Borax Detection Tool in Foodstuffs Using Color Recognition Detection Method on HuskyLens Camera

Abstract

The use of Borax as a food additive has adverse effects on public health. In an effort to prevent contaminated foodstuffs from becoming public consumption and to detect this contamination quickly and effectively, this research proposes the design of an Internet of Things (IoT)-based borax detection tool using color recognition technology from the HuskyLens Camera. This tool is designed to detect the presence of borax in food with high accuracy and in a short time. This research uses the HuskyLens Camera as a sensor that is sensitive to the target contaminant, and the data obtained from this sensor is transmitted through the IoT network to the MIT App Inventor platform. Optimized detection methods and data processing algorithms are used to accurately interpret the sensor results, providing authorities with immediate information on the safety status of the tested foodstuffs. Experimental test results show that the proposed detection tool has good performance in detecting borax in various types of foodstuffs. It provides an efficient and reliable solution to the challenge of food contamination. With the integration of IoT technology, the detector is able to provide real-time information to authorities, potentially raising awareness about overall food safety. Thus, the design of this IoT-based borax detector has great potential to overcome the problem of food contamination and make a significant contribution to maintaining public health.

Keywords

• huskylens
• borax
• artificial intelligence
• internet of things