ELECTRONIC TONGUE SENSOR

An electronic tongue sensor comprises an array of piezoelectric quartz crystal sensors with at least one coating specific for sensing a specific taste-producing molecule. The at least one coating comprises molecularly imprinted polymers of a specific taste-producing molecule.published_or_final_versio

Combined electronic nose and tongue for a flavour sensing system

We present a novel, smart sensing system developed for the flavour analysis of liquids. The system comprises both a so-called "electronic tongue" based on shear horizontal surface acoustic wave (SH-SAW) sensors analysing the liquid phase and a so-called "electronic nose" based on chemFET sensors analysing the gaseous phase. Flavour is generally understood to be the overall experience from the combination of oral and nasal stimulation and is principally derived from a combination of the human senses of taste (gustation) and smell (olfaction). Thus, by combining two types of microsensors, an artificial flavour sensing system has been developed. Initial tests conducted with different liquid samples, i.e. water, orange juice and milk (of different fat content), resulted in 100% discrimination using principal components analysis; although it was found that there was little contribution from the electronic nose. Therefore further flavour experiments were designed to demonstrate the potential of the combined electronic nose/tongue flavour system. Consequently, experiments were conducted on low vapour pressure taste-biased solutions and high vapour pressure, smell-biased solutions. Only the combined flavour analysis system could achieve 100% discrimination between all the different liquids. We believe that this is the first report of a SAW-based analysis system that determines flavour through the combination of both liquid and headspace analysis

Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types

Predicting the composition of red wine blends using an array of Multicomponent peptide-based sensors

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine

LeviSense: a platform for the multisensory integration in levitating food and insights into its effect on flavour perception

Eating is one of the most multisensory experiences in everyday life. All of our five senses (i.e. taste, smell, vision, hearing and touch) are involved, even if we are not aware of it. However, while multisensory integration has been well studied in psychology, there is not a single platform for testing systematically the effects of different stimuli. This lack of platform results in unresolved design challenges for the design of taste-based immersive experiences. Here, we present LeviSense: the first system designed for multisensory integration in gustatory experiences based on levitated food. Our system enables the systematic exploration of different sensory effects on eating experiences. It also opens up new opportunities for other professionals (e.g., molecular gastronomy chefs) looking for innovative taste-delivery platforms. We describe the design process behind LeviSense and conduct two experiments to test a subset of the crossmodal combinations (i.e., taste and vision, taste and smell). Our results show how different lighting and smell conditions affect the perceived taste intensity, pleasantness, and satisfaction. We discuss how LeviSense creates a new technical, creative, and expressive possibilities in a series of emerging design spaces within Human-Food Interaction

Tactile feedback display with spatial and temporal resolutions.

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications

Using context to make gas classifiers robust to sensor drift

The interaction of a gas particle with a metal-oxide based gas sensor changes the sensor irreversibly. The compounded changes, referred to as sensor drift, are unstable, but adaptive algorithms can sustain the accuracy of odor sensor systems. This paper shows how such a system can be defined without additional data acquisition by transfering knowledge from one time window to a subsequent one after drift has occurred. A context-based neural network model is used to form a latent representation of sensor state, thus making it possible to generalize across a sequence of states. When tested on samples from unseen subsequent time windows, the approach performed better than drift-naive and ensemble methods on a gas sensor array drift dataset. By reducing the effect that sensor drift has on classification accuracy, context-based models may be used to extend the effective lifetime of gas identification systems in practical settings

Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

Rawan Atari - The Influence of Multi-Sensory Environment on Physiological Response in Children with Autism Spectrum Disorders and Children with Special Health Care Needs

A research study based on the sensory integration theory was conducted to examine the effects of multi-sensory environment (MSE) on physiological arousal in children with autism spectrum disorder (ASD) and special health care needs. Adapted environments may serve as a mechanism to treat anxiety levels in a population of children who experience more severe generalized anxiety symptoms than typically developing children. The sample consisted of children with community-based diagnoses of ASD and children with special health care needs, primarily children diagnosed with cerebral palsy (CP) from the Milwaukee Center for Independence (MCFI). Treatment for the autism sample was carried out by a trained MCFI staff member and treatment for children with special health care needs was carried out by a trained physical therapist. Electrodermal response was used as a measure to detect the “fight or flight” response of the sympathetic nervous system. The measurement of electrodermal activity was recorded by a wireless bracelet device that recorded the skin conductance level of the participant prior to entering the sensory room, during treatment in the sensory room, and after exiting the sensory room. Results indicated increased arousal in children with CP, as sensory stimulation was the main goal of physical therapists. Results for the autism sample varied by participant and indicated that treatment needs to be individualized for optimal benefits. Findings support the use of MSE as an alternative technique to improve therapeutic opportunities for children with cerebral palsy by stimulating sensations that are otherwise generally dormant.https://epublications.marquette.edu/mcnair_2014/1000/thumbnail.jp