Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible-Near-Infrared Spectroscopy and Image-Processing Technologies.

During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible-near-infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea.

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing.

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3-4-1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

Time series prediction of insect pests in tea gardens.

BACKGROUND: Tea-garden pest control is crucial to ensure tea quality. In this context, the time-series prediction of insect pests in tea gardens is very important. Deep-learning-based time-series prediction techniques are advancing rapidly but research into their use in tea-garden pest prediction is limited. The current study investigates the time-series prediction of whitefly populations in the Tea Expo Garden, Jurong City, Jiangsu Province, China, employing three deep-learning algorithms, namely Informer, the Long Short-Term Memory (LSTM) network, and LSTM-Attention. RESULTS: The comparative analysis of the three deep-learning algorithms revealed optimal results for LSTM-Attention, with an average root mean square error (RMSE) of 2.84 and average mean absolute error (MAE) of 2.52 for 7 days' prediction length, respectively. For a prediction length of 3 days, LSTM achieved the best performance, with an average RMSE of 2.60 and an average MAE of 2.24. CONCLUSION: These findings suggest that different prediction lengths influence model performance in tea garden pest time series prediction. Deep learning could be applied satisfactorily to predict time series of insect pests in tea gardens based on LSTM-Attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. © 2024 Society of Chemical Industry.

White-Light GaN-µLEDs Employing Green/Red Perovskite Quantum Dots as Color Converters for Visible Light Communication.

GaN-based µLEDs with superior properties have enabled outstanding achievements in emerging micro-display, high-quality illumination, and communication applications, especially white-light visible light communication (WL-VLC). WL-VLC systems can simultaneously provide white-light solid-state lighting (SSL) while realizing high-speed wireless optical communication. However, the bandwidth of conventional white-light LEDs is limited by the long-lifetime yellow yttrium aluminum garnet (YAG) phosphor, which restricts the available communication performance. In this paper, white-light GaN-µLEDs combining blue InGaN-µLEDs with green/red perovskite quantum dots (PQDs) are proposed and experimentally demonstrated. Green PQDs (G-PQDs) and red PQDs (R-PQDs) with narrow emission spectrum and short fluorescence lifetime as color converters instead of the conventional slow-response YAG phosphor are mixed with high-bandwidth blue InGaN-µLEDs to generate white light. The communication and illumination performances of the WL-VLC system based on the white-light GaN-based µLEDs are systematically investigated. The VLC properties of monochromatic light (green/red) from G-PQDs or R-PQDs are studied in order to optimize the performance of the white light. The modulation bandwidths of blue InGaN-µLEDs, G-PQDs, and R-PQDs are up to 162 MHz, 64 MHz, and 90 MHz respectively. Furthermore, the white-light bandwidth of 57.5 MHz and the Commission Internationale de L'Eclairage (CIE) of (0.3327, 0.3114) for the WL-VLC system are achieved successfully. These results demonstrate the great potential and the direction of the white-light GaN-µLEDs with PQDs as color converters to be applied for VLC and SSL simultaneously. Meanwhile, these results contribute to the implementation of full-color micro-displays based on µLEDs with high-quality PQDs as color-conversion materials.