An Efficient Tea Quality Classification Algorithm Based On Near Infrared Spectroscopy And Random Forest

Traditional tea quality evaluation methods are based on chemical testing, such as gas chromatography-mass spectrometry (GCMS) and high-performance liquid chromatography (HPLC). However, the process of extracting chemical components is generally time-consuming and expensive, which makes it unsuitable for wide range of applications. Therefore, this paper presents a new approach to evaluate tea quality based on Near-infrared Spectroscopy (NIRS) devices. In our method, factor analysis compression algorithm is first applied to initially compress the input NIRS vectors, which are acquired from tea samples with high dimensional data. Then, random forest algorithm is employed to construct a voting strategy. More precisely speaking, we proposed a low-cost and convenient tea quality estimation scheme that can be widely used in tea industry. The proposed approach has been verified using tea NIRS datasets which were acquired from Fujian Province. Experiments show that the proposed NIRS-based approach significantly outperforms the GCMS-based and HPLC-based methods. Specially, we achieved a highly competitive performance (AP = 0.989) on the comprehensive data set that contains 869 annotated Chinese tea samples, which means that tea quality can be estimated in a convenient and cheaper way.Practical Applications The proposed tea classification approach based on artificial intelligence which lend new perspectives to tea merchants and consumers insight and decision-making. The approach can perform preference adjustments in various conditions such as regions, crowd habits, seasons, etc.