烟草科技2024，Vol.57Issue(12)：74-83,10.DOI:10.16135/j.issn1002-0861.2024.0151
基于太赫兹光谱检测技术的烟丝掺配比例识别模型
A model to identify cut tobacco blending ratio based on terahertz spectroscopy
摘要
Abstract
To accurately identify the blending ratios of strips and stems in cut tobacco blends,terahertz spectroscopy combined with machine learning was applied to study the spectral characterization of blended tobacco samples at different ratios to refine the classification and identification methods.The absorption spectra of tobacco blends at different blending ratios were analyzed in the bandwidth of 0-2.0 THz.Four classification models including Logistic Regression(LR),Random Forest(RF),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM)classification models were developed and tested.The classification outcomes of the above models were validated for tobacco samples with different cut stem contents.The results showed that the SVM classification model based on the absorption coefficient had the best general result.For the tobacco samples with cut stem contents ranging from 2%to 10%,the SVM model achieved an accuracy of 91.19%in internal validation and an identification rate of 80.56%in external validation.For the tobacco samples with cut stem contents ranging from 10%to 50%,the SVM model achieved 92.27%accuracy in internal validation and 86.25%identification rate in external validation.The machine learning method based on absorption coefficients therefore can be used to identify tobacco blends with different cut stem contents,which provides a reference for detecting tobacco blending uniformity in production.关键词
太赫兹光谱/分类模型/支持向量机/烟丝掺配/逻辑回归Key words
Terahertz spectroscopy/Classification model/Support Vector Machine/Cut tobacco blending/Logical regression分类
轻工纺织引用本文复制引用
刘涛,丁美宙,张二强,杨心安,杨雨要,邓国栋,朱文魁..基于太赫兹光谱检测技术的烟丝掺配比例识别模型[J].烟草科技,2024,57(12):74-83,10.基金项目
中国烟草总公司重点研发项目"烟草气固两相流工业过程仿真关键技术研发及应用"(110202102042) （110202102042）
中国烟草总公司重大科技项目"雪茄烟特色工艺技术研究"[110202101063(XJ-12)]. （XJ-12）
