Publication

Abstract : Ensuring food safety is crucial for health and economic sustainability. A key indicator of food quality is its smell, which can be assessed using an electronic nose (e-nose). Combining e-nose technology with machine learning methods has shown promise in enhancing classification accuracy. However, traditional methods such as Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN) often fall short in accurately distinguishing between fresh and spoiled beef. To address these limitations, this study proposes a novel hybrid model that integrates Random Forest, CNN for local feature extraction, and Gated Recurrent Unit (GRU) for global feature extraction. Utilizing data from 11 e-nose sensors, this model achieves an unprecedented accuracy of up to 0.9977 in differentiating fresh from spoiled beef across all 12 types of cuts. The hybrid model demonstrates superior performance metrics, including accuracy, precision, recall, F1 score, coefficient of determination (R2), root mean square error (RMSE), and residual predictive deviation (RPD), significantly outperforming traditional approaches in beef quality classification. By incorporating both local and global features, our approach offers innovative insights and marks a significant advancement in machine learning applications for food quality assessment.