Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology.
Food Chem
; 350: 129141, 2021 Jul 15.
Article
em En
| MEDLINE
| ID: mdl-33618087
ABSTRACT
This study aimed to assess the feasibility of identifying multiple chemical constituents in matcha using visible-near infrared hyperspectral imaging (VNIR-HSI) technology. Regions of interest (ROIs) were first defined in order to calculate the representative mean spectrum of each sample. Subsequently, the standard normal variate (SNV) method was applied to correct the characteristic spectra. Competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) were used to optimize the models. They were built based on partial least squares (PLS), creating two models referred to as CARS-PLS and BOSS-PLS. The BOSS-PLS models achieved best predictive accuracy, with coefficients of determination predicted to be 0.8077 for caffeine, 0.7098 for tea polyphenols (TPs), 0.7942 for free amino acids (FAAs), 0.8314 for the ratio of TPs to FAAs, and 0.8473 for chlorophyll. These findings highlight the potential of VNIR-HSI technology as a rapid and nondestructive alternative for simultaneous quantification of chemical constituents in matcha.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Chá
/
Imageamento Hiperespectral
/
Raios Infravermelhos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Food Chem
Ano de publicação:
2021
Tipo de documento:
Article