In terms of the low accuracy and unsatisfactory effect of traditional prediction models for consumption behavior, in the study of deep learning DNN model, a consumption behavior prediction model based on rDNN model is proposed. By choosing the appropriate function as the activation function of the model, the random sampling method is used to select negative samples of consumer behavior data to determine the N/P ratio, which improves the DNN model. Based on the improved DNN model, a consumer behavior prediction model based on the rDNN model is constructed. The results show that when the tanh function is used as the activation function and the ratio of N/P is 3, the rDNN model has the best prediction effect on consumption behavior, with AUC value of 0.8422 and the fastest operation efficiency of 434.36 s. Compared with traditional prediction models, DNN, and KmDNN deep learning models, the proposed model has more reliable prediction results and can be used to predict actual consumption behavior.

A high performance liquid chromatographic method for the determination of 28 exogenous medicines and endogenous components in the herbal drink was developed. The samples were extracted ultrasonically with methanol-water (70:30, v/v), and the extracts were separated in a Thermo Accucore C18 column (100 mm×4.6 mm, 2.6 μm) with methanol-acetonitrile-20 mmol/L ammonium acetate solution (pH 4.2) as the mobile phases by gradient elution. The flow rate was 1.2 mL/min and the column temperature was 35℃. The detection wavelengths were 254 nm and 220 nm. Quantification analysis was performed by the external standard method. The result showed the compounds had a good linear relationship in the range of 1-100 mg/L, and the correlation coefficients () were not less than 0.999. The limits of detection (LODs) of the 28 compounds were 1-10 mg/kg in the liquid sample and 20-200 mg/kg in the solid sample. The average recoveries of the 28 compounds in the liquid and solid samples were in the ranges of 88.8%-118.6% and 92.7%-112.3% with the relative standard deviations (RSDs) of 0.1%-6.7% and 0.1%-6.4%, respectively. The method was applied to analyze 456 herbal drink samples, and 55 positive samples were found. The positive rate was 12.1%. The developed method was simple and reliable, and it was suitable for the determination of 28 components in the herbal drink.

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