高光谱成像
人参
主成分分析
人工智能
计算机科学
可视化
降维
成像光谱仪
原位
模式识别(心理学)
环境科学
生物系统
遥感
分光计
化学
地理
光学
医学
替代医学
病理
物理
有机化学
生物
作者
张伟 Zhang Wei,Xueyuan Bai,Guo Jian-ying,Jin Yang,Bo Yu,Jiaqi Chen,Jinyu Wang,Daqing Zhao,He Zhang,Meichen Liu
标识
DOI:10.1016/j.saa.2024.124700
摘要
In industrial production, the timely assessment of ginseng-derived ingredients is crucial and requires nondestructive techniques for identifying and analyzing composition. Hyperspectral imaging (HSI) effectively visualizes the three-dimensional spatial distribution of phytochemicals in dried ginseng. This study explores the in-situ prediction and visualization of moisture content (MC) and ginsenoside content (GC) in thermally processed ginseng using dual-band HSI. We collected hyperspectral images from 216 raw ginseng samples, which underwent dimensionality reduction, noise reduction, and feature enhancement via Principal Component Analysis (PCA) and Minimum Noise Separation (MNF). Linear regression models were developed following these pretreatments and evaluated using a validation set. The PCA-based models demonstrated superior performance over those based on MNF, especially in predicting GC in the near-infrared (NIR) spectrum. Similarly, models predicting MC in the visible spectrum showed favorable results. HSI enables rapid generation of distribution maps, facilitating real-time imaging for commercial applications. Repeated drying cycles and increased duration primarily affect the textural characteristics and visible color of the ginseng surface, without significantly altering its intrinsic properties. The deployment of this predictive model alongside real-time content inversion using HSI technology holds promise for integrating visual and intelligent quality monitoring in the trade of valuable herbal commodities.
科研通智能强力驱动
Strongly Powered by AbleSci AI