Deteksi Adulterasi Daging Babi Pada Patty Sapi Menggunakan Electronic nose Berbasis Sensor MQ5 dan MQ6

Abstract

The growing demand for halal products in Indonesia has not yet been fully met by guarantees of halal certification for processed meat products. Cases of pork adulteration are still found in products such as meat patties, without consumers being informed, thereby causing harm, particularly to Muslim consumers. Given these conditions, a rapid, objective, and efficient detection method is needed to identify the presence of pork. Rapid aroma detection in this study was performed using an electronic nose based on MQ5 and MQ6 sensors. The digital data displayed using CoolTerm software was processed using Microsoft Excel and analyzed using supervised learning-based artificial intelligence approaches, namely Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and the Random Forest model. The sensing process utilized heating at 70°C. Research indicates that the electronic nose can clearly distinguish aroma patterns across various adulteration concentration levels. This method has proven effective as a rapid test with a high level of accuracy, even at the lowest concentration (10%). Thus, the MQ-5 and MQ-6 sensor-based electronic nose can be used as an objective, fast, and efficient detection method to support the monitoring of food product halal compliance.