Chemical Sensing at the Robot Fingertips: Toward Automated Taste

Sep 18, 2018 - The development of robotic sensors that mimic the human sensing capabilities is critical for the interaction and cognitive abilities of...
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Chemical Sensing at the Robot Fingertips: Toward Automated Taste Discrimination in Food Samples Bianca Ciui,†,‡,# Aida Martin,†,# Rupesh K. Mishra,†,# Tatsuo Nakagawa,† Thomas J. Dawkins,† Mengjia Lyu,† Cecilia Cristea,‡ Robert Sandulescu,‡ and Joseph Wang*,† †

Department of Nanoengineering, University of California, San Diego, La Jolla, California 92093, United States Analytical Chemistry Department, UMF Cluj, Napoca 400349, Romania

‡

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S Supporting Information *

ABSTRACT: The development of robotic sensors that mimic the human sensing capabilities is critical for the interaction and cognitive abilities of modern robots. Though robotic skin with embedded pressure or temperature sensors has received recent attention, robotic chemical sensors have long been unnoticed due to the challenges associated with realizing chemical sensing modalities on robotic platforms. For realizing such chemically sensitive robotic skin, we exploit here the recent advances in wearable chemical sensor technology and flexible electronics, and describe chemical sensing robotic fingers for rapid screening of food flavors and additives. The stretchable taste-sensing finger electrochemical devices are printed on the robotic glove, which simulates the soft skin, and are integrated with a wireless electronic board for real-time data transmission. The printed middle, index, and ring robotic fingers allow accurate discrimination between sweetness, sourness, and spiciness, via direct electrochemical detection of glucose, ascorbic acid, and capsaicin. The sweet-sensing ability has been coupled with a caffeinesensing robotic finger for rapid screening of the presence of sugar and caffeine in common beverages. The “sense of taste” chemically sensitive robotic technology thus enables accurate discrimination between different flavors, as was illustrated in numerous tests involving a wide range of liquid and solid food samples. Such realization of advanced wearable taste-sensing systems at the robot fingertips should pave the way to automated chemical sensing machinery, facilitating robotic decision for practical food assistance applications, with broad implications to a wide range of robotic sensing applications. KEYWORDS: robotics, flavor sensing, sweetness, sourness, spiciness, screen-printed electrodes umanoid robots, introduced as “mechanical knights” by Leonardo da Vinci in 1495 A.D., will eventually work alongside humans if they understand human intelligence, reasoning, and simulate the human behavior.1 Currently, there is tremendous interest in developing human-like robots which function in a safe and efficient manner and which are “aware” of their surroundings.2−4 Therefore, emerging industrial and assistive personal robots, artificial intelligence, and machine learning are advancing at a rapid pace. In general, robots and automation technologies have been developed for repetitive tasks, to decrease the workforce, to substitute people in hazardous or inaccessible environments, and to complete various assignments. 5−7 The recent trend in modern autonomous robots is to coexist with humans (e.g., elderly), share living and working environments with humans, and perform diverse tasks, while adapting to their surroundings and reacting to unexpected events.8−10 Thus, most industries and fields use reliable mechatronic machines, including the agricultural, food, automotive, and domestic industries. Additionally, with increasing sophistication of ingenious robotization systems, research has also been oriented to explore new robotic applications in military or medical fields.4,8−11 The human hand is one of the most important parts in the body, as

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it can execute complex operations.5 Hence, robotic hands should be able to perform similar tasks as humans in real time. Robotic hand applications have been based primarily on tactile pressure and other physical sensors devoted to dexterous manipulation,12 fingerprint recognition,13 or grasping objects.5,14 In all these cases, these physical sensors were integrated on the materials which the robot is made of. However, other modern approaches rely on the incorporation of sensory systems onto external soft and elastomeric materials, inspired by biological skin, which smoothly conform on the robotic body.15 These assistive technologies, such as soft robotic gloves embodied with sensors, have been designed and applied to identify vegetables, to help in the rehabilitation for individuals with functional grasp pathologies,9 for wirelessly translating the American Sign Language alphabet into text,16 or for gesture recognition.17 Covering the surface of a robot’s body with feedback physical sensors, based on e-skin, proved to be attractive for numerous applications. In contrast, little Received: August 3, 2018 Accepted: September 18, 2018 Published: September 18, 2018 A

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Figure 1. Robotic Sense fingers: schematics, fabrication, and analytical performance. (A) Schematics of robotic finger immersed in lemon juice, coffee, and chili powder placed on the table, for subsequent detection of sourness, caffeine, and spiciness, respectively. (B) Image illustrating the robotic hand used to develop the glove based sense fingers. (C) Prototype of the screen-printed robotic sense fingers with long connections to the wrist where the electronic interface is located. Robotic fingers are identified with colored rings: green, for carbon-printed sour-finger; blue, for GOx Prussian blue-printed sweet-finger; and red, for carbon-printed spicy-finger. Scale bar, 5 cm. (D) Images and (E) corresponding electrochemical data of (i) robotic sour-finger dipped in orange juice and the SWV signature of ascorbic acid, (ii) robotic sweet-finger in cherry juice and amperometry data of glucose, (iii) spicy-finger on green-pepper and SWV feedback response to the presence of capsaicin. Dotted lines correspond to the blank PBS response.

expand the sensing ability of robots to chemical sensory modalities. Of particular interest are food screening and safety applications involving the tasting of ingredients found in food and drinks in connection to food preparation and consumption.23−25 Although limited efforts have been made for developing an electronic tongue for food screening, such sensor arrays present only general digital signatures, and are not selective toward detection of food components.26−29 However, robotics interfaced with chemical sensors, capable of providing feedback related to food flavors, might find broad applications in both household (such as “robotic chef”) and industrial environments.30,31 Aiming for the realization of robots close to human, the demand for the intellectual machines embodied with taste senses seems to be of immense significance.25 To address the challenge of incorporating chemical sensors into a robot hand, we introduce here a robotic chemical-flavor sensing glove for “touch and sense” fingertip detection of key tastes, including sweetness, sourness, and spiciness, in a varied range of foodstuffs and beverages (Figure 1). We relied on printing the corresponding flexible stress-enduring taste electrochemical sensors on a nitrile glove, which behave as soft robotic skin, and interfaced these fingertip sensing electrodes with an ultralight conformal wearable potentiostatic

attention has been given to chemical sensory modalities, hence greatly hindering the interaction and cognitive capabilities of mobile robots in variety of industrial and daily activities.15 A key limitation for realizing human-like performance in diverse robotic operations is the lack of chemically sensitive skin for robots.7 Despite of the availability of numerous chemical sensing methods, their incorporation into a robot is not straightforward. Thus, crucial to such advances is the development of robotic chemical sensors that can mimic the human “touch and sensing” capabilities. Such chemical sensing robotic skin should help robots to track their surrounding chemical environment (e.g., for potential hazards and search and rescue missions) and to create advanced chemical identification mapping capabilities. Skin-worn wearable chemical sensors have been developed recently to directly sense different biomarkers (metabolites, electrolytes) on the epidermis in connection to diverse fitness and healthcare applications.18−22 However, applications of wearable flexible chemical sensors to human-like robots have been limited and unexplored.6 To realize the robotic chemical monitoring capability, it is essential to develop similar robotic skin chemical sensors technology, with high selectivity and sensitivity. The coupling of low-cost skin-worn electrochemical skin sensors with robotic technologies has the potential to B

