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[学术文献 ] Regression-Based EGCG Detection in Green Tea Employing MIP Electrodes 进入全文
IEEE Sensors Journal 期刊
In this treatise, multiple tree-based ensemble regression machine learning (ML) approaches were used to estimate the epigallocatechin-3-gallate (EGCG) content (mg/g) in green tea samples. Co-polymerizing ethylene glycol dimethacrylate (EGDMA) with acrylonitrile (AN) yielded the molecularly imprinted polymer EGCG (MIP-EGCG) electrode. On imbibing the MIP-EGCG electrode with real samples, the differential pulse voltammetry (DPV) signatures were recorded to identify performance. The recorded dataset is subjected to decision trees (DTs) and random forest (RF) along with boosting methods namely adaptive boosting (AdaBoost), gradient boost, and extreme gradient boost (XGBoost). Further, hyperparameter adjustments were performed to construct a generalized model. The mean-squared error (mse), mean-squared logarithmic error (MSLE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination ( R2 ) are involved in evaluating the performance of the model. In the context of quantitative prediction of EGCG, the XGBoost algorithm performed well with the value of the coefficient of determination ( R2 ) being 0.99 and mse value of 0.0001.
[学术文献 ] Dissipation and processing factors of emamectin benzoate and tolfenpyrad in tea (Camellia Sinensis) 进入全文
Journal of Food Measurement and Characterization 期刊
A reliable analytical method for the simultaneous determination of emamect in benzoate and tolfenpyrad in fresh and dried tea (Camellia sinensis) leaves was developed. We determined the dissipation of the two pesticides in fresh tea leaves from Jiangsu, Hunan, Guangxi, and Fujian provinces (China) using different dissipation kinetic curves. We found that the optimal model for both compounds was the combined first + first-order kinetic in most cases. The half-lives of emamectin benzoate, calculated by the best-fit model, were 0.11–0.49 d, while that of tolfenpyrad were 1.12–1.85 d. This indicated that the two pesticides quickly dissipated in the fresh tea leaves. In order to eliminate the influence of water loss, we calculated the pesticide residues in fresh tea leaves based on dry weight and then calculated the processing factors in dry weight. The processing factors (dry weight) of emamectin benzoate and tolfenpyrad were found to be 0.23–0.98 and 0.18–0.67, respectively with loss rates of 1.6%–76.7%and 32.7%–82.1% determined for emamectin benzoate and tolfenpyrad residues during tea processing.
[学术文献 ] Residue behavior, transfer and risk assessment of tolfenpyrad, dinotefuran and its metabolites during tea growing and tea brewing 进入全文
Journal of the Science of Food and Agriculture 期刊
BACKGROUND：Tolfenpyrad and dinotefuran are two representative pesticides used for pest control in tea gardens. Their application may bring about a potential risk to the health of consumers. Therefore, it is essential to investigate the residue behavior, transfer and risk assessment of tolfenpyrad, dinotefuran and metabolites from tea garden to teacup. RESULTS：An effective analytical method was established and validated to simultaneously determine tolfenpyrad, dinotefuran and its metabolites (DN and UF) in tea. The average recoveries of tolfenpyrad, dinotefuran, DN and UF werein the range 72.1–106.3%, with relative standard deviations lower than 11.8%.On the basis of the proposed method, the dissipation of tolfenpyrad and dinotefuran in fresh tea leaves followed first-order kinetics models with half-lives of 4.30–7.33 days and 4.65–5.50 days, respectively. With application amounts of 112.5–168.75 g a.i. ha–1 once or twice, the terminal residues of tolfenpyrad and total dinotefuran in green tea were lower than 19.6 and 7.13 mg kg−1, respectively, and below their corresponding maximum residue limits . The leaching rates of tolfenpyrad and total dinotefuran during the tea brewing were in the ranges 1.4–2.3% and93.7–98.1%, respectively. CONCLUSION：Tolfenpyrad and dinotefuran in tea were easily degraded. The RQc and RQa values for tolfenpyrad were 37.6% and 5.4%, which were much higher than for dinotefuran at 24.7% and 0.84%, respectively. The data indicated that there was no significant health risk in tea for consumers at the recommended dosages. The results provide scientific data regarding the reasonable use of tolfenpyrad and dinotefuran aiming to ensure safe tea consuption.
[学术文献 ] Microscopic Insect Pest Detection in Tea Plantations: Improved YOLOv8 Model Based on Deep Learning 进入全文
Agriculture-Basel 期刊
Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model’s learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan’s ecological tea industry and ensures the healthy growth of the tea industry.
[相关专利 ] METHOD FOR AUTOMATICALLY DETECTING PESTICIDE RESIDUE IN TEA LEAVES 进入全文
世界知识产权组织
一种自动化检测茶叶中农药残留的方法，包括如下步骤：(1)制备样品粗提液；(2)将步骤(1)所得粗提液在自动净化装置中净化，收集样品流出液和洗脱液；(3)利用超高效液相色谱-串联四级杆质谱对步骤(2)获得的样品流出液和洗脱液进行检测，分析出茶叶中农药的残留情况。该方法能在显著降低或消除基质效应的同时，使用更少的有机溶剂、产生更少的实验室有害废弃物、获得更快分析速度、实现更好实验效率。
[会议论文 ] Linear Regression Modelling on Epigallocatechin-3-gallate Sensor Data for Green Tea 进入全文
ICRCICN 会议
In this paper, linear regression machine learning techniques are applied to determine the quality of green tea samples. The data set is obtained by applying Differential Pulse Voltammetry (DPV) on green tea samples using Epigallocatechin-3-gallate (EGCG) specific sensor based on Molecular Imprinted Polymer (MIP) technique. Multiple linear regression models have been developed using this dataset that gives more hidden insight of the dataset and helps to find the input feature importance out of it. Regularization techniques are applied on linear regression like Ridge regression (L2 Penalty), Lasso regression (L1 Penalty) and ElasticNet regression (combination of L1 and L2 Penalty) considered to reduce overfitting of the model and to provide better prediction. The variation of cross validation score vs regularization parameter for different regularized techniques of linear regression are also taken under consideration and best value of the regularization parameter is calculated to develop the model for getting better prediction with high accuracy. From the result obtained from model metrics, a clear picture is portrayed how lasso regression performs better than ridge regression for this dataset and eliminates the less important features to develop the model as sparsity can be useful in practice if we have a high dimensional dataset with many features that are not effective for modelling. The beauty of ElasticNet Regression model is also highlighted how both L1 and L2 penalty go hand in hand to give prediction at a high accuracy.
