Detecting bioactive compound contents in Dancong tea using VNIR-SWIR hyperspectral imaging and KRR model with a refined feature wavelength method.
Food Chem
; 460(Pt 2): 140579, 2024 Dec 01.
Article
in En
| MEDLINE
| ID: mdl-39126740
ABSTRACT
Hyperspectral imaging (HSI) provides opportunity for non-destructively detecting bioactive compounds contents of tea leaves and high detection accuracy require extracting effective features from the complex hyperspectral data. In this paper, we proposed a feature wavelength refinement method called interval band selecting-competitive adaptive reweighted sampling-fusing (IBS-CARS-Fusing) to extract feature wavelengths from visible-near-infrared (VNIR) and short-wave-near-infrared (SWIR) hyperspectral images. Combined with the proposed IBS-CARS-Fusing method, a kernel ridge regression (KRR) model was established to predict the contents of bioactive compounds including chlorophyll a, chlorophyll b, carotenoids, tea polyphenols, and amino acids in Dancong tea. It was revealed that the IBS-CARS-Fusing method can improve Rp2 of KRR model for these bioactive compounds by 4.77%, 4.60%, 6.74%, 15.52%, and 13.10%, respectively, and Rp2 of the model reached high values of 0.9500, 0.9481, 0.8946, 0.8882, and 0.8622. Additionally, a leaf compound mass per area thermal map was used to visualize the spatial distribution of the compounds.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tea
/
Plant Leaves
/
Spectroscopy, Near-Infrared
/
Camellia sinensis
/
Hyperspectral Imaging
Language:
En
Journal:
Food Chem
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
United kingdom