Monitoring Moisture Content for Various Kind of Tea Leaves in Drying Processes Using RF Reflectometer-Sensor System

You, Kok Yeow; Lee, Chia Yew; Chan, Kok San; Lee, Kim Yee; Cheng, E. M.; Lee, Yeng Seng

doi:10.3390/instruments2030018

Cited by 9 publications

(2 citation statements)

References 18 publications

(23 reference statements)

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“…The moisture content is mainly detected using the direct or indirect method (Chen, Zhan, Zheng, & Jin, 2013;Wu et al, 2018;You et al, 2018). In this study, we used the 120 C rapid moisture determination method (direct method) to detect the moisture content in the tea samples.…”

Section: Test Designmentioning

confidence: 99%

Variation law and prediction model to determine the moisture content in tea during hot air drying

Duan

Zhao

et al. 2022

J Food Process Engineering

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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.

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Section: Test Designmentioning

confidence: 99%

Variation law and prediction model to determine the moisture content in tea during hot air drying

Duan

Zhao

et al. 2022

J Food Process Engineering

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“…At present, the MC detection technology used for processing tea is mainly divided into the direct method and the indirect method [2]. The direct method determines the MC of the sample by weighing the moisture loss of the sample at a certain temperature [3,4].…”

Section: Introductionmentioning

confidence: 99%

Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient

Yin

Wang

et al. 2023

Agriculture

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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.

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Application of Computing Techniques in Monitoring Black Tea Processing for Improved Quality: Review and Future Directions

Kimutai,

Ngenzi,

Said

et al. 2023

Advances in Intelligent Systems and Computing

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Monitoring Moisture Content for Various Kind of Tea Leaves in Drying Processes Using RF Reflectometer-Sensor System

Cited by 9 publications

References 18 publications

Variation law and prediction model to determine the moisture content in tea during hot air drying

Variation law and prediction model to determine the moisture content in tea during hot air drying

Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient

Application of Computing Techniques in Monitoring Black Tea Processing for Improved Quality: Review and Future Directions

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