The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy.

Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.

Moisture content (MC) is an important indicator to monitor the quality of Longjing tea during processing; therefore, it becomes more critical to develop digital moisture content detection methods for processing. In this study, based on a micro-near infrared (NIR) spectrometer and portable colorimeter, we used Longjing tea under the full processing process as the research object, and used competitive adaptive reweighted sampling (CARS) and a principal component analysis (PCA) to extract characteristic bands of spectral data as well as the principal component reduction processing of the color difference and glossiness data, respectively, combined with sensor data fusion technology to establish a quantitative prediction model of the partial least squares (PLS) for the moisture content of Longjing tea. The PLS quantitative moisture content prediction model, based on middle-level data fusion, obtained the best prediction accuracy and model robustness, with the correlation coefficient of the prediction set (${\mathrm{R}}_{\mathrm{p}}$) and the root mean square error of prediction (RMSEP) being 0.9823 and 0.0333, respectively, with a residual predictive deviation (RPD) of 6.5287. The results indicate that a data fusion of a micro NIR spectrometer and portable Colorimeter is feasible to establish a quantitative prediction model of the moisture content in Longjing tea processing, while multi-sensor data fusion can overcome the problem of a low prediction accuracy for the model established by single sensor data. More importantly, data fusion based on low-cost, fast, and portable detection sensors can provide new ideas and methods for real-time online detection in Longjing tea in actual production.