. This is a task that would otherwise be performed by a human being. Accordingly, for a taste sensor to be smart, mere computer control of the sensor is not enough. The computer control is expected to result in a taste sensor that is, to a certain degree, capable of providing the type of information about the system being interrogated that a human would. The output of the smart taste sensor has to go beyond the chemical parameters actually sensed. Therefore, the term “smart taste sensor” in the context of this article is understood to mean a taste sensor that is capable of giving answers to questions that otherwise only a human could provide.
What are taste sensors needed for?
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Approaches to taste sensors
Although the science behind taste is still not fully elucidated, we know that it relies on a series of taste cells densely packed in taste buds mainly located in the tongue, palate, and pharynx. These taste buds are connected to nerve fibers that carry the signals of the chemical environment to the brain stem, where central processing of the information occurs (10). It is nowa
days commonly accepted that hu Data processing Raw data from mans distinguish at least five basic Analytical task sensor array taste qualities of saltiness, sour Gained information ness, bitterness, sweetness, and u mam i, although some consider PCA, ANN ANN, PLS, LRG Basic taste sensor this model too simplified (11). A variety of receptor proteins Similarity of Concentration of Classification, Quantification chemical selected compounds, for the five basic taste qualities discrimination composition recipe have been identified (10). Rat taste cells were used to confirm that single taste cells do not respond to Human knowledge HKB (e.g., ANN) just one of the basic taste stimuli. input required Network inversion Although such cells were found, others responded to two, three, or all of the basic tastes when stimu lated with a sweet, salty, sour, or Similarity of taste, Taste description actual taste, bitter compound (12). The “elec Smart taste sensor (quality, intensity) gustatory illusions tronic” counterpart is an array of chemical sensors, in which raw signal output is transferred to a FIGURE 1. Schematic of the most often applied data processing methods, which lead to the distinccomputer system and the data tion between basic and smart taste sensors. processed. As an analogy to na PLS, partial least squares regression; LRG, linear regression; ANN, artificial neural network; PCA, principal ture, it is generally assumed that component analysis; and HKB, human knowledge base. taste sensors have to rely on sen sors with rather broad or relatively low chemical selectivity. example, different types of coffee or differently aged orange This philosophy is also supported by IUPAC in a technical re juices. Although selective sensors are often included in its ar port on electronic tongues (3). However, taste sensors based rays, the group has particularly focused on nonspecific, broadly on sensor arrays with high selectivity have proven to be use selective potentiometric sensors. ful as well. Some groups focused on quantitative analyses of mineral The majority of taste-sensing systems for food and beverage waters and wines (23, 24). However, the quantitative informa analyses rely on arrays of electrochemical sensors (4). Among tion gained is not directly related to human taste perception, those, potentiometric electrodes are by far the largest group of and no relationship between the concentration of a certain sensors. Toko and co-workers were among the first to use po chemical component and the taste is provided. The correla tentiometric sensors for taste (13–17). The sensing chemistry is tion of results obtained from the taste-sensing system with based on a series of different lipids carrying negative charges, those from human panelists was investigated with Italian wine positive charges, or weakly negative charges immobilized in samples (25). The perceived flavor parameters are characteris PVC membranes. A miniaturized version in the form of a taste tic for wines but were not investigated for their chemical com field-effect transistor was also investigated (18). The sensors do position. The researchers have also demonstrated a combined not distinguish each chemical substance in the sample but are application of their taste sensor with an odor sensor for the used to recognize sour, salty, sweet, bitter, and umami, as well quantitative analysis of certain components in Italian wines as taste intensity. The array gives similar response patterns for (26). The response of the taste sensor to chemical compounds samples belonging to the same basic taste group. that are bitter, sweet, and salty was investigated, and success To compensate for the rather low sensitivity toward sweet ful discrimination among the substances of these three tastes substances, which are generally electrically neutral, the taste- was shown (27). sensing array has been combined with a glucose-selective en Another group using an array