Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology.
J Sci Food Agric
; 101(5): 2135-2142, 2021 Mar 30.
Article
em En
| MEDLINE
| ID: mdl-32981110
ABSTRACT
BACKGROUND:
Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms.RESULTS:
Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%.CONCLUSION:
This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Chá
/
Espectroscopia de Luz Próxima ao Infravermelho
/
Camellia sinensis
/
Imageamento Hiperespectral
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Idioma:
En
Revista:
J Sci Food Agric
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China