Artificial neural network techniques to predict the moisture ratio content during hot air drying and vacuum drying of Radix isatidis extract

Background: To predict the moisture ratio of Radix isatidis extract during drying. Methods: Artificial neural networks were designed using the MATLAB neural network toolbox to produce a moisture ratio prediction model of Radix isatidis extract during hot air drying and vacuum drying, where regression values and mean squared error were used as evaluation indexes to optimize the number of hidden layer nodes and determine the topological structure of artificial neural networks model. In addition, the drying curves for the different drying parameters were analyzed. Results: The optimal topological structure of the moisture ratio prediction model for hot air drying and vacuum drying of Radix isatidis extract were "4-9-1" and "5-9-1" respectively, and the regression values between the predicted value and the experimental value is close to 1. This indicates that it has a high prediction accuracy. The moisture ratio gradually decreases with an increase in the drying time, reducing the loading, initial moisture content, increasing the temperature, and pressure can shorten the drying time and improve the drying efficiency. Conclusion: Artificial neural networks technology has the advantages of rapid and accurate prediction, and can provide a theoretical basis and technical support for online prediction during the drying process of the extract.