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Fabrication of the Robotic Gustatory Fingers. Flexible and stretchable inks were prepared following our previous work.37 In brief, 8.2 g Ercon carbon ink was mixed with 5% (1 g) PS-PI-PS (which was prepared in 8 mL xylene). Subsequently, the prepared mixture was placed in a speed mixer (DAC 150.1 FVZ, FlackTek, Inc., Landrum, SC, USA) for 2 min at 1000 rpm. The carbon-based ink was employed to design the working and counter electrodes. Reference electrode and connections, based on Ag/AgCl ink, were prepared by mixing 8.7 g Ercon Ag/AgCl ink and 1.3 g (13 wt %) of Ecoflex using the speed mixer for 3 min at 1200 rpm. For the sweetness finger, the PB flexible ink was prepared following a similar protocol than for carbon flexible ink. The electrode design was made using AutoCAD (Autodesk, San Rafael, CA) and outsourced for fabrication on stainless steel through-hole 12 in. × 12 in. framed stencils of 200 μm thickness (Metal Etch Services, San Marcos, CA, USA). Ag/AgCl layer was first printed followed by carbon or PB layers. Finally, on top of the connections, an insulator flexible layer was printed to avoid shortcuts. The printed ink layers were cured at 75 °C for 10 min after each layer was printed. The printed gustatory electrodes can withstand mechanical stress due to their inherent flexible properties of the ink coupled with the soft and flexible properties of the investigation glove. Before use, carbon-based electrode surfaces were electrochemically activated. Carbon-based index “sour finger” was oxidized at +1.2 V for 600 s in 1 M Na2CO3. Carbon-based ring “spicy finger” was pretreated by applying 5 cyclic voltammetric scans from 0 V to +1 at 0.1 V s−1 in 0.1 M acetate buffer, pH 4.5. The middle finger of the robotic hand based on PB ink-based working electrode was used for glucose sensing. Enzyme GOx was immobilized on the working electrode for detection of the enzymatic product, hydrogen peroxide.38 The GOx solution (40 mg/mL in 0.1 M PBS, pH 7.4) was mixed in an equal ratio with chitosan solution (0.5% in 0.1 M acetic acid). Subsequently, 5 μL of the homogeneous solution was drop-cast on the surface of the PB-based working electrode and allowed to dry overnight at 4 °C. The modification of the PB transducer was completed by placing 3 μL of a 0.5% Nafion solution (in H2O:ethanol, 1:1), with the resulting Nafion layer protecting the enzyme. Once the Nafion layer was dried up, the electrodes were qualified for usage. Robotic Hand Preparation. The robotic hand (Figure 1B) is based on flexible joints capable of semiautomatically maneuvers, by pulling the cords that independently connect each finger. Prior to use, the printed glove was smoothly fixed on the robotic hand, acting as an external e-skin layer. In this way the printed electrodes evenly matched the fingertips of the index, middle, and ring robotic fingers, and can readily come into contact with several foodstuff samples. Circuitry and Contacts. The performance of the robotic hand was dictated not only by the quality of the individual system elements, but also by the way they are integrated. Therefore, the robotic hand was incorporated with a flexible 50.8 × 24.1 mm2 printed circuit board (PCB) to wirelessly transmit the data to a laptop or tablet. The electronic PCB board consisted of a polyimide-based flexible platform based on a controller CC2640 procured from Texas Instrument (TI, Dallas, TX). The board contained an integrated Bluetooth Low Energy (BLE) function to empower the data transmission wirelessly between the robotic sense fingers and the laptop. The flexible electronic board was powered using a Li-ion rechargeable battery. The battery output was controlled by a low dropout regulator (LP2981− 33 from TI) to gain 3.3 V actual and steady power for each circuit parts. Moreover, an inverting DC-DC converter was utilized to form nominal −3.6 V power that was provided to the circuit of the counter electrode to increase voltage limits to +1.2 V for the caffeine detection. Since the number of wires/threads might affect the robot’s dexterity, the threads were routed along the bottom palm, while the fingers were cable-free (Figure 1C). Connections from the PCB to the Ag/AgCl pads of the bottom robotic hand were established using 3 × 1 mm2 cylinder-shaped neodymium magnets for affixing the conductive thread which connects the printed trace to the PCB (Figure S1 in the Supporting Information). The conductive threads offer a fairly low resistivity of 10 ohms per foot. The threads are less prone to oxidation providing the robustness advantage to the robotic

circuitry. By doing so, the recorded data was wirelessly transmitted, in real-time, to a smart mobile device. The operation of the robotic sense fingers is based on voltammetric detection of key molecules responsible for the corresponding flavors of the food sample, and confirming their identity by their specific electrochemical signatures (peak shape and potential). The “sense-of-taste” robotic hand, thus, contains three printed fingertip sensing electrodes, operated by square wave voltammetry and amperometric electrochemical techniques. The carbon-printed index finger is used to sense the sour flavor through the detection of ascorbic acid.32 Prussian-blue modified enzyme-based biosensor, printed on the sweetsensing middle finger, allows rapid detection of glucose.33 The third ring carbon-based finger senses the food spiciness through the detection of the capsaicin molecule, which is responsible for this taste.34 The flavor of numerous solid and liquid food samples was thus rapidly identified using the robotic sensing hand. Such fingertip testing of solid food samples is analogous to voltammetry of microparticles operation,35 known formerly as abrasive voltammetry,36 which involves mechanical transfer of the sample powder onto an active electrode area. The robotic taste sensing concept was successfully extended for discrimination between caffeinated/decaffeinated and sugar/sugar-free beverages using a separate glove consisting of the caffeine and sugar sensing fingers. The human-like robotic sensing skin can thus discriminate accurately between different tastes and food additives (e.g., sweet, sour, spicy, or caffeine) in connection with the electrochemical fingerprint of the taste marker. Such taste sensing in humanoid robots could facilitate a wide range of industrial operations and daily activities. The chemical sensing robotic skin concept could be further extended toward automatic decision-making operation in connection to additional data algorithms and processing. The new soft wearable robotic sensing approach provides a low-cost and rapid pointof-use screening tool for the food industry, and can be readily expanded to diverse chemical sensing applications, holding considerable promise for cooperative human assistance and diverse robotic operations.

