Numerous applications have been documentedfor electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for enose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs)-reliefF, chi-square, and Gini index-for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.