Machine learning technique combined with data fusion strategies: A tea grade discrimination platform

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% & PLUSMN; 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.

Automated summary

It is important to evaluate vine tea comprehensively by considering both high content quality-related compounds and trace-level volatile compounds. NIR and GC-MS technologies were used for grade discrimination, and machine learning methods of random forest and PLS-DA were employed to construct a tea grade discrimination platform. The results showed that the mid-level fusion combined with random forest achieved impressive performance in terms of balanced matrix, efficient features, and discriminant ability.