Design and Validation of a Blood-Salt Level Analysis Meter

Ramaswamy, Harshini; Prabhu, Prasidha; C, Sai Kavya Neharika M; Rajakannu, Subashini

doi:10.1109/delcon54057.2022.9753648

Cited by 1 publication

(1 citation statement)

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“…However, in the measurement of conductivity for determining food quality, the salt concentration is of high interest because of its cost-effective nature, and simple integration and is applicable for solid foods too [27]. In a few reported works, conductivity has been utilized to measure salt content in cured ham [24], in-mouth salt release [28], blood [29], and water quality [30]. The work is limited by either using high-end technology or two electrodes technique.…”

Section: Introductionmentioning

confidence: 99%

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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