Design and detection method of a four electrodes cross-conductive sensor for fluid conductivity measurement

Hussain, Saddam; Ali, Sayed Afzal; Islam, Tarikul; Mahboob, Md. Rahat

doi:10.1007/s00339-023-06577-2

Cited by 1 publication

(2 citation statements)

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“…The materials were selected such that the sensor including the circuit can be fabricated at a low cost. A detailed investigation of the design criterion that includes the size of the sensor, the material of the electrode, the electrode gap, the thickness of the sensor, and the effect of shielding is reported in our recent work [38]. To keep the prototype system inexpensive, the sensor and the circuit were integrated on a copper cladded PCB.…”

Section: Design and Simulationmentioning

confidence: 99%

“…Sensor S1 yields a conductivity value of 49.95 mS/m with an error of 0.10% whereas sensor S2 results in only 0.06% error with respect to the standard conductivity value. The comparatively high error for sensor S1 is due to the fringing field effect which gets minimized in S2 through shielding [38]. Hence, sensor S2 structure was fabricated for experimental validation of the theory.…”

Section: Design and Simulationmentioning

confidence: 99%

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An Intelligent Food Salt Tester Using Cross-Conductive Sensor

Ali

Mahboob

Islam

2023

IEEE Open J. Instrum. Meas.

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Salt is a mineral used in various industries/foods to fulfil different needs. Its utilization for food preparation is most common and its unmonitored consumption leads to many diseases. In this article, two sensors S1 and S2 have been designed and simulated using COMSOL MULTIPHYSICS 5.6 software to measure the salt concentration of various salt samples. The performances of the sensor structure have been evaluated by varying geometrical parameters and electrode materials. The sensor works on the extension of Thompson and Lampard Theorem, a well-known principle used to fabricate a primary standard of capacitor. Sensor S2 is fabricated, and experiments are conducted with various concentrations of salt samples. The experimental results closely match the results of the simulation and the commercial conductivity meter. A highly linear response with a sensitivity of 1.87 mV/(mg/100 ml) and with an average repeatability index of ±0.17% is observed. Furthermore, the Gaussian Naïve Bayesbased machine learning algorithm has been used to categorize salt samples into eight different salt taste types. The data for salt concentration samples are generated using 100 volunteers. The machine learning algorithm achieved an accuracy of 93.33% for predicting salt taste type.

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Section: Design and Simulationmentioning

confidence: 99%

Section: Design and Simulationmentioning

confidence: 99%