Regression prediction of tobacco chemical components during curing based on color quantification and machine learning

Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical components. In order to clarify the relationship between the changes of tobacco color and chemical components, here we established several prediction models of chemical components with the color values of tobacco based on machine learning algorithms. The results of correlation analysis showed that tobacco moisture content was highly significantly correlated with the parameters such as a^*, H^* and H°, the reducing sugar and total sugar content of tobacco was significantly correlated with the color values, and the starch content was highly significantly correlated with the color values except for b^* and C^*. The random forest models performed best in predicting tobacco moisture, reducing sugar, total sugar and starch constructed with the R² of the model validation set was higher than 0.90, and the RPD value was greater than 2.0. The consistent between the predictions and measurements verified the availability and feasibility using color values to predict some chemical components of the tobacco leaves with high accuracy, and which has distinct advantages and potential application to realize the real-time monitoring of some chemical components in the tobacco curing process.