随机森林
融合
人工智能
传感器融合
计算机科学
线性判别分析
模式识别(心理学)
机器学习
数学
语言学
哲学
作者
Qianqian Li,Chaoyang Zhang,Huawei Wang,Shengfan Chen,Wei Liu,Yang Li,Jianxun Li
标识
DOI:10.1016/j.indcrop.2023.117127
摘要
It is desirable to give a full-scaled evaluation of vine tea by incorporating both the quality related compounds in high content and the volatile compounds in trace level. The NIR and GC-MS technologies were performed for vine tea grade discrimination through data fusion approaches. Two machine learning methods of random forest (RF) and partial least squares-discrimination analysis (PLS-DA) were carried out to construct the tea grade discrimination platform with eight data driven models. Besides, the Monte-Carlo technology was implemented to acquire more representative results from thirty sub-models. As a result, the mid-level fusion combined with RF (92.38% ± 0.0446%) gave more impressive performance owing to the overall analysis with more balanced matrix, efficient features, and remarkable discriminant ability. The results revealed that the mid-level fusion coupled with RF is a promising method for tea grade identification and have great potential to guarantee the food quality.
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