Live Demonstration: A Portable Microsensor Fusion System with Real-Time Measurement for On-Site Beverage Tasting

Margarit, Josep Maria; Giménez-Gómez, Pablo; Escudé-Pujol, Roger; Gutiérrez-Capitán, Manuel; Jimenez-Jorquera, Cecilia; Liu, Shih‐Chii

doi:10.1109/iscas.2019.8702184

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Merging stablebut general-sensors like conductivity and ORP with more specific ones-but also more unreliable-like the ISFETs allows one to achieve a certain degree of operative independence with respect to unexpected ISFET failure or drift. This approach has already been reported in the literature (Giménez-Gómez et al, 2016;Gutiérrez-Capitán et al, 2019;Margarit-Taulé et al, 2019) as successfully improving the performance of electrochemical microsensor technologies in the chemometric analysis of water and wine.…”

Section: Chemical Sensors and Readoutmentioning

confidence: 79%

“…Detail of sensors and electrodes: 1× temperature Pt-100 (a), 2× Pt microelectrodes (ORP and conductivity, b), 2× reference electrodes (c), 6× ISFET microsensors (d). Adapted from Margarit-Taulé et al ( 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…In Margarit-Taulé et al ( 2019 ) we demonstrated a preliminary implementation of a portable electronic tongue analyzing temporal microsensor data via PLS-DA and SVMs. This work builds on these results to introduce SNNs as accurate and power efficient models to perform chemometric data fusion on the edge for liquid analysis.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays

et al. 2021

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Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor.

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Section: Chemical Sensors and Readoutmentioning

confidence: 79%

Section: Methodsmentioning

confidence: 99%