Method for intelligent quality evaluation of instant green tea based on multi-source data fusion and use thereof

A method for intelligent quality evaluation of instant green tea based on multi-source data fusion, comprising:

• step 1, selecting instant green tea as a raw material;
• brewing one portion of the instant green tea with boiling water to form a tea infusion, while keeping another portion of the instant green tea as a tea powder;
• using a computer vision device to collect visual images of the tea infusion and the tea powder;
• using an electronic nose to acquire aroma information of the tea infusion and the tea powder;
• using an electronic tongue to collect taste information of the tea infusion;
• and applying high-performance liquid chromatography to measure information of quality components in the tea infusion and the tea powder;
• step 2, performing dimensionality reduction separately on the aroma information, the visual images, and the taste information that are used as data of feature parameters;
• after the dimensionality reduction, dividing a dataset;
• determining quality indicators of the tea infusion and the tea powder;
• and performing correlation analysis on the quality indicators and the feature parameters to form a fused feature set;
• step 3, selecting features from the fused feature set formed in step 2 to form an optimal feature subset;
• constructing, based on the optimal feature subset, regression and classification models and a prediction model to predict contents of the quality indicators of the instant green tea, and using indicators of classification accuracy rate, R2 value of training and prediction sets, root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and relative prediction deviation (RPD) as evaluation criteria;
• and
• step 4, applying data fusion at a decision level and a feature level based on the optimal feature subset formed in step 3;
• applying a multiple linear regression model and Dempster-Shafer (D-S) evidence theory in a model architecture at the decision level and introducing a feature selection method in a model architecture at the feature level to perform multi-source fusion on the data separately;
• deriving fusion models based on evaluation results;
• for variety identification of the instant green tea, applying the model architecture at the decision level;
• for quality prediction of the instant green tea, applying the model architecture at the feature level;
• and using the R2 value, the RMSEC, the RMSEP, and the RPD as evaluation indicators for evaluation so as to select an optimal fusion model.