of ion-selective electrodes zyme electrode (19). Recent work has focused directly on im (including a pH electrode) for taste sensing is that of Ciosek proving sensitivity for sweetness (20). A combined application and Wroblewski (28). They worked with sensor arrays that with an odor sensor has been demonstrated to discriminate dif combined broadly selective and ion-selective electrodes to dis ferently aged samples of the same red wine (21). The research criminate milk, orange juices, and tonic waters (29). Recently, team of Vlasov and Legin, in collaboration with the groups of they compared the classification abilities of taste-sensing sys D’Amico and Di Natale, was the first to use potentiometric tems made either from only broadly selective electrodes or sensor arrays comprising both selective solid-state electrodes from only ion-selective electrodes with a system combining the and plasticized organic membranes as well as broadly selective two types of sensors (30). The best classification results were chalcogenide glass electrodes (22). This system proved useful produced when both types of sensors were incorporated into in situations most likely encountered in industrial QC for dis the system. tinguishing among versions of the same type of beverage, for The research groups of Krantz-Rülcker, Lundström, and J u n e 1 , 2 0 0 8 / A n a ly t i c a l C h e m i s t r y
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FIGURE 2. Bottled water. (a) Example of a plot of the first three principal components for the discrimination of different mineral waters by a basic taste sensor. The arrows demonstrate a deviation from the standard, in this case simulated by the contamination (c in front of a product name) of the original water with organic matter. (Adapted with permission from Ref. 24.) (b) Because the principal components have no chemical meaning (concentration), no information about the concrete differences between samples is obtained.
Winquist make use of different voltammetry techniques (e.g., cyclic, stripping, and pulsed) for taste-sensing purposes. An ad vantage of this approach is the possibility of obtaining various aspects of information about the sample. The use of different working electrodes allows for further variations in the system. The aging of orange juice and milk was successfully monitored, and drinks were classified with two types of working electrodes (31). A voltammetric taste sensor with six working electrodes was also applied in combination with an odor sensor, resulting in improved beverage classification (32). In another approach, the researchers combined voltammetric, potentiometric, and conductivity sensors in a hybrid taste sensor and classified fer mented milk samples (33). 3968
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Suslick and co-workers have introduced a simple yet very in teresting taste-sensing system that relies on optical detection. They immobilized a series of functional dyes (solvatochromic dyes, pH indicators, and metalloporphyrins) onto a hydropho bic support; this system was used on gaseous analytes (34) and later applied as a taste sensor in liquid samples (35–37). The ability to be used in liquid and gaseous systems is a significant advantage of the system. Color changes of the dye spots before and after exposure to the sample are monitored with a conven tional flatbed scanner. Different types and different brands of soft-drink samples were clearly discriminated; the system could also distinguish between diluted and decarbonated drinks, which suggests that it could be used for QC. So far, no corre lation with the actual human perception of taste of the inves tigated samples and no information concerning the chemical composition of the samples have been obtained. The taste sensor described by our research group is to the best of our knowledge the only system relying solely on an ar ray of highly selective potentiometric and amperometric sen sors. It provides quantitative information about the sample composition and quantitative taste values based on the five basic tastes (38). In contrast to the majority of the sensor ar rays mentioned, the single sensors used in our approach are well-known chemical or biochemical sensors. In particular, we know which species these sensors are responding to and what kind of potential interferences are found in real samples. We combined ion-selective potentiometric electrodes (for Na+, K+, Cl−, and H+) with selective amperometric enzyme electrodes (for glucose, sucrose, and glutamate) and a voltammetric elec trode operating in a specific potential window for the selective detection of bitterness caused by caffeine (39). Several approaches have been discussed for the transduc tion mechanism and chemical selectivity. However, neither the chemical transduction scheme nor the choice of selective or broadly selective sensors accounts for the smartness of a taste sensor. Smartness is determined by the data processing meth ods applied to the raw data (Figure 1). For a review of compu tational data processing methods, consult Ref. 40.
