TY - JOUR

T1 - Quality Evaluation for Appearance of Needle Green Tea Based on Machine Vision and Process Parameters

AU - Dong, Chunwang

AU - Zhu, Hongkai

AU - Zhou, Xiaofen

AU - Yuan, Haibo

AU - Zhao, Jiewen

AU - Chen, Quansheng

PY - 2017

Y1 - 2017

N2 - 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 using three multivariate correction methods such as partial least squares (PLS), extreme learning machine (ELM) and strong predictor integration algorithm (ELM-AdaBoost). The comparison of the results showed that the ELM-AdaBoost 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). AdaBoost 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.

AB - 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 using three multivariate correction methods such as partial least squares (PLS), extreme learning machine (ELM) and strong predictor integration algorithm (ELM-AdaBoost). The comparison of the results showed that the ELM-AdaBoost 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). AdaBoost 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.

KW - Appearance

KW - Intelligent algorithm

KW - Machine vision

KW - Needle green tea

KW - Non-linearity

KW - Sensory quality

U2 - 10.6041/j.issn.1000-1298.2017.09.005

DO - 10.6041/j.issn.1000-1298.2017.09.005

M3 - Journal article

AN - SCOPUS:85037535409

VL - 48

SP - 38

EP - 45

JO - Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery

JF - Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery

SN - 1000-1298

IS - 9

ER -