Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality.
Spectrochim Acta A Mol Biomol Spectrosc
; 237: 118407, 2020 Aug 15.
Article
in En
| MEDLINE
| ID: mdl-32361218
ABSTRACT
The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tea
/
Spectroscopy, Near-Infrared
/
Food Analysis
/
Cheminformatics
/
Hyperspectral Imaging
Type of study:
Health_economic_evaluation
/
Prognostic_studies
Country/Region as subject:
Asia
Language:
En
Journal:
Spectrochim Acta A Mol Biomol Spectrosc
Journal subject:
BIOLOGIA MOLECULAR
Year:
2020
Document type:
Article
Affiliation country:
China
Publication country:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM