Prediction of CO2 and ethylene produced in‐packaged apricot under cold plasma treatment by machine learning approach

In this study, the fresh and in-packaged apricots were treated with dielectric barrier discharge (DBD) cold plasma for 5, 10, and 15 min and then stored at 21 C for 12 days to simulate the shelf life of the apricot. The effects of DBD treatment on the main quality attributes of apricot such as physicochemical traits (mass loss, pH, soluble solid content, titratable acidity, and skin color), mechanical properties (Young's modulus, tangent modulus, and bioyield stress), in-package gas composition and ethylene production were investigated during the storage time. In addition, the bruise susceptibility (BS) of control and treated samples at the microscale was evaluated by using pendulum test and scanning electron microscopy. The results of the mass loss, pH, soluble solid content, titratable acidity, skin color, and bioyield stress have been applied for input parameters of the developed artificial neural network (ANN) and support vector regression (SVR) models to predict the CO2 and ethylene production. The statistical data showed the performance of the developed ANN to predict the CO2 (R2 = 0.983, root mean square error [RMSE] = 0.486) and ethylene production (R2 = 0.933, RMSE = 5.376) was superior to SVR (CO2: R2 = 0.894, RMSE = 6.077 and ethylene: R2 = 0.759, RMSE = 14.117). These results indicated that intelligent methods are effective and robust tools for predicting the quality parameters of fresh fruits in the postharvest process.