Quality Evaluation for Appearance of Needle Green Tea Based on Machine Vision and Process Parameters

DONG Chunwang;ZHU Hongkai;ZHOU Xiaofen;YUAN Haibo;ZHAO Jiewen;CHEN Quansheng;School of Food and Biological Engineering,Jiangsu University;Tea Research Institute,Chinese Academy of Agricultural Sciences;Department of Food Science,University of Copenhagen;Agricultural Bureau,Wuyi County;

needle green tea;;machine vision;;appearance;;sensory quality;;intelligent algorithm;;nonlinearity

Green tea has the largest consumption in China,and needle-shaped green tea is a typical type of green tea. The appearance of green tea is the key sensory evaluation index of green tea. However,it is hard to realize an accurate,objective and quantitative evaluation of green tea through manual evaluation on the characteristics as the color,stripe,tenderness and uniformity,etc. Based on internal and external factors such as quality forming process and visual morphology of tea,an intelligent sensory evaluation method of the appearance quality of tea was established. Firstly,collecting the process parameters of tea products and image characteristics of made tea,totally 17 process parameters,nine color features and six texture features were selected, conducting correlation analysis with expert sensory evaluation, and screening out remarkably correlated characteristic variables. In order to obtain an efficient evaluation model, based on process parameters and image characteristic parameters respectively, multiple quantitative evaluation models were established for needle-shaped green tea appearance senses by usingthree multivariate correction methods such as partial least squares( PLS),extreme learning machine( ELM) and strong predictor integration algorithm( ELM-Ada Boost). The comparison of the results showed that the ELM-Ada Boost model based on image characteristics had the best performance( RPD was more than 2). Its predictive performance was superior to other models,with smaller RMSEP( 0. 874),Bias(-0. 148),SEP( 0. 226),and CV( 0. 018) values of the prediction set,respectively. Meanwhile,non-linear model had better predictive performance than linear model,which can better represent the analytic relationship between process parameters,image information and sensory scores,and modeling faster( 0. 014 ~ 0. 281 s). Ada Boost method,which was a hybrid integrated algorithm,can further promote the accuracy and generalization capability of the model. The above conclusions indicated that it was feasible to evaluate the quality of appearance of needle green tea based on machine vision and process. This study provided an effective technical method and idea for developing tea sensory quality evaluation methods,and laid theoretical basis and data supports on the development of expert process strategy supporting systems of tea quality,which had a broad industry prospect in tea processing,trading and refined blend technology.