Basic taste sensors
The most commonly described taste sensors are broadly selec tive arrays of potentiometric chemical sensors coupled to a data processing system for principal component analysis (PCA). Such sensors provide information about the similarity of sam ples, in terms of chemical composition of ionic species (Figure 1). They do not provide any information about the taste of the sample as a human being would perceive it, and therefore they do not fall under our definition of a smart taste sensor. We re fer to these kinds of systems as basic taste sensors. The purpose of PCA is to reduce the multidimensional data obtained from all the sensors in an array to two or three principal compo nents while retaining the largest amount of information (vari ance) possible. In the resulting PCA plots, samples with similar chemical properties form groups, which in the ideal case are clearly distinguishable from other samples (Figure 2a). The basic taste sensors are part icularly useful for the QC of
known food and beverage prod ucts in routine production to de Substance, Sensor Taste tect small differences in chemical concentration composition, for example, due to contamination. The sensor can be set to raise an alarm when a sam RBFN1 ple exceeds a predefined threshold + Na+ Na N ISE Na value set by a standard. However, RBFN2 the alarm only indicates a chemical composition different from that of K+ ISE K+ the standard, without providing any RBFN3 hint about the taste quality (Figure Alkaline ion water Orange juice O 2b) or any information about the Cl – ISE Cl – Cl specific chemical compound caus HKB RBFN4 ing that difference. Evaluation by a + electrode pH human taster and further chemical H Saltiness Salt Sa a in ines ess analysis will be required because 5 TTrain Tra Testt the principal components have no 1 chemical or physical meaning per H+ RBFN8 Sour So uurness urne rness ss Bitterness Sourness Bitterne Bit B Bi iittttte nneesss se, and they are normally not related Sucrose Sucr cros cr ose os RBFN9 RBFN5 to the actual taste of the sample as a en y Sucrosee enzyme human taster would perceive it. In electrode the example of the mineral water Umami Sw S we w eetness ettnesss Umami Sweetness Gluccose os Glucose samples shown in Figure 2a, the ar en y Glucosee enzyme rows demonstrate a deviation from electrode the standard, in this case simulated by the contamination of the origi H+ RBFN6 nal water with organic matter (24). All that the basic taste sensor indi Glut Glut utam amat am atee at Glutamate cates is the deviation itself, not its utamate enzyme Glutamate origin. electrode Coffee (no sugar) Japanese miso soup Whether a sensor array is com RBFN7 posed of broadly selective or se Caff fffei eine Caffeine tammetric caffeine caffein Voltammetric lective sensors, the basic type of sensor taste sensor can also be used to Phase 1 obtain quantitative information in Phase 2 terms of the concentrations of vari ous chemical components (Figure 1). For this purpose, partial least FIGURE 3. Schematic of the smart chemical taste sensor. squares (PLS) regression or artifi The sensor array consists of eight highly selective electrochemical sensors. Data processing occurs by opcial neural networks (ANNs) are timized RBFNs in two phases. The first phase correlates the electrode signals to the concentrations of the normally applied (23). In the case eight major taste-causing substances (RBFN1–7). The second phase, relying on HKB input, correlates the of selective sensors, simpler calibra substance concentrations with the intensities (RBFN8) and standard deviations (RBFN9) for the five basic tastes. The radar plots show the taste sensor’s response to four real samples; the pink shaded area indition procedures (linear regression) cates the standard deviations of the taste predictions. (ISE, ion-selective electrode; adapted from Ref. 38.) might be sufficient. But no matter how many chemical substances are quantified in a specific sam sible to apply human-knowledge-base (HKB) data processing ple, there will not be any information about the actual taste of methods (43). A very simple approach by Toko and co-workers was applied to beer (16). When the positions of samples in a that sample as it is perceived by a human being. PCA plot were correlated with observations from human pan Human-knowledge-base data processing elists, it became possible to attribute principal components, So far, there are no absolute models that correlate the taste that which by themselves have no physical meaning, to certain hu a human perceives with the chemical composition of a sample. man taste perceptions specific for beer. Given that taste perception is just one part in the big puzzle of In general, the correlation of signals for a specific sample complex human senses, such models will not be available soon, obtained from an array of chemical sensors with the taste char and may never be (41, 42). Although intelligence in the context acteristics of the same sample perceived by humans requires a of a sensor is not comparable with human intelligence, it is pos “supervised” data processing method. In a supervised method, J u n e 1 , 2 0 0 8 / A n a ly t i c a l C h e m i s t r y
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FIGURE 4. One taste affects another. Gustatory illusions predicted by a smart chemical taste sensor of (a) the suppression of bitterness by sweetness in coffee and (b) the enhancement of umami by saltiness in Japanese miso soup. (Adapted from Ref. 38.)
the system is forced to assign each piece of independently ob tained information (sensor response) to a specified dependent piece of information (basic taste intensity) relying on a priori information from an HKB (40, 44). In this way, it is possible to combine human intelligence (in the form of a taste perception database) with the data obtained from a sensor array, result ing in a smart chemical taste sensor. The different variations of ANNs are by far the most useful data processing techniques for such purposes, because they do not require a mathematical model to describe the relationship between chemical substance concentrations and taste perceived by humans. No such model exists yet. In our smart taste-sensing system, we applied opti mized radial basis function networks (RBFNs) for processing sensor data (38).