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EXPERIMENTAL SECTION

Chemicals, Materials, and Instrumentation. Robot hand (4M, Hong Kong, China), white nitrile examination powder-free gloves (Diamond Gloves, CA, USA), carbon ink (E3449, Ercon, Inc., MA, USA), Prussian blue (PB) ink (C2070424P2, Gwent Group, UK), Ag/AgCl ink (E2414, Ercon, Inc., MA, USA), silicone elastomer Ecoflex 00−30 (Smooth-On, Inc., PA), permanent fabric adhesive (Aleene’s, Inc., Fresno, CA, USA), potassium ferricyanide, dipotassium hydrogen phosphate, potassium dihydrogen phosphate, sodium acetate (Sigma-Aldrich, USA), xylene (Fisher scientific, USA), polystyrene-block-polyisoprene-block-polystyrene (PS−PI-PS) (styrene 14 wt %) were used. L-ascorbic acid, α-D-glucose, capsaicin, caffeine, sodium carbonate anhydrous, acetic acid, glucose oxidase, chitosan, and Nafion were purchased from Sigma-Aldrich (St. Louis, MO, USA), while acetate buffer was purchased from Fluka Analytical, glucose oxidase (GOx), and phosphate buffer saline (PBS) from Sigma-Aldrich (USA). The agarose powder was purchased from MCB Manufacturing Chemists, Inc. (Gibbstown, NJ, USA). Ultrapure water (18.2 MΩ cm) was employed in all experiments. All reagents were used without further purification. The different food samples were purchased from a grocery store located in San Diego, CA. Cylindershaped neodymium magnets were purchased from Thackery, Etsy (NY, USA) while the conductive thread was obtained from Adafruit (NY, USA). Electrochemical measurements were carried out using a flexible printed circuitry wearable board described below. C

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hand system. In addition, the thin yarns are less bulky compared to other commercial wires. The threads stick well to the magnet, being convenient for proper alignment with the silver trace printed on the investigation glove (Figure S1). A magnet-holding rectangular strip system was glued on the reverse side of the investigation glove and featured three holes (of 3 mm diameter/each). In this way, a set of three magnets was attached on the reverse side of the glove, while the matching magnets were positioned on the top of the threads. The magnetic force between the magnets on both sides allows the stable and sturdy connection between the printed silver trace and the conductive thread (Figure S1 in the Supporting Information). The threads were then connected with the flexible PCB, the threads being sandwiched between two magnets, one on top and one on bottom of the PCB. Robotic Fingers Taste Sensing. Two main sets of experiments were carried out to examine the applicability of the robotic sensing fingers to commonly consumed liquid and solid phase of food samples. The probes were placed above each gustatory finger and, subsequently, the finger moved down to a certain depth (for liquid samples) or touching the powder (solid ones) (Figure 1D). After recording the electrochemical “taste” signal of the target samples, the finger moved up, to the noncontact position. The electrochemical response of the robotic sensing finger was performed using square wave voltammetry (SWV) measurements with a 25 mV amplitude and 15 Hz frequency, between −0.2 and +0.8 V (for ascorbic acid), between +0.4 and +1.2 V (for caffeine), and between −0.2 and +0.8 V (for capsaicin). Amperometric glucose measurements were carried out at −0.1 V for 100 s after contacting the food sample for 2 min. Flavor Detection in Liquid Alimentary Samples. The robotic sensing fingers were consecutively immersed into the glasses containing the liquid samples, including fruit juices (orange, lemon, pineapple, cherry, apple, watermelon), sodas (cola), energy drinks (sweet, unsweetened, and caffeine-based), coffee, tea, and chili extracts (green, red, and paprika varieties). The electrochemical measurements were carried out immediately after the fingers were in direct contact with the investigated solutions, except for “sweet finger”, which required a 2 min incubation time to allow the enzymatic reaction to take place. In all the experiments aimed at screening the liquid foodstuff, 5 mL of the commercial (untreated and undiluted) beverage was poured in a glass in which the gustatory fingers-based sensors were immersed. In order to ensure the reproducibility of the robotic fingers, all the liquid alimentary samples tested were measured three times. Flavor Detection in Solid Alimentary Samples. Conductive gels have been considered for their remarkable softness, displaying favorable mechanical properties and offering diverse possibilities for designing the sensing robot fingers.7 In this work, agarose hydrogel was used for completing an electrochemical cell during assays of solid food samples. Initially, 0.5% agarose powder was added to 0.1 M PBS (pH 7.4) and stirred under heat at 120 °C in a glass vial, until complete dissolution was obtained. Subsequently, 60 μL of the warm solution was drop-cast on the three electrode surface and allowed to cool down to room temperature when it gains the gel consistency. Once the “capturing gel” was cooled down, the robotic fingers were ready for measurements. Solid samples included cane sugar crystals, salt, vitamin C capsules, wheat flour, honey, two varieties of chili powders, lollipop, lemon candies, coffee powders, black pepper, and curry powder. For the investigation of the solid alimentary items, 5 mg of the sample were placed on a weighted paper, except for candies which were analyzed as a whole. The robotic fingers-based electrochemical sensors, interfaced with the conductive gel, gently touched the fixed amount of solid foodstuff. The fingers were in contact with the sample powder for 5 s, as required for the sample collection and adhesion onto the conductive gel. Since the dry sample ingredients need to diffuse through the conductive gel and reach the active area of the working electrode, an optimal time was selected for each separate analyte, as presented in Figure S2 (in the Supporting Information). All the solid samples were screened as triplicates, to verify the reproducibility of the robotic-fingers response.

