Influence of raw material moisture on the synthesis of black tea production process

Vargas, Rafael; Vecchietti, Aldo

doi:10.1016/j.jfoodeng.2015.10.043

Cited by 19 publications

(10 citation statements)

References 21 publications

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“…The processing steps of black tea involve harvesting, withering, leaf crushing, fermentation and drying and sieving (Vargas and Vecchietti 2016). However, the unique taste of black tea forms only during fermentation.…”

Section: Introductionmentioning

confidence: 99%

Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea

Chen

Liu

et al. 2018

J Food Sci Technol

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Fourier transform near-infrared spectroscopy (FT-NIR) coupled to chemometric algorithms such as back propagation (BP)-AdaBoost and synergy interval partial least square (Si-PLS) were deployed for the rapid prediction taste quality and taste-related components in black tea. Eight main taste-related components were determined via chemical analysis and Pearson correlations. The achieved chemical results of the eight taste-related components in black tea infusion were predicted based on 160 tea samples obtained from different countries. Prediction results revealed BP-AdaBoost models gave superior predictions, with all the correlation coefficients of the prediction set (R) > 0.76, and the root mean square error values of the prediction set (RMSEP) < 1.7% compared with Si-PLS models (0.71 ≤ Rp ≤ 0.94, 0.08% ≤ RMSEP ≤ 1.73%). This implies that FT-NIR combined to BP-AdaBoostis capable of being deployed for the rapid evaluation of black tea taste quality and taste-related components content simultaneously.

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Section: Introductionmentioning

confidence: 99%

Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea

Chen

Liu

et al. 2018

J Food Sci Technol

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“…With the decrease of moisture content during the withering process, the chemical components existed in the fresh leaves are also changing. For example, polyphenols were degraded, with the total amount reduced and oxidation products continuously increased with the strengthening of the withering 3 , 6 ; under the effect of chlorophyllase, chlorophylls were hydrolyzed to chlorophyllide (Cda, Cdb) and phytol, which induced the changes of colors of tea leaves 2 . Meanwhile, the constantly losing moisture content of the fresh leaves may cause cell collapse and structural damages, and further lead to morphological changes and shrinkage of the leaves 8 , 23 .…”

Section: Results and Analysismentioning

confidence: 99%

“…When the stems and leaves are withering, the luster on the leaf surface is fading away, the color of leaves is turning to dark green, and the grass flavor is vanishing 4 . The moisture loss rate of fresh leaves is usually taken as the quantitative indicator to determine a proper withering 5 , 6 . When the moisture content is about 58~62% 7 , the withering is considered as a proper withering.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Liang

Dong

et al. 2018

Sci Rep

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Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

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“…Traditional methods for detecting moisture content in green tea, including the constant temperature oven method and rapid moisture determination method were accurate, but time‐consuming, destructive, and cumbersome operation (Vargas & Vecchietti, ). Currently, spectroscopy technology had been used for its simple structure and high‐speed detection (Panigrahi et al, ; Sinija & Mishra, ).…”

Section: Introductionmentioning

confidence: 99%

Nondestructive detection for moisture content in green tea based on dielectric properties and VISSA‐GWO‐SVR algorithm

Sun

Tian

et al. 2020

J Food Process Preserv

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Moisture content was an important indicator to measure quality of green tea. In order to detect moisture content in green tea effectively and accurately, nondestructive detection for moisture content in green tea based on dielectric technology was proposed in this paper. Inductance-capacitance-resistance (LCR) measuring instrument and coaxial-line cylinder capacitor were used to collect dielectric data. The characteristic frequency points were extracted by successive projection algorithm (SPA) and variable iterative space shrinkage approach (VISSA). Support vector regression (SVR) was used to establish prediction models based on full frequency points and characteristic frequency points. The model results demonstrated that VISSA-SVR model based on dielectric loss factor ε″ performed best among all the prediction models, but the prediction accuracy was not enough, so the gray wolf optimization (GWO) algorithm was introduced to optimize the parameters (c and g) in SVR model. Furthermore, the best prediction performances for detecting moisture content in green tea was obtained, with the determination coefficient and root mean square errors (RMSEs) for prediction were 0.9695 and 0.0602, respectively. Therefore, dielectric technology combined with VISSA-GWO-SVR model is feasible for nondestructive determination of the moisture content in tea, which will provide a promising tool for the moisture content detection of other agricultural products. Practical applicationsWell understanding moisture content in tea is great importance. The practical application of this paper is to develop a novel method for moisture content detection in tea using dielectric technology. Compared with traditional methods, dielectric technology can be used to detect moisture content in tea nondestructively and accurately. Characteristic frequency selection algorithms are used to remove the redundant information in the data, and optimization algorithm is used to improve the performance of the model. Thus, dielectric technology combined with the optimal model is considered the most promising method for detecting the moisture content in green tea.How to cite this article: Sun J, Tian Y, Wu X, et al.Nondestructive detection for moisture content in green tea based on dielectric properties and VISSA-GWO-SVR algorithm. J Food Process Preserv. 2020;44:e14421.

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Influence of raw material moisture on the synthesis of black tea production process

Cited by 19 publications

References 21 publications

Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea

Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Nondestructive detection for moisture content in green tea based on dielectric properties and VISSA‐GWO‐SVR algorithm

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