摘要
发酵是红茶加工中最关键的步骤,为解决传统加工依赖人工经验的问题,利用计算机视觉和电子鼻联合数据融合策略快速无损检测红茶的发酵质量。通过茶多酚质量分数将红茶发酵程度划分成不同等级,并与图像和气味信息建立关系;采用不同的数据融合策略结合随机森林(RF)、K最近邻(KNN)和支持向量机(SVM)建立红茶发酵的定性判别模型,并与单一传感器模型进行比较。结果表明,数据融合策略结合了不同传感器的信息,获得了更全面的数据,其模型判别结果优于单一传感器;特征级融合策略提取了不同传感器的信息特征值,简化了模型数据,获得了比数据级融合策略更优的模型性能;其中基于特征级融合策略的SVM模型分类效果最好,训练集分类正确率为100%,预测集分类正确率为95.56%,实现了对红茶发酵程度的快速、准确判别。
Fermentation is the most critical step in black tea processing.To address the limitations of traditional methods that rely on manual experience,a rapid and non-destructive detection approach for assessing black tea fermentation quality was developed using a data fusion strategy that combines computer vision and electronic nose technologies.The fermentation degree of black tea was classified into different levels based on the mass fraction of tea polyphenols,and a correlation was established with image and odor information.Qualitative discriminant models for black tea fermentation were developed using different data fusion strategies in combination with random forests(RF),K-nearest neighbors(KNN),and support vector machine(SVM)models,and these were compared with the single sensor models.The results showed that data fusion strategies integrated information from different sensor,providing more comprehensive data,and their discrimination result was better than that of a single sensor.The feature-level data fusion strategies extracted the eigenvalues of different sensors information,simplifying the model data and achieving the superior performance compared to data-level fusion strategies.Among them,the SVM model based on feature-level data fusion achieved the best classification performance,with a classification accuracy rate of 100%in the training set and 95.56%in the prediction set,realizing the rapid and accurate identification of different fermentation degrees of black tea.
作者
戴振华
李露青
周巧仪
宋飞虎
凌彩金
宋春芳
DAI Zhenhua;LI Luqing;ZHOU Qiaoyi;SONG Feihu;LING Caijin;SONG Chunfang(School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China;Tea Research Institute of Guangdong Academy of Agricultural Sciences,Guangzhou 510640,China;School of Tea and Food Science&Technology,Anhui Agricultural University,Hefei 230036,China;State Key Laboratory of Tea Plant Biology and Utilization,Anhui Agricultural University,Hefei 230036,China)
出处
《食品与生物技术学报》
CAS
CSCD
北大核心
2024年第8期103-111,共9页
Journal of Food Science and Biotechnology
基金
广东省茶树资源创新利用重点实验室开放课题项目(2020KF02)
安徽农业大学茶树生物学与资源利用国家重点实验室开放基金资助项目(SKLTOF20210117)。
关键词
计算机视觉
电子鼻
数据融合策略
红茶发酵
computer vision
electronic nose
data fusion strategy
black tea fermentation
