Classification and Concentration Prediction of VOC Gases Based on Sensor Array with Machine Learning Algorithms

In this work, a facile method is proposed to monitor the freshness of meat and fruits by combining a gas sensor array with machine learning algorithms, where the sensor array consists of four commercial metal oxide-based gas sensors. Back-propagation neural network (BPNN) is used for gas classification, and the average accuracy can reach 98.8%. To obtain more effective prediction of VOC gas concentration (ethanol, trimethylamine, and ammonia), four algorithms including BPNN, radial basis function neural network (RBFNN), support vector machine (SVM), and hybrid LDA-SVM, which is a combination of SVM and linear discriminant analysis (LDA) are implemented, which are trained with the same training set. By analyzing and comparing the prediction results of these four algorithm models, the RBFNN achieves the peak performance for the concentration predictions of ethanol and ammonia, and the average relative errors are less than 5% and 6.5%, respectively. For trimethylamine (TMA) concentration prediction, the average relative error of RBFNN is equal to 4.41%, which is better than 5.11% of SVM, while the mean absolute error of RBFNN is slightly inferior to SVM. Therefore, the classification accuracy of the gas type by BPNN and the prediction accuracy of gas concentration by RBFNN can meet the requirement of distinguishing the freshness of food.