To study the changes in the moisture content of tea leaves during the hot air drying process of tea production, this study designed an experiment to monitor the dynamic change of moisture content of rolled green tea during the hot air drying process under different feeding amounts (800-1,200 g), drying temperature (90-120 C), and drum speed (20-30 rpm/min). We established a dynamic prediction model to determine the moisture content using the BP, Elman, and Particle swarm optimization Elman neural network (PSO Elman) algorithms. The prediction calculation was conducted by considering the drying temperature, drum speed, drying initial water, and prediction time as input and the water content prediction result during the tea drying process as the output. Additionally, we analyzed the significant factors to explore the dynamic changes in the water content of tea leaves under different drying conditions.Experimental results showed that the temperature, rotational speed, and feeding rate significantly affected the drying effect. Furthermore, we established a traditional multiple linear regression fitting model was established to compare and analyze with the above three neural network models. The verification and error analysis results of the four water prediction models showed that the PSO-Elman model could better predict the changes in the water content during the drying process. Practical ApplicationsThe change of moisture content in the process of tea hot air drying research can provide a theoretical basis for the hot air drying technology and process of tea, and have important significance for guiding tea processing and production, improving processing efficiency and tea quality.
The rapid and nondestructive detection of tea leaf moisture content (MC) is of great significance to processing tea with an automatic assembly line. This study proposes an MC detection method based on microwave scattering parameters (SPs). Through the established free-space electromagnetic measurement device, 901 different frequency points are taken between 2.45 and 6 GHz using a vector network analyzer (VNA). The SPs of tea leaves with different moisture contents (5.72–55.26%) at different bulk density and different sample thicknesses were measured. The relationship between frequency, S21 amplitude and moisture content, thickness, and bulk density of tea was analyzed using correlation coefficients, significance analysis, and model construction. Back propagation (BP) neural network, decision tree (DT), and random forest (RF) MC prediction models were established with the frequency, amplitude, and phase of the SPs, thickness, and bulk density of the samples as inputs. The results showed that the RF-based model had the best performance, with determination coefficient (R2) = 0.998, mean absolute error (MAE) = 0.242, and root mean square error (RMSE) = 0.614. Compared to other nondestructive testing processes for tea, this method is simpler and more accurate. This study provides a new method for the detection of tea MC, which may have potential applications in tea processing.
This study aimed to investigate the water dissipation pattern from peanut pods under natural drying conditions after harvest. The Shandong peanut Luhua 22 was used to examine the effects of varying moisture content, bulk density, and porosity on the relative permittivity of the peanut at a signal frequency of 5.8 GHz. The peanut dielectric constant, porosity, and bulk density were used as inputs and peanut kernel moisture as outputs. Support vector regression (SVR), extreme learning machine (ELM), sparrow search algorithm-support vector regression (SSA-SVR), and sparrow search algorithm-extreme learning machine (SSA-ELM) were used to create a prediction model of peanut kernel moisture content. The results show that the water content of peanut kernels decreased in a fast and then slow manner throughout the drying process and that the water content of kernels was stable at 5–8% at the end of drying. The relative permittivity of peanut kernels increased with an increase in the water content and bulk density but decreased with an increase in porosity. The developed SVR, ELM, SSA-SVR, and SSA-ELM water-content prediction models were validated and analyzed in this study, with the model test set coefficients of determination of 0.936, 0.949,0.984, and 0.994, respectively. In comparison to SVR, ELM, and SSA-SVR, the SSA-ELM root mean square error was reduced by 0.0080, 0.0060, and 0.0012, respectively. According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. This prediction model provides a theoretical foundation for the variations in peanut seed moisture content during the natural drying process after harvesting peanuts in Shandong, which will be useful for future peanut storage and transportation.
In this study, a novel Vivaldi antenna with dimensions of 100 mm × 85 mm × 1.6 mm, designed for a moisture measurement system, is built to enhance the gain of conventional Vivaldi antennas in the low-frequency band to suit the needs of moisture detection. The fence structure and choke slot are modified to enhance the antenna’s radiation properties in the low-frequency band, and simulation is performed to determine how different structural parameters affect the antenna’s performance. The results show that in the frequency band of 5-6 GHz, the voltage standing-wave ratio (VSWR) of the antenna is less than 2, and the gain at 5.8 GHz reaches 16.2 dBi after installing the lens. Compared with conventional unmodified Vivaldi antennas, the gain at 5.8 GHz increases by approximately 6.11 dBi. The antenna is then processed and measured, and the measured results are in good agreement with the simulated results; hence, the antenna can be widely used in the field of moisture detection.
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