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

Abstract

Monosodium 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%.