摘要
为提高卷烟配方模块的分类识别准确率,并为卷烟配方模块的科学评估提供技术支撑,提出了一种基于近红外光谱特征筛选的卷烟配方模块香型预测方法。选取2017—2019年238个卷烟配方模块样品的近红外光谱数据,结合特征工程中的递归特征消除法和BP神经网络、随机森林、XGBoost3种机器学习技术,构建了基于特征变量的香型预测模型。与全光谱数据训练的分类效果对比,经过递归特征消除法筛选后的光谱特征变量能够有效提升卷烟配方模块香型的识别准确率,其中,XGBoost算法分类效果最佳,模型对测试集的识别准确率达到了90.41%。结果表明,基于近红外光谱特征筛选的香型预测方法对卷烟配方模块的快速定位、科学评价及卷烟配方设计等有一定的辅助决策作用。
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 module samples from 2017 to 2019 were selected to construct an aroma prediction model based on feature variables 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 classification effect of full spectrum data training,the spectral feature variables filtered by recursive feature elimination 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.
作者
王林
郑明明
王翀
吴庆华
崔南方
李建斌
WANG Lin;ZHENG Mingming;WANG Chong;WU Qinghua;CUI Nanfang;LI Jianbin(China Tobacco Industrial Co.Ltd.at Hubei Province,Wuhan 430040,China;College of Management,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《华中农业大学学报》
CAS
CSCD
北大核心
2024年第1期226-231,共6页
Journal of Huazhong Agricultural University
基金
湖北中烟工业有限责任公司科技项目(2021JCYL3JS2B022)。
关键词
烟叶
香型
近红外光谱
递归特征消除
随机森林
XGBoost
tobacco
aroma type
near infrared spectroscopy
recursive feature elimination
random forest
XGBoost