Article

RESULTS AND DISCUSSION

Robotic Taste Sensing Fingers: Concept. Incorporating the new chemical sensing ability onto an e-skin robot’s hand expands the sensor ability of robots and their cognitive capabilities while enhancing their interaction with the surrounding objects. Such new chemical sensing capabilities are illustrated here toward automated taste discrimination at the robot fingertips. Figure 1A displays the general concept of “Robotic Taste Sensing Fingers” and their applicability to “sense” and distinguish major tastes of food samples. The robotic sensing fingers (Figure 1B) were judiciously designed and fabricated on an investigation lab glove (with the role of robot’s e-skin, Figure 1C), using cost-effective and highperformance screen-printing technology. The electrodes were printed along the fingertips and were connected through conductive serpentine structures up to the wrist where a compact electronic circuitry board was placed. The robotic sense fingers were fabricated based on the different layers of stress-enduring inks printed on the powder-free nitrile gloves. Primarily, the Ag/AgCl ink was mixed with the elastomeric Ecoflex substrate (to impart resilience toward withstand extreme strains), and was employed to print the reference electrode as well as the conductive serpentine connections toward the wrist (containing the electronic interface). The second printed layer used for fabricating the working and counter electrodes, consisting of carbon ink, contain the elastomeric PS−PI−PS copolymer that imparts the necessary stretchability. The sensors were segregated from the connection pads by printing an insulator layer composed of a transparent stretchable glue, that protected the sensor connections, while exposing the sensing surface to the surroundings (food sample). Numerous past robotic devices have relied on fairly rigid, solid materials for their design.39 However, following studies of human behavior and the sensing performance of the tissues and skin, softer materials are receiving growing attention for realizing humanoid robots with enhanced performance.39 Elastic overlays and mechanically compliant contact surfaces are often advocated for their frictional and other properties.39 Therefore, embedding the gustatory sensors on elastic material, such the robotic glove, offers attractive advantages toward the sample manipulation, along with the necessary flexibility, conformability, and stretchability. The new robotic sense fingers offer rapid, onsite detection of different flavors associated with a variety of food items in two short steps, involving “touching” and “sensing” the food samples. For liquid samples, the robotic fingers were dipped into the liquid matrix and the sensing step was initiated immediately. In the case of solid food samples (powders), the robotic fingers were coated with a conductive gel. Figure 1D illustrates the application of index robotic sour, sweet, and spicy fingers to screen and identify the corresponding ascorbic acid, glucose, and capsaicin taste analytes in orange juice, cherry drink, and chili pepper samples. Therefore, the robotic taste sensors using stretchable carbon and silver inks facilitates such robotic application in realistic scenarios. The sensors are able to categorize a great assortment of chemical constituents into flavors, analogous to biological systems. The ready-made robotic fingers were employed to sense and differentiate the flavors by various colored rings (green, blue, and red) denoting the different tastes. The robotic sense glove could readily detect the target flavors based on the primary molecules responsible for the specific flavor. D

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Figure 2. Liquid-phase tests using the robotic finger sensors. (A) SWVs for ascorbic acid detection in (i) orange juice, (ii) cola, (iii) lemon juice, (iv) sports drink, and (v) pineapple juice. (B) Amperometric feedbacks for glucose in (i) apple cider, (ii) sugar-free sports drink, (iii) cola, (iv) sugar-free energy drink, and (v) apple juice. (C) SWVs for capsaicin detection in (i) green chili extract, (ii) coffee, (iii) red paprika extract, (iv) watermelon juice, and (v) red pepper extract. Dotted lines correspond to the blank signal recorded in PBS.

corresponding taste markers, independent of the nature of the food matrix. The electrochemical data of Figure 2 clearly indicate that food items with different tastes yield distinct electrochemical signatures (in terms of decision-making parameters), and that each taste can, thus, be easily discriminated. Several tests were conducted with a diverse range of food samples in order to demonstrate the analytical performance of the robotic sensing fingers. Figure 2A shows a schematic of the robotic index finger dipped in lemon juice, illustrating the operation and performance for sourness detection. Figure 2 A(i-v) displays the SWV response for ascorbic acid in randomly tested food items such as (i) orange juice, (ii) cola drink, (iii) lemon juice, (iv) energy drink, and (v) pineapple juice samples. Depending on the sample sourness, the printed index sensing robotic finger displayed a positive and negative response linked to the presence or absence of ascorbic acid in the specific food samples. Accordingly, based on the decision-making parameters, orange, lemon, and pineapple juices were categorized as sour drinks, while the other beverages were not associated with this taste. Similarly, the robotic sensing-skin was applied to verify the sweetness of frequently consumed beverages. The middle robotic finger was, thus, utilized for amperometric detection at the GOx-modified Prussian-blue electrode transducer, as described in the Experimental Section. Upon contact with the sweet-based sample, GOx reacts with glucose molecules present in the sample, and the peroxide enzymatic product is detected amperometrically at −0.1 V. The level of H2O2 generated via the enzymatic reaction is closely related to the glucose content in the investigated beverages. Figure 2B shows the schematics of the middle sweet-sensing fingers immersed into the target beverage sample, toward screening its sweetness flavor. Various drinks (Figure 2B(i-v)) were selected for testing the performance of this finger, including (i) apple cider, (ii) sugar-free sports drink, (iii) cola, (iv) sugar-free energy drink, and (v) apple juice. The corresponding amperometric response of these tests was correlated with the glucose content specified on the label of these beverages. The robotic middle finger,

The respective electrochemical signatures, displayed in Figure 1E, can readily alert the robot about the sourness, sweetness, and spiciness, respectively, of the corresponding sample. The ascorbic acid and capsaicin targets were screened directly using SWV, while glucose was indirectly detected amperometrically by measuring the H2O2 product of the GOx/glucose reaction. The electrochemical feedback was thus wirelessly transmitted to a smart device via the built-in wireless communication feature of the electronic board. Based on the individual electrochemical signatures linked to the specific taste responsible target, it was possible to determine the decision parameters for the robotic sensor. In this fashion, the correct integration of the signals from different gustatory sensors had permitted a yes/no detection of the flavors. Thus, the decision of sourness (AA presence) is done when peak potential (Ep) is around ∼ +0.2 V (vs Ag/AgCl) with initial (Ei) and final (Ef) potentials of ∼ +0.02 to ∼ +0.5 V and a full width at half-maximum (fwhm) of around 300 mV. Likewise, the decision regarding the presence of sweetness is made based on the change in current intensity (>2 μA) at −0.1 V. The spiciness decision is made at Ep ∼ +0.35 V (vs Ag/ AgCl) with Ei and Ef of ∼ +0.2 to ∼ +0.5 V and fwhm of around 100 mV. Moreover, the robotic sense hand was able to alert about the presence of caffeine in different commercial beverages, when Ep was around ∼ +1.2 V (vs Ag/AgCl) with Ei and Ef of ∼ +1.1 to ∼ +1.3 V and fwhm of around 200 mV. As a consequence, the robotic sense fingers were able to identify the sweet, sour, or spicy flavored food, based on the corresponding electrochemical signatures as decision makers. Robotic Fingers for Taste Sensing in Liquid Foodstuff. Three typical key taste substances, ascorbic acid (sour), glucose (sweet), and capsaicin (spicy), were screened in liquid food samples, by dipping the new robotic taste sensors in the glass beakers containing these drinks and beverages. Figure 2 shows the electrochemical feedback, corresponding to the different flavors, obtained rapidly using SWV and amperometric measurements. The corresponding robot tasting sensors yield similar response (signature) associated with the E

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Figure 3. Solid-phase tests using the robotic finger sensors coated with 0.5% agarose conductive gel. Images show the mechanical transfer of the solid powder onto the fingertip sensing electrode. (A) SWVs for ascorbic acid detection in (i) vitamin C capsules, (ii) sugar powder, (iii) lemon candy, (iv) coffee, and (v) vitamin C tablets. (B) Amperometry signatures for glucose in (i) lollipop candy, (ii) wheat flour, (iii) honey, (iv) salt, and (v) lemon candy. (C) SWVs for capsaicin detection in (i) paprika powder, (ii) black pepper, (iii) curry powder, (iv) nondairy coffee creamer (powdered), and (v) red paprika powder. Dotted lines correspond to the blank signal recorded in PBS solution.