Selective or broadly selective sensors?
In principle, all of the electrochemical and optical chemical sensor arrays presented here are suitable for combination with HKB ANNs data processing, as long as the information ob tained from the single sensors is sufficiently diversified. The required diversification can be achieved by the use of broadly selective sensors. However, it can also be achieved with selec tive sensors, but only when the selective sensors cover a range of analytes that is broad enough to provide data about the es sential chemical compounds responsible for all five tastes. This is an essential characteristic of our smart taste sensor based on chemically selective sensors. How does our approach compare with others that rely on broadly selective sensors? Most researchers agree that systems based on broadly selective sensors are more closely related to the systems found in nature. The human tongue does not have a selective sensor for every possible individual taste-causing substance but rather a limited number of cross-selective recep tors. However, these systems are generally not able to identify and quantify the taste-causing substances. On the other hand, taste-sensing systems based on selective sensors are limited in terms of possible taste-causing substances they can detect. A 3970
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sweetness-recognizing sensor ensemble detecting glucose and sucrose will find nothing “sweet” in a soft drink artificially sweetened with sucralose, unless a sensor selective for the spe cific artificial sweetener is added to the array. However, if a sample is recognized as being sweet, there is an almost 100% certainty that this sample really tastes sweet because of the presence of glucose or sucrose. The sweetness-causing sub stances are not only identified but can also at the same time be precisely quantified, because the chemical parameters actually measured are exactly known. Therefore, such a taste-sensing system is less prone to false positives, which could, for example, cause an apparent sweet taste in a nonsweet sample.
A qualitative and quantitative smart taste sensor that relies on selective sensors
With our chemically selective sensors, we can quantify the concentrations of eight major taste-causing substances—Na+, Cl−, K+, H+, sucrose, glucose, monosodium glutamate, and caf feine. Sensors for the determination of the artificial sweetener aspartame and the bitterness caused by quinine are currently being evaluated. The quantification occurs in the first phase of the two-phase RBFN data processing, where sensor signals are correlated to the specific concentrations (Figure 3). How ever, even if selective sensors for all taste-causing substances in a sample were available, this would not be sufficient to de termine the concentrations of the single compounds. Even with such chemical information available, it is still not possible to predict the taste, because there is no known mathemati cal model and because interactions among taste-causing sub stances can lead to suppression effects (45). It seems impossible that a taste-sensing system would be smart enough to sense what are known as “gustatory illusions”. For example, when sugar is added to a cup of black coffee, not only do humans taste an increase in sweetness, they also taste a decrease in the bitterness, although the concentration of caf feine remains unchanged. In an experiment in which increas ing amounts of sucrose are added to black coffee, the sucrose-
responsive enzyme electrode in our taste sensor will detect higher concentrations of sucrose, whereas the caffeine-detect ing electron cyclotron resonance-sputtered carbon electrode does not register any changes. However, here is where the smartness relying on the HKB comes in. When the second-phase RBFN of the two-phase ANNs approach that relies on the taste perception of human panelists is trained (Figure 3), the system learns how to attri bute a given combination of chemical concentrations of tastecausing substances to the intensities of the five basic tastes (RBFN8). For example, although salty taste is caused by the presence of NaCl and KCl, the system does not quantify salti ness on the basis of the sensors for Na+, K+, and Cl− alone. Rather, an overall approach is chosen, in which the concentra tions of all measured taste-causing substances are correlated with the taste intensities of all five basic taste qualities. Once trained in such a way, the system can recognize, for
Correlating taste and concentration
A special characteristic of our smart taste sensor lies in its twophase neural-network data processing structure. In principle, the output of the sensors could be directly correlated to the five basic taste qualities, instead of quantifying concentrations in a first phase and then correlating them to taste perception in the second phase, although doing so results in significantly larger relative errors of taste prediction (38). What are other advantages of obtaining quantitative information about key taste-causing substances? Quantifying the eight major tastecausing chemical compounds is not sufficient to provide a complete recipe of a measured sample. However, there are situations in which a simple recipe might be helpful to devel opers and manufacturers, for example, when compensating for seasonal sweetness variations in fruits and vegetables. Only the combination of an HKB data processing method with quan titative concentration data provides information to determine which ingredient to add or reduce in what amount to reach the desired taste of a standard. Finally, our smart sensor in combina tion with the neural-network inversion technique allows a user to produce a desired taste or recipe without actually performing experiments (47). The inversion technique searches for the combination of taste-causing substances that results in the desired taste pattern. Depending on the HKB applied, a different recipe could be obtained according to gender, age, or nationality.
