基于近红外光谱的醇化雪茄烟叶品种判别模型研究
Study on Discrimination Model of Alcoholized Cigar Tobacco Varieties Based on Near Infrared Spec-troscopy
孙利 1张毅 1孟广云 2余彦 2高飞 2王潞2
作者信息
- 1. 湖南农业大学 生物科学技术学院,湖南 长沙 4101281
- 2. 云南省烟草公司 保山市公司,云南 保山 678000
- 折叠
摘要
为提高不同醇化后雪茄烟叶品种的判别准确性,采用多元散射校正等预处理算法对光谱数据进行去噪处理,以降低试验、环境和仪器噪音对数据的影响.结合支持向量机、BP神经网络和随机森林建立不同品种的近红外光谱判别模型,通过准确率和混淆矩阵评估模型性能.结果表明:采用SNV+FD预处理算法和CARS特征波长选择算法建立的模型效果最佳,在训练集和预测集上均表现出较高准确性,证实了利用近红外光谱技术快速判别不同醇化后雪茄烟叶品种的可行性.综上,利用近红外光谱技术可实现对不同品种醇化后雪茄烟叶的无损、快速判别,进一步提高雪茄烟叶工业可用性.
Abstract
The aim of this study was to improve the discrimination accuracy of different post-alcoholised cigar tobacco varieties,pre-processing algorithms such as multiple scattering correction were used to denoise the spectral data in order to reduce the influence of experimental,environmental and instrumental noise on the data.Support vector machines,BP neural networks and random forests were combined to establish near-infrared spectral discrimination models for different varieties.The model performance was evaluated by ac-curacy and confusion matrix.The results showed that the model built with SNV+FD preprocessing algorithm and CARS feature wave-length selection algorithm was the most effective,and showed high accuracy in both training and prediction sets,which confirmed the feasibility of the use of NIR spectroscopy to quickly discriminate different varieties of post-alcoholisation cigar tobacco leaves.In sum-mary,the use of near infrared spectroscopy could realize the non-destructive and rapid discrimination of different varieties of alco-holized cigar tobacco,and further improve the industrial availability of cigar tobacco.
关键词
雪茄烟叶/近红外光谱技术/品种判别/支持向量机/随机森林/BP神经网络Key words
cigar tobacco/near infrared spectroscopy/variety discrimination/support vector machine/random forest/BP neural network引用本文复制引用
基金项目
中国烟草总公司云南省公司科技项目(2021530000242040)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据