method is reliable and can be deployed to diverse settings with easily maneuvered robotic sensing fingers. The experiments were performed repeatedly (n = 5) by rotation of the indexmiddle-ring robotic fingers, and all three output patterns were characterized with RSD lower than 7%. The analysis time was up to 100 s for both the SWV or amperometric operations. The exact sensitivity of the real-sample data depends primarily on the concentration of the taste marker molecule as well as the nature and viscosity of these different media. Moreover, in order to establish an objective control on the responses without compromising the subjectivity of the analyst, electrochemical analysis was applied for real samples, as shown in Video S1. By virtue of wireless data transmission to a smart device, these robotic sensing fingers can ultimately be extended for differentiating the taste of a diverse range of food items. Furthermore, the corresponding taste can be quantified by the robotic sense finger following the calibration curve of the standard taste molecules (Figure 2). Thus, the robotic e-skin sensing fingers are capable of direct sensing of the taste itself, to offer efficient taste discrimination and quality control of food samples. Automated data processing could impart the intelligence essential for automated robotic operation and corresponding decision making. Robotic Fingers for Taste Sensing in Solid Foodstuff. The inspiration for developing the sense fingers has been the goal to offer robots and humans the ability to discriminate accurately between the main flavors among a wide range of food items. Besides testing the foodstuff in liquid phase, the novel robotic hand was extended for detection of sweetness, sourness, and spiciness in a variety of alimentary items in their solid/dry phase. As illustrated in Figure 3, such robotic taste sensing of solid sample involves mechanical transfer of the powder sample to the fingertip working electrode prior to the voltammetric scan. Agarose conductive gel was used to facilitate the collection of the solid sample onto the sensing area. Details of the conductive gel preparation and its

thus, yielded positive feedback linked to glucose content for apple cider, cola, and apple juice samples. In contrast, and as expected, no glucose response was observed for the sugar-free sports drink and energy drink samples, confirming the absence of glucose in these two samples. Additionally, in a control experiment, the middle “sweet finger” was tested in the presence of sweet beverages without the GOx modification (Figure S3 in the Supporting Information). As expected, no apparent current response was obtained for the sweet drinks in the absence of the enzyme, confirming the important role of GOx for the operation of the “sweet robotic finger”. The capsaicin molecule is mainly responsible for the spiciness of food items. The third robotic sense finger was developed to screen and test the spiciness of the food items. As illustrated in Figure 2C(i-v), the robotic sense glove was applied to discriminate several samples, including (i) green chili extract, (ii) coffee, (iii) red paprika extract, (iv) watermelon juice, and (v) pepper extract. As indicated from Figure 2C, green chili extract displayed a positive electrochemical response, with a large well-defined SWV oxidation peak around +0.35 V (vs Ag/AgCl), with high peak current of 12 μA. The red paprika and pepper extracts also yielded defined oxidation peaks at similar potential (with similar shape but smaller magnitude), corresponding to their capsaicin content. In contrast, a negative response was obtained for the unspicy coffee and watermelon samples. The electrochemical sensor of the robotic ring finger is thus capable of reliably distinguishing between spicy and nonspicy food samples. Overall, the complete set of tests shown in Figure 2 clearly illustrate the reliability, adaptability, scope, and potential of the new robotic flavor-sensing fingers. Such favorable performance of the robotic sense fingers is based on screening the molecules responsible for various flavors in the target samples, and associates their electrochemical signatures with default analytical parameters. In this way, a particular target molecule can be traced at a similar potential region, showing that the F

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Figure 4. (i) Electrochemical signatures and (ii) the corresponding calibration curves achieved with the robotic fingers-based sensors; (A) SWVs response for 1 to 6 mM ascorbic acid, (B) amperograms for 1 to 6 mM glucose, (C) SWVs recorded for 10 to 50 ppm capsaicin, and (D) SWVs recorded for 0.1 to 0.9 mM caffeine.

advantages are described in Experimental Section. Subsequently, we optimized the incubation time required for robotic fingers touching and collecting the solid food samples. For example, for ascorbic acid detection, the index finger touching the solid residue was tested for 12 min, recording the electrochemical signature of the analyte every 2 min. As illustrated in Figure S1, the voltammetric peak increases upon increasing the incubation time, reaching saturation after 10 min. However, it was noticed that the response obtained at 4 min was qualitatively enough to screen the ascorbic acid in dry samples. Thus, this time was chosen to detect the sourness in samples. Similar tests were conducted for sensing sweetness and spiciness using the middle and ring robotic sense fingers, respectively. The glucose and capsaicin residues were touched for different time durations and their electrochemical response was recorded in the presence of 0.5% conductive agarose gel. Based on the experimental outcomes, the incubation time of 2 and 4 min were chosen for glucose and capsaicin detection, respectively (Figure S1). Following the procedure of taste sensing applied for liquid alimentary items, the flavor screening of the commonly consumed dry food samples was carried out. For screening of sourness (Figure 3A(i-v)), various food representing ascorbic acid sources were tested, which includes (i) vitamin C capsules, (ii) cane sugar powder, (iii) lemon candy, (iv) coffee, and (v) vitamin C tablets. As illustrated in this figure, mixed responses were recorded, few samples were identified as sour, with ascorbic acid content, while the other samples were found negative and had no sourness. Vitamin C capsules, candy, and vitamin C tablets shown a positive SWV peak, whereas sugar and coffee voltammograms illustrated the absence of the oxidation peak associated with ascorbic acid. The highest current response was obtained with vitamin C capsule (148 μA current value) which signifies the presence of highest concentrations of ascorbic acid, while smaller peak responses of lemon candy and vitamin C tablets displayed lower concentration of ascorbic acid. Similarly, the robotic hand was applied to sense sweetness and spiciness in various food/supplements. As described in the previous section, amperometry was implemented to test the presence of glucose. For sweetness detection (Figure 3B(i-v)), several samples were tested, including (i) lollipop candy, (ii) wheat flour, (iii) honey, (iv) salt, and (v) lemon candy. Among the tested samples, lollipop candy, honey, and lemon candy were found positive, which signifies the presence of glucose. Based on the obtained current values by the middle robotic finger, the sweetness order of tested samples was as follows: lollipop