Differences in taste perception are attributed mostly to age and gender, but geography may play a role because of a variety of nutritional traditions and local tastes. example, certain concentration combinations of increased su crose and constant caffeine as being less bitter than a sample with identical caffeine but lower sucrose (Figure 4a). When this knowledge-base “human factor” is introduced into the data processing, the sensor array can react in a smart, humanlike way. Figure 4b illustrates a second gustatory illusion. When salt was added to a Japanese miso soup sample, the smart taste sensor correctly indicated increased saltiness together with in creased umami, although the concentration of umami-causing sodium glutamate remained unmodified.
Simulating variations in human taste perception
The differences in human taste perception are attributed mostly to differences in age and gender (46). However, geogra phy may play a role because of a variety of nutritional traditions and local tastes. Our smart taste sensor accounts for such per ception differences by providing intensity values for the five ba sic tastes and the corresponding standard deviations (RBFN9; Figure 3). This allows for simulation of how different people sense taste and includes the intensity margins within which the taste of a sample is likely to be perceived. This feature could be attractive to the global food and bev erage industry. When different HKBs obtained with tasters of varying ages, genders, and nationalities are used, the smart taste sensor can indicate how and within which margins these different groups of people will perceive the taste of a sample. Manufacturers could more easily adapt their products to spe cific consumers, especially during the development phase of a new product. Chemical taste sensors are definitely advanta geous to food and beverage manufacturers in terms of cost compared with using groups of human panelists.
Outlook
Regardless of sensing technology and the signal-transduction mechanism applied, the developers of even smarter chemical taste sensors should consider a combined approach of broadly selective and selective chemical sensors. Because the human perception of taste is influenced by the perception of odor, smart chemical taste sensors should be combined with smart chemical odor sensors. As recently summarized by Shepherd, there is more to the enjoyment of food than taste and smell (42). The integration of different smart sensors, not just those of a chemical nature, will further improve the performance of the artificial senses. In 1999, Winquist and co-workers presented an “artificial mouth” that combined odor sensing with auditory (microphone) and tactile (force) sensors to distinguish among crispbread samples (48). It will be necessary to include sensors for visual signals to detect the color and shape of food. The perceptions of pain (spicy food) and astringency (dry wine) also strongly contrib ute to the overall impression perceived by consumers. Finally, we hope that such combined smart artificial humanlike sensing systems will be applied to benefit consumers and not to fool the human senses. We thank all who contributed to the development of our taste-sensing system, in particular S. Ishihara, A. Ikeda, K. Maruyama, M. Hagiwara, C. Suga, R. Suzuki (all from Keio University), and H. Yamazaki (Techno J u n e 1 , 2 0 0 8 / A n a ly t i c a l C h e m i s t r y
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Medica). We kindly acknowledge A. Ueda (Tokyo Institute of Technology) and Y. Kobayashi (Keio University) for their creative support in the preparation of the figures. Daniel Citterio is an associate professor and Koji Suzuki is a professor at Keio University (Japan). Citterio’s research focuses on the development and application of multianalyte sensing systems based on electrochemical and optical sensing schemes. Suzuki’s research focuses on the development and application of new analytical and bioanalytical sensors. Address correspondence about this article to Suzuki at Department of Applied Chemistry, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan (
[email protected]).
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