VNIR公司
高光谱成像
特征(语言学)
近红外光谱
遥感
红外线的
模式识别(心理学)
人工智能
计算机科学
光学
地理
物理
语言学
哲学
作者
Teng Long,Xinyu Tang,Changjiang Liang,Binfang Wu,Binshan Huang,Yubin Lan,Haitao Xu,Shaoqun Liu,Yongbing Long
出处
期刊:Food Chemistry
[Elsevier BV]
日期:2024-07-24
卷期号:460: 140579-140579
被引量:1
标识
DOI:10.1016/j.foodchem.2024.140579
摘要
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 R
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