candy > honey > lemon candy. The other two samples including the wheat flour and salt did not contain glucose, and hence, their amperograms were similar to the blank response (shown as black dotted curves). Likewise, spiciness was also tested in various solid foodstuffs to develop a complete robotic sense fingers for taste. For spiciness detection (Figure 3C(iv)), capsaicin molecule was screened in (i) paprika powder, (ii) black pepper, (iii) curry powder, (iv) nondairy coffee creamer (powdered), and red (v) paprika powder. As illustrated in this figure, the voltammograms showed positive anodic response associated with the presence of capsaicin for paprika powder, curry powder, and red paprika powder, with higher to lower spiciness content, respectively. The other tested capsaicin-free samples, such as the nondairy coffee creamer and black pepper powders, did not generate any voltammetric response, which is in agreement with the food labels, confirming the absence of capsaicin. Unlike the liquid assays, solid-phase testing of powder samples relies on singleuse measurements considering the challenges of removing the collected sample powder from the agarose gel coating and avoiding false positive readings. Calibration of Flavor Analytes with Robotic Fingers. Different electrochemical techniques, including SWV and amperometry, have been employed to develop and demonstrate the adaptability of the robotic sensing fingers to discriminate among different tastes, such as sweetness, sourness, and spiciness in food items. These flavors are in strong correlation with the molecules responsible for those tastes which include but are not limited to glucose, ascorbic acid, and capsaicin, accordingly. The voltammetric measurements have been carried out after pretreatment of the carbon ink electrodes, while the amperometry measurements required the modifications of the PB transducer with GOx. The SWV was employed for measuring increasing ascorbic acid concentrations in 1 mM steps up to 6 mM by the index “sour finger”. Figure 4A,i displays the distinct oxidation peak of ascorbic acid at +0.2 V (vs Ag/AgCl). Furthermore, increasing levels of glucose (1 mM to 6 mM) were readily detected using amperometric technique by the middle “sweet finger”. The results shown in Figure 4B,i display an increase of the reduction current of H2O2, proportional to the glucose concentrations. The analyte for spiciness taste, capsaicin, was also quantified by SWV, analysis performed by the ring “spicy finger”. Figure 4C,i illustrates the voltammograms obtained at the carbon-based electrode for capsaicin concentrations increased from 10 to 50 ppm. A well-defined sharp oxidation G

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Figure 5. (A) Schematic of “caffeine robotic finger” dipped in coffee. Response of the “caffeine” and “sweet” sensing fingers to (B) coffee, (C) cola or juice, and (D) tea varieties. (i,ii) SWVs obtained with “caffeine robotic finger” in (B,i) coffee, (B,ii) decaffeinated coffee, (C,i) cola, (C,ii) orange juice, (D,i) black tea, and (D,ii) herbal tea. (iii, iv) Amperograms obtained with “sweet finger” in (B,iii) sweetened and (B,iv) sugar-free coffees, (C,iii) regular and (C,iv) diet cola, and (D,iii) sweetened and (D,iv) sugar-free black tea. (E,i-iii) Time-lapse images from Video S1 (in the Supporting Information), illustrating the coffee detection with “caffeine finger”.

robotic finger was deployed for screening for the presence or absence of caffeine and, thus, discriminating between caffeinated and caffeine-free beverages such as coffee, tea, cola, and soft juices. Coupled with the caffeine detection, the robotic sweetness sense finger was used for detecting the presence or absence of sugars in coffee and tea samples and identifying the presence of sugar substitutes in these drinks. As illustrated in Figure 5, caffeine was measured using SWV, while glucose was detected amperometrically using the GOx finger. For this application, the glove was consisted of the caffeine and sugar sensing fingers. Figure 5Bi shows the voltammograms of caffeine detection in (i) coffee and in (ii) decaffeinated coffee. Figure 5C displays the caffeine response in (i) coffee and (ii) orange juice, while Figure 5D shows the response of (i) black tea and (ii) herbal tea. The results obtained for caffeine screening in the tested samples were in good agreement with the supplier label, with a defined caffeine oxidation peak observed at the potential of +1.2 V (vs Ag/AgCl) for the coffee, cola, and black tea samples, confirming the presence of caffeine in these drinks. Figure 5E illustrates the performance of the caffeine robotic finger during (i) dipping the finger in beverage, (ii) recording the electrochemical signature of an unknown analyte, and (iii) confirming the presence of the caffeine molecule based on the decision-making parameters. Video S1 (in the Supporting Information) presents the realtime maneuver for “caffeine finger” toward caffeine detection. Moreover, the same beverages samples were analyzed by the robotic “sweet” finger for their glucose content. Figure 5B displays the amperometric response for the (iii) coffee sample mixed with one teaspoon glucose and for the (iv) sugar-free coffee. Similarly, Video S2 shows the real time sweetness detection in coffee drink. Figure 5C shows the amperograms for (iii) sugar-rich cola drink sample and of the (iv) sugar-free diet cola beverage. Both the sweetened coffee and cola samples were characterized with a change in the current intensity (>2 μA, at −0.1 V) specific to glucose detection, while a response similar to the background signal (black dotted line) was observed for the sugar-free beverages. Moreover, the

peak was observed at +0.35 V (vs Ag/AgCl), the peak current being increased linearly upon increments in the spicy analyte concentration. The oxidation peaks of ascorbic acid vs capsaicin are, thus, differentiated by the robotic sense fingers based on the distinct oxidation potential, accompanied by the particular shape of the peak: ascorbic acid oxidation is characterized by a blunt peak, while capsaicin oxidation is identified with a distinct, sharp, and narrow peak. Additionally, caffeine has been measured by SWV, displaying increasing oxidation peaks for increasing (0.1 μM to 0.9 μM) levels of caffeine. Figure 4D,i shows the linearity of the caffeine oxidation at potential of +1.2 V (vs Ag/AgCl). The corresponding calibration plots (Figure 4,ii) display a welldefined calibration for each of the four tested analytes. The robotic fingers were designed based on the single-use concept; therefore, each analysis was completed using a new robotic finger, emphasizing good reproducibility in the performances. The gustatory fingers responded with excellent linearity (R2 near 1 for all the analytes), as presented in Figure 4,ii. Moreover, the robotic sensors demonstrated a good sensitivity with linear dependence (slope of 4.25 μA/mM for ascorbic acid, 4.94 μA/mM for glucose, 0.102 μA/ppm for capsaicin, and 0.05 mM/μA for caffeine) and nearly zero intercept (2.3 ± 2.5 for acid ascorbic, −0.4 ± 1.4 for glucose, 0.7 ± 0.8 for capsaicin, and 1.3 ± 1.7 for caffeine), for 95% confidence level. In order to check the reproducibility of the system, triplicates of the calibration plots were performed for each analyte, showing good interassay robotic finger reproducibility with RSD < 2% for ascorbic acid, 10% for capsaicin, 5% for glucose, and 8% for caffeine (n = 3). Robotic Finger Application for Detecting Caffeine and Glucose in Beverages. In modern day society, consumers have become more selective and demanding regarding their diet and lifestyle. Therefore, the new robotic sensing fingers have been extended to caffeine detection because of its considerable relevance as the most widely consumed global psychoactive drug, with coffee and tea representing the major source of intake.40 In this sense, the H

