AUTHOR=Ran Jing , Xu Hui , Wang Zhilong , Zhang Wei , Bai Xueyuan 

TITLE=Non