Predicting aroma type of cigarette recipe module based on near infrared spectroscopy
A method for predicting the aroma type of cigarette recipe module based on near-infrared spectral feature dimensionality reduction was proposed to classify and identify the aroma type of cigarette recipe modules with near-infrared spectroscopy.The near-infrared spectral data of 238 cigarette recipe mod-ule samples from 2017 to 2019 were selected to construct an aroma prediction model based on feature vari-ables through combining the recursive feature elimination method in feature engineering and three machine learning techniques including BP neural network,random forest and XGBoost.Compared with the classifi-cation effect of full spectrum data training,the spectral feature variables filtered by recursive feature elimina-tion method effectively improved the recognition accuracy of aroma type of cigarette recipe module.Among them,the algorithm of XGBoost had the best classification performance,with a model recognition accuracy of 90.41%for the test set.It is indicated that the prediction method of aroma type based on the recursive feature elimination of near-infrared spectral features has a certain role in assisting decision-making in the rapid positioning,scientific evaluation and cigarette formulation design of cigarette recipe modules.
NETL
NSTL
万方数据
维普
