IoT-Based Cooking Oil Quality Monitoring System Using Thresholding Method on Android Application
Abstract
Repeated use of cooking oil can reduce its quality and have a negative impact on health, but these changes are difficult to recognize with the naked eye. This research aims to develop an Internet of Things (IoT)-based cooking oil quality monitoring system with a thresholding method connected to an Android application. The system is equipped with a TCS3200 sensor to detect color, an LDR sensor to measure clarity, and a pH sensor to monitor oil acidity. The readings from the three sensors are used to classify the oil quality into three categories : good, medium, and unfit. The final classification is determined using unified decision logic based on the majority values from the sensors. Tests were conducted on six oil samples with a reading frequency of 60 times per sample. Data was sent in real-time to firebase and displayed through an android app. In addition, the system sent automatic notifications via telegram for remote monitoring. The results show that one-time use oil is classified as good, 2 to 4 times use is moderate, and 5 times use is categorized as unfit for consumption. The system offers a practical and efficient solution for digital and real-time monitoring of oil quality.
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References
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