Machine learning-based characteristic identification of MSG content in gravy foods

\nMonosodium Glutamate (MSG) is a sodium salt that binds to amino acids in the form of glutamic acid, widely used as an additive in cooking as a flavoring. Therefore, this research aims to detect the level of MSG content in soupy foods using Machine Learning. This research determines the identification of MSG using the Machine Learning method Naive Bayes classifier algorithm in Python software. This tool determines the identification of MSG dissolved in water using a Photodioda sensor, push button, RGB LED, Arduino Nano and Resistor. From the research obtained the results that the color of the light source affects the sensor reading value. Sensor value readings based on different light sources have the same pattern, but different values. The difference in sensor value is caused by the effect of LED color on specimen color. The more MSG used, the greater the photodiode sensor reading value. Based on this research, the accuracy value is 83.6%.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-01-09
DOI
https://doi.org/10.1051/bioconf/202414804019/pdf
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
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article

Machine learning-based characteristic identification of MSG content in gravy foods

Springer Link (Chiba Institute of Technology)
Advanced Chemical Sensor Technologies
article

Machine learning-based characteristic identification of MSG content in gravy foods

article en

Abstract

\nMonosodium Glutamate (MSG) is a sodium salt that binds to amino acids in the form of glutamic acid, widely used as an additive in cooking as a flavoring. Therefore, this research aims to detect the level of MSG content in soupy foods using Machine Learning. This research determines the identification of MSG using the Machine Learning method Naive Bayes classifier algorithm in Python software. This tool determines the identification of MSG dissolved in water using a Photodioda sensor, push button, RGB LED, Arduino Nano and Resistor. From the research obtained the results that the color of the light source affects the sensor reading value. Sensor value readings based on different light sources have the same pattern, but different values. The difference in sensor value is caused by the effect of LED color on specimen color. The more MSG used, the greater the photodiode sensor reading value. Based on this research, the accuracy value is 83.6%.\n

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 57%
Advanced Chemical Sensor Technologies
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