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These new capabilities will enhance their cognitive and motion capabilities to gather useful chemical information about their surroundings and providing remarkably useful feedback toward human assistance.

sweetened black tea (Figure 5D(iii) had a positive response for glucose content, while the unsweetened black tea (Figure 5D(iv) showed a negative response toward the glucose detection. The developed robotic sensors were deployed for caffeine and glucose detection, keeping in mind that the drinks containing caffeine and/or sugar are the largest consumed beverages across the globe.41−44 The robotic finger sensors were able to successfully discriminate between caffeinated/ decaffeinated drinks and sugar/sugar-free beverages, indicating considerable help to consumers in many ways. For instance, robotic fingers embodied with electrochemical sensors can be beneficial in avoiding the risk associated with caffeine or sugar intake. On the other hand, security in the food sector is also required to ensure the availability of nutrition/required ingredients. One of the major problems for patients suffering from illnesses related to food intake is the lack of premature information if the meal is adequate to be served or if the level of food constituents greatly exceeds the safe consumption.45 As a matter of fact, as presented in Figure S4 (in the Supporting Information), the middle robotic finger was challenged to offer quantitative analytical feedback related to the level of glucose in (A) coffee and (B) tea. In this scenario, (i) the sugar-free drinks and the drinks mixed with (ii) one, (ii) two, and (iii) three spoons of glucose were carefully investigated with the robotic-based printed sensor system. The data achieved by the sustainable robotic model strongly confirms that the current intensities are increasing upon increasing the level of glucose in coffee and tea drinkable samples. Under these circumstances, the decision-making robotic fingers can execute not only qualitative, but also quantitative analytical tasks, for a sterling response toward human assistance.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssensors.8b00778. Information about the optimization parameters and control experiments (PDF) Experimental Video S1  caffeine finger (AVI) Experimental Video S2  sweet finger (AVI)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Joseph Wang: 0000-0002-4921-9674 Author Contributions #

Equal contribution.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by Defense Threat Reduction Agency Joint Science and Technology Office for Chemical and Biological Defense (HDTRA 1-16-1-0013) and the UCSD Center of Wearable Sensors. B.C. acknowledges the support from the Fulbright grant and UMF Cluj-Napoca, Romania for the research grant no. 7690/24/15.04.2016.

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CONCLUSIONS The present study has aimed at expanding the robotic sensory ability to include flexible skin chemical sensor technology. The new chemical-sensing robotic fingers concept was demonstrated for screening key tastes in diverse food and beverage items. Fast and reliable discrimination of sweetness (middle finger), sourness (index finger), and spiciness (ring finger) was thus accomplished by detecting the glucose, ascorbic acid, and capsaicin constituents, respectively, in a wide range of food samples. Such “sense-touch” detection of taste marker molecules relies on their distinct electrochemical signatures. Additionally, the robotic fingers concept has been expanded for detecting the presence of caffeine and sugars in common beverages. The flexible robotic taste sensors have been integrated to an ultralight portable wireless electronic (potentiostatic) interface. The enzyme-based “sweet” sensing can be replaced with enzyme-free fingers utilizing common nonenzymatic catalytic glucose sensors.46 In addition, while the “spicy” and “sour” fingers have relied on the printed carbon electrodes, the corresponding carbon inks can be modified whenever needed with various catalytic nanomaterials for achieving higher sensitivity and selectivity. Such automated taste discrimination could benefit from the integration of advanced algorithms toward autonomous decision making. The speed and direct efficient screening of a substantial number of foodstuff samples make the gustatory robotic sense fingers an attractive alternative to existing taste sensors. By expanding the sensing ability of robots to chemical sensory modalities, the new robotic fingertip chemical sensing is expected to pave the way to new applications in soft wearable robotics and to broaden the perceptual capabilities of robots.

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REFERENCES

(1) Gates, B. A. Robot in Every Home. Sci. Am. 2007, 296, 58−65. (2) Brogårdh, T. Present and future robot control development-An industrial perspective. Annu. Rev. Control. 2007, 31, 69−79. (3) Cirillo, A.; Cirillo, P.; De Maria, G.; Natale, C.; Pirozzi, S. A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing. J. Sens. 2017, 2017, 1−14. (4) Samani, H.; Saadatian, E.; Pang, N.; Polydorou, D.; Fernando, O. N. N.; Nakatsu, R.; Koh, J. T. K. V. Cultural Robotics: The Culture of Robotics and Robotics in Culture. Int. J. Adv. Robot. Syst. 2013, 10, 400−410. (5) Lee, M.; Nicholls, H. Review Article Tactile sensing for mechatronics-a state of the art survey. Mechatronics. 1999, 9, 1−31. (6) Agrawal, S. K.; Erdman, A. G. Biomedical Assist Devices and New Biomimetic Machines-A Short Perspective. J. Mech. Des. 2005, 127, 799−801. (7) Dahiya, R. S.; Metta, G.; Valle, M.; Sandini, G. Tactile SensingFrom Humans to Humanoids. IEEE Trans. Robot. 2010, 26, 1−20. (8) Song, G.; Wang, H.; Zhang, J.; Meng, T. Automatic docking system for recharging home surveillance robots. IEEE Trans. Consum. Electron. 2011, 57, 428−435. (9) Polygerinos, P.; Wang, Z.; Galloway, K. C.; Wood, R. J.; Walsh, C. J. Soft robotic glove for combined assistance and at-home rehabilitation. Rob. Auton. Syst. 2015, 73, 135−143. (10) Saudabayev, A.; Varol, H. A. Sensors for Robotic Hands: A Survey of State of the Art. IEEE Access. 2015, 3, 1765−1782. (11) Majidi, C. Soft Robotics: A Perspective-Current Trends and Prospects for the Future. Soft Robot. 2014, 1, 5−11. (12) Choi, W.-C. Polymer Micromachined Flexible Tactile Sensor for Three-Axial Loads Detection. Trans. Electr. Electron. Mater. 2010, 11, 130−133. I

DOI: 10.1021/acssensors.8b00778 ACS Sens. XXXX, XXX, XXX−XXX

Article

ACS Sensors

Microparticles: A Simple Method for Screening Purposes. Electroanalysis 2001, 13, 477−483. (36) Scholz, F.; Lange, B. Abrasive stripping voltammetry -an electrochemical solid state spectroscopy of wide applicability. TrAC, Trends Anal. Chem. 1992, 11, 359−367. (37) Mishra, R. K.; Hubble, L. J.; Martín, A.; Kumar, R.; Barfidokht, A.; Kim, J.; Musameh, M. M.; Kyratzis, I. L.; Wang, J. Wearable Flexible and Stretchable Glove Biosensor for On-Site Detection of Organophosphorus Chemical Threats. ACS Sensors. 2017, 2, 553− 561. (38) Kim, J.; Campbell, A. S.; Wang, J. Wearable non-invasive epidermal glucose sensors: A review. Talanta 2018, 177, 163−170. (39) Girão, P. S.; Ramos, P. M. P.; Postolache, O.; Miguel Dias Pereira, J. Tactile sensors for robotic applications. Measurement. 2013, 46, 1257−1271. (40) Maiolino, P.; Galantini, F.; Mastrogiovanni, F.; Gallone, G.; Cannata, G.; Carpi, F. Soft dielectrics for capacitive sensing in robot skins: Performance of different elastomer types. Sens. Actuators, A 2015, 226, 37−47. (41) Verster, J. C.; Koenig, J. Caffeine intake and its sources: A review of national representative studies. Crit. Rev. Food Sci. Nutr. 2018, 58, 1250−1259. (42) Knight, C. A.; Knight, I.; Mitchell, D. C.; Zepp, J. E. Beverage caffeine intake in US consumers and subpopulations of interest: Estimates from the Share of Intake Panel survey. Food Chem. Toxicol. 2004, 42, 1923−1930. (43) Popkin, B. M.; Nielsen, S. J. The Sweetening of the World’s Diet. Obes. Res. 2003, 11, 1325−1332. (44) Miller, P. E.; McKinnon, R. A.; Krebs-Smith, S. M.; Subar, A. F.; Chriqui, J.; Kahle, L.; Reedy, J. Sugar-Sweetened Beverage Consumption in the U.S. Am. J. Prev. Med. 2013, 45, 416−421. (45) Schwartz, M. W.; Woods, S. C.; Porte, D.; Seeley, R. J.; Baskin, D. G. Central nervous system control of food intake. Nature 2000, 404, 661−671. (46) Zhu, X.; Ju, Y.; Chen, J.; Liu, D.; Liu, H. Nonenzymatic Wearable Sensor for Electrochemical Analysis of Perspiration Glucose. ACS Sens. 2018, 3, 1135−1141.

(13) Rey, P.; Charvet, P.; Delaye, M. T.; Abou Hassan, S. A high density capacitive pressure sensor array for fingerprint sensor application. Proc. Int. Solid State Sensors Actuators Conf. (Transducers ’97), IEEE 1997, 1453−1456. (14) Almassri, A. M.; Wan Hasan, W. Z.; Ahmad, S. A.; Ishak, A. J.; Ghazali, A. M.; Talib, D. N.; Wada, C. Pressure sensor: State of the art, design, and application for robotic hand. J. Sens. 2015, 2015, 1− 12. (15) Ishida, H.; Wada, Y.; Matsukura, H. Chemical sensing in robotic applications: A review. IEEE Sens. J. 2012, 12, 3163−3173. (16) O’Connor, T. F.; Fach, M. E.; Miller, R.; Root, S. E.; Mercier, P. P.; Lipomi, D. J. The Language of Glove: Wireless gesture decoder with low-power and stretchable hybrid electronics. PLoS One 2017, 12, 1−12. (17) Roh, E. B.; Hwang, U.; Kim, D.; Kim, B. Y.; Lee, N. E. Stretchable, Transparent, Ultrasensitive, and Patchable Strain Sensor for Human-Machine Interfaces Comprising a Nanohybrid of Carbon Nanotubes and Conductive Elastomers. ACS Nano 2015, 9, 6252− 6261. (18) Bandodkar, A. J.; Jeerapan, I.; Wang, J. Wearable Chemical Sensors: Present Challenges and Future Prospects. ACS Sensors. 2016, 1, 464−482. (19) Kim, J.; Valdés-Ramírez, G.; Bandodkar, A. J.; Jia, W.; Martinez, A. G.; Ramírez, J.; Mercier, P.; Wang, J. Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites. Analyst 2014, 139, 1632−1636. (20) Oncescu, V.; O’Dell, D.; Erickson, D. Smartphone based health accessory for colorimetric detection of biomarkers in sweat and saliva. Lab Chip 2013, 13, 3232−3238. (21) Gao, W.; Emaminejad, S.; Nyein, H. Y. Y.; Challa, S.; Chen, K.; Peck, A.; Fahad, H. M.; Ota, H.; Shiraki, H.; Kiriya, D.; Lien, D. H.; Brooks, G. A.; Davis, R. W.; Javey, A. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 2016, 529, 509−514. (22) Bandodkar, A. J.; Wang, J. Non-invasive wearable electrochemical sensors: a review. Trends Biotechnol. 2014, 32, 363−371. (23) Gray, J. O.; Davis, S. T. Robotics in the food industry: an introduction, in: Robot. Autom. Food Ind. 2013, 21−35. (24) Ahmad Nayik, G. Robotics and Food Technology: A Mini Review. J. Nutr. Food Sci. 2015, 5, 1000384. (25) Bogue, R. The role of robots in the food industry: a review, Industrial Robot. An Int. J. 2009, 36, 531−536. (26) Ciosek, P.; Wróblewski, W. Sensor arrays for liquid sensing − electronic tongue systems. Analyst 2007, 132, 963−978. (27) del Valle, M. Electronic Tongues Employing Electrochemical Sensors. Electroanalysis 2010, 22, 1539−1555. (28) Podrażka, M.; Bączyńska, E.; Kundys, M.; Jeleń, P.; Witkowska Nery, E. Electronic Tongue-A Tool for All Tastes? Biosensors 2018, 8, 3−11. (29) Krantz-Rülcker, C.; Stenberg, M.; Winquist, F.; Lundström, I. Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review. Anal. Chim. Acta 2001, 426, 217−226. (30) Bollini, M.; Tellex, S.; Thompson, T.; Roy, N.; Rus, D. Interpreting and Executing Recipes with a Cooking Robot. Exp. Robot. 2013, 88, 481−495. (31) Shimazu, H.; Kobayashi, K.; Hashimoto, A.; Kameoka, T. Tasting Robot with an Optical Tongue: Real Time Examining and Advice Giving on Food and Drink. Symp. Hum. Interface. 2007, 4557, 950−957. (32) Schiffman, S. S.; Dackis, C. Taste of nutrients: Amino acids, vitamins, and fatty acids, Percept. Psychophys. 1975, 17, 140−146. (33) Mahawanich, T.; Schmidt, S. J. Molecular mobility and the perceived sweetness of sucrose, fructose, and glucose solutions. Food Chem. 2004, 84, 169−179. (34) Yang, F.; Zheng, J. Understand spiciness: mechanism of TRPV1 channel activation by capsaicin. Protein Cell 2017, 8, 169−177. (35) Cepriá, G.; Abadías, O.; Pérez-Arantegui, J.; Castillo, J. R. Electrochemical Behavior of Silver-Copper Alloys in Voltammetry of J

DOI: 10.1021/acssensors.8b00778 ACS Sens. XXXX, XXX, XXX−XXX