2023Journal article
Wu J, Zareef M, Chen Q, Ouyang Q
Food chemistry
Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.
Abstract text comes from PubMed, OpenAlex or Europe PMC and belongs to the authors and publisher.
Study type
Observational (human)
Surveys, cohorts and consumer studies, with no intervention.
Assigned automatically from the title, abstract and subject terms.
Topics
Matcha is a main subject here (named in the title).
Wu J, Zareef M, Chen Q, Ouyang Q. (2023). Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators. Food chemistry. https://doi.org/10.1016/j.foodchem.2023.136185
DOI: 10.1016/j.foodchem.2023.136185 · PubMed ID: 37099951 · Cited by 56 (OpenAlex)
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