Prediction Method of the Moisture Content of Black Tea during Processing Based on the Miniaturized Near-Infrared Spectrometer

Abstract

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.

Development of Mussaenda frondosa sepal infused functional tea with enhanced antioxidant and alpha amylase inhibitory activities

“…Proximate analysis of tea samples is important for determining their nutritional quality. The moisture content of tea should be between 3 and 5% [24]. proximate compositions of the MFT-40 and black tea samples were given in Table S2 (supplementary information).…”

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

“…Li et al established an LS-SVM model for the quantitative prediction of eight individual catechins during the black tea drying process, based on near-infrared spectroscopy (NIRS) and chemometric methods such as CARS and SPA [ 18 ]. Based on a miniature near-infrared spectrometer, Zou et al built an SVR model optimized by the grey wolf algorithm to accurately measure the moisture content of black tea, and the prediction accuracy of the nonlinear SVR model was significantly higher than that of the linear PLS model [ 19 ]. Ze et al quickly determined tea polyphenols and other compounds in Pu’er tea using an improved weighted partial least squares algorithm [ 20 ].…”

Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient

“…The disadvantage is that the determination period is long, and the determination process is destructive to the sample, so it is only widely used in a laboratory setting. There has been much research into the indirect method, including capacitance [5], machine vision [6,7], near-infrared spectroscopy [8,9], hyperspectral [10], microwave, and so on. The capacitance method needs to be calibrated before each measurement, and the measured material needs to be in full contact with the electrode plate to ensure accurate measurement.…”