| Literature DB >> 33268162 |
Xue Jintao1, Yang Quanwei2, Li Chunyan3, Liu Xiaolong4, Niu Bingxuan5.
Abstract
Lonicerae Japonicae Flos (LJF) has historically been widely utilized as a tea and health food. To better understand and evaluate its quality evaluate its quality, a near-infrared spectroscopy (NIRS) method was developed for the rapid and simultaneous analysis of the 3 main active components (chlorogenic acid, isochlorogenic acid A and isochlorogenic acid C). The NIRS model was built using 2 different strategies: partial least squares (PLS) as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method. Furthermore, the NIRS method was applied to analyze the 4 main quality factors, which included 5 processing methods (shade drying, sun drying, vacuum drying, freeze drying and hot-air drying), 2 kinds of harvest time (flower bud stage and florescence stage), 2 species and 8 geographical origins. Collectively, NIRS is a promising method for the quality analysis of LJF.Entities:
Keywords: Artificial neural networks; Lonicerae Japonicae Flos; Near-infrared spectroscopy; Partial least squares; Quality analysis
Year: 2020 PMID: 33268162 DOI: 10.1016/j.foodchem.2020.128386
Source DB: PubMed Journal: Food Chem ISSN: 0308-8146 Impact factor: 7.514