Application of non-thermal plasma for decontamination of thyme and paprika

The decontamination importance of spices as a widely used product in the world and the limitations of conventional decontamination methods conduct to find new and safe ways for microbial reduction of spices. Recently, cold plasma technology has been considered as a technique for disinfection in the food industry. In this study, microbial destruction of thyme and paprika through non-thermal plasma was investigated. The implemented method to create atmospheric cold plasma was dielectric barrier discharge (DBD). Plasma was applied to spices for 5 minutes. The results showed that by applying non-thermal plasma to thyme, total bacterial counts were reduced to 1.18 log cycle, but molds and yeasts were not changed. For paprika, considerable effects were not observed. It seems that cold plasma as an inexpensive, water-free and non-thermal method has the potential to reduce the bacterial contamination of thyme but further research is required to improve its effect. It is also possible to use DBD plasma treatment in combination with other technologies

Simulation and control of fan speed in a solar dryer for optimization of energy efficiency

In a forced convection solar dryer, the dryer efficiency is continuously changing during the drying process due to changes of solar radiation and temperature. So, it is important to use a control system to optimize energy efficiency based on changing drying factors. For this reason, a controller was designed, simulated and evaluated. In this research fan speed was simulated and controlled based on changing system variables accordingly to maintain the optimized efficiency. Fan speed was simulated by SIMULINK toolbar of MATLAB software. The dryer efficiency was determined by considering the mathematical relations and monitoring the air temperature in 3 positions: inlet and outlet of collector and outlet of drying chamber. All experiments were carried out in three replications. The current and optimized dryer efficiencies were calculated by using the control program. Results showed that the simulated model was capable of modeling fan speed. So, statistical analysis showed that the control system highly improved the dryer efficiency throughout its operation at probability level of 1%

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