摘要
为了快速无损地判别国产烤烟香型,指导卷烟配方原料利用,对1383份烟叶样品进行近红外光谱采集,基于八大香型区划结果进行香型分类,选用各香型稳定区的样品构建香型分类模型。结果显示,基于近红外原始光谱数据利用随机森林构建的香型分类模型准确率仅为48.64%,光谱数据经过SG滤波一阶或二阶导数和多元散射校正预处理后,模型准确率提高29.54个百分点,然后经因子分析降维处理(45个因子)模型准确率提高到85.91%,最后对模型关键参数进行优化,当评估器数量为500、随机种子为9时,模型准确率最高为90.45%。利用建立的分类模型对预测集进行预测,清甜香型、焦甜焦香型、清甜蜜甜香型和木香蜜甜香型的召回率均达到90.00%以上,召回率最低的为蜜甜香型和焦甜醇甜香型(66.67%)。以上结果表明,利用近红外光谱技术能够有效鉴别烤烟八大香型。
In order to quickly and non-destructively discriminate the aroma type of domestic flue-cured tobacco and guide the utilization of raw materials for cigarette formulation,near infrared spectra were collected from 1383 tobacco samples,the aroma was classified based on the results of eight aroma zones,and the samples from each stable aroma zone were selected to build the aroma classification model.The results showed that the accuracy of the fragrance classification model constructed by using random forest based on the NIR raw spectral data was only 48.64%;the model accuracy was improved by 29.54percentage points after the spectral data were pre-processed by SG filtering first or second derivatives and multiple scattering correction;then the model accuracy was improved to 85.91%by factor analysis and dimensionality reduction(45 factors);finally,the model key parameters were optimized,the highest model accuracy of 90.45%was achieved when the number of base evaluators was 500 and the random seed was9.This classification model was used to predict the prediction set,the recall rates of fresh-sweetness type,burnt-sweetness-burnt type,fresh-honey-sweetness type and woody-honey-sweetness type all reached more than 90.00%.The lowest recall rate was for honey-sweetness type and burnt-honey-sweetness type(66.67%).The results show that the use of NIR spectroscopy can effectively identify the eight aroma types of flue-cured tobacco,which provides new ideas and technical support for the rapid identification of flue-cured tobacco aroma types and digital evaluation of tobacco styles.
作者
付博
杨永锋
刘向真
赵森森
刘茂林
贾国涛
牛洋洋
张坤芳
于建军
彭桂新
姬小明
FU Bo;YANG Yongfeng;LIU Xiangzhen;ZHAO Sensen;LIU Maolin;JIA Guotao;NIU Yangyang;ZHANG Kunfang;YU Jianjun;PENG Guixin;JI Xiaoming(Technology Center,China Tobacco Henan Industrial Co.,Ltd.,Zhengzhou 450016,China;College of Tobacco,Henan Agricultural University,Zhengzhou 450002,China)
出处
《河南农业科学》
北大核心
2023年第7期163-171,共9页
Journal of Henan Agricultural Sciences
基金
河南中烟工业有限责任公司科技项目(C202023)。
关键词
烤烟
近红外光谱
香型
分类模型
随机森林
Flue-cured tobacco
Near infrared spectroscopy
Aroma type
Classification model
Random forest