On-line Detection of Moisture Content of Green Tea Processing

The on-line detection of moisture content in green tea processing is important for stabilizing product quality. To address the shortcomings of traditional manual assessment such as low accuracy and poor consistency, this study proposed the use of on-line NIR spectroscopy to establish a rapid, accurate and quantitative method for the detection of moisture content in green tea processing. 415 samples of green tea leaves from multiple seasons and batches were collected as research materials, and the NIR spectral signals of green tea leaves were collected in real time by an on-line NIR spectrometer. The true moisture content of green tea leaves was subsequently determined by the traditional drying method. Preprocessing methods such as Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and Detrending were used to eliminate the noise in raw spectra. The genetic algorithms (GA) and Competitive Adaptive Reweighted Sampling (CARS) were compared for the selection of moisture-related characteristic wavelengths. Partial Least Squares regression (PLS) algorithm was then employed to develop PLS, GA-PLS and CARS-PLS quantitative detection models for moisture content. The results showed that the spectral preprocessing could reduce the noise interference in the raw signal, and significantly improve the prediction accuracy of the models, among which SNV achieved the optimal performance. Both GA and CARS could effectively reduce the data dimensionality, among which CARS achieved the optimal prediction performance, the prediction accuracy of the proposed SNV-CARS-PLS model for the samples in prediction set performed with Rp=0.9402 and RMSEP=1.57%. The research established an on-line method for the detection of moisture content of green tea during fixation, which could provide real-time quantitative feedback on the quality condition of the fixation process, and also provide an important basis for the intelligent processing of green tea.