Prediction of TVB-N content in eggs based on electronic nose

Total volatile basic nitrogen (TVB-N) content is an important freshness index of egg. An electronic nose was used to distinguish room-temperature storage periods of eggs by means of principal component analysis (PCA). The loadings plot analysis was used to identify the sensor responses as input parameters of support vector regression (SVR) model. Responses of sensor array in electronic nose were employed to establish TVB-N content model able to describe egg storage periods. Results showed that the E-nose could distinguish eggs of different storage periods by PCA. The optimum SVR kernel function was selected as Gaussian kernel by simulation. The optimum SVR inner parameters of the penalty parameter C, the radius ε and the width parameter δ2 were studied and set at 25, 2−3 and 22 by the grid searching method. The simulation results demonstrated that the SVR model could achieve better accuracy and generalization than the back-propagation neural network (BPNN). The experimental results showed that the average prediction accuracy, RMSE and MRE of the TVB-N content prediction SVR model were achieved as 94.62%, 0.09% and 0.05% respectively, which implied that the E-nose was effective for TVB-N content prediction in eggs by the SVR model.