A portable device based on an electrical conductivity sensor for the detection of monosodium glutamate (MSG) in soupy foods
Bibliographic record
Abstract
In everyday life, food is made with various taste images, including the savory taste obtained from monosodium glutamate (MSG). Although it is allowed as a food ingredient, excessive use of MSG and continuous consumption will have adverse effects on health. The effects of consuming MSG excessively on the body's organs include brain, ovarian, testicular, liver, kidney, heart, and respiratory disorders. MSG detection using technology is needed to control the consumption of MSG in the body. Hence, this study was focused on creating a portable device for detecting monosodium glutamate (MSG) in soupy foods: meatball soup and chicken soup. 3 mg of MSG was applied to each solution sample and four MSG brands, including SS, MW, AM, and MR. The concept of this prototype is based on the conductivity value and total dissolved solids (TDS) in the MSG solution. These solutions can produce an electrolyte because there is a sodium salt content in the MSG solution and water. The electrolyte solution can conduct electricity. An electrical conductivity sensor was installed in this prototype and these sensors included two electrode plates, a positive electrode (anode) and a negative electrode (cathode) with a distance of ± 1 cm. The conductivity sensor begins by starting a sensor into an electrolyte solution and, given an electric voltage input, changes to the value of the electric voltage. The sensor can be read and detect the MSG level in the solution. Then, the data are received and recorded in the DRF analog which is to process the signal from the sensor into analog form, and the data is sent to the Arduino Nano as a microcontroller. From the experiment result, the average conductivity value and TDS value of meatball soup are 21.98 mS and 7.91 Mg/I, while the average conductivity value and TDS value of Chicken Soto are 16.92 mS and 6.08 mg/I. This prototype was successfully created and implemented for the MSG detection in soupy foods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".