An Approach for in-Line Control of Moisture Content During Green Tea Processing

Zhao, Zhijia; Chen, Lun; Chen, Guoda; Xie, Weiyue; Peng, Jiyu

doi:10.1109/access.2020.2983134

“…In addition, drying preserved the phytochemical constituents of tea leaves [14]. Previous report showed that high moisture content supported microbial growth and triggered the deterioration of tea phytochemicals [21]. Wang et al [22] reported that a decrease in moisture content tea and enhanced tea aroma.…”

Section: Resultsmentioning

confidence: 95%

Polyphenols and Antioxidant Activity of Thunbergia laurifolia Infused Tea under Drying Conditions

Essiedu

¹

,

Gonu

²

,

Adadi

³

et al. 2023

Journal of Food Quality

4

0

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Thunbergia laurifolia leaf is used in Thai herbal medicine to moderate alcohol, food poisoning, and other health-related diseases mainly due to its overwhelming phytochemical compounds which exert several biological functions such as antioxidant, and anti-inflammatory properties, among others. This study investigated the potential effects of hot air-drying conditions (TL-D80°C, TL-D90°C, and TL -D100°C) of T. laurifolia tea leaves on phenolic compounds, total flavonoid content (TFC), total phenolic content (TPC), and antioxidant activities (AOA) of the infused teas. The results show that an increase in drying temperature significantly p < 0.05 improved TPC (709.7 ± 1.36–744.8 ± 5.79 mg GAE/) and TFC (198.98 ± 7.59–207.16 ± 4.10 mg RE/L) of infused teas. TL-D80°C (69.9 ± 0.95%) and TL-D90°C (69.3 ± 0.7%) infused teas showed significantly p < 0.05 higher DPPH inhibitory effect compared to TL-D100°C. Treatment had no effects p > 0.05 on ABTS.+ scavenging activity. The phenolic compounds detected in infused teas were rosmarinic acid, caffeic acid, gallic acid, catechin, rutin, and quercetin. Regarding, the cumulative phenolic compounds TL-D100°C infused teas were significantly higher p < 0.05 compared to TL-D90°C and TL-D80°C. The results suggest that drying conditions (i.e., TL-D100°C within 30 min) could be used to achieve appropriate moisture content of T. laurifolia tea leaves without compromising the phytochemical compositions and antioxidant potentials of the resulting infused teas.

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“…The MC of the tea samples was 5.72% according to the drying method; they were divided into several portions of 2 kg each and placed in a sealed bag for use. Since the MC of tea before drying is approximately 55%, the MC range was set to 5.7-55.2% [20,21] and the MC gradient was set to about 2%. The MC formula was used to calculate the amount of distilled water that needed to be sprayed on each sample [22].…”

Section: Sample Preparationmentioning

confidence: 99%

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

Yin

¹

,

Ma

²

,

Wang

³

et al. 2023

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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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“…Tea production involves multiple processing procedures, drying being is the last process. Owing to the loss of water and consolidation of the shape of the tea leaves, subtle reactions occur in the internal components of tea, which is essential to develop color, aroma, and taste (Li, Ai, Yu, Chen, & Ni, 2019;Ye, Gong, Gui, Teng, & Gao, 2019;Zhao, Chen, Chen, Xie, & Peng, 2020). Because traditional tea drying processes are based on the subjective judgment of workers and masters to ensure high tea quality, it lacks stability.…”

Section: Introductionmentioning

confidence: 99%

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

Duan

¹

,

Ma

²

,

Zhao

³

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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An Approach for in-Line Control of Moisture Content During Green Tea Processing

Cited by 5 publications

References 27 publications

Polyphenols and Antioxidant Activity of Thunbergia laurifolia Infused Tea under Drying Conditions

Polyphenols and Antioxidant Activity of Thunbergia laurifolia Infused Tea under Drying Conditions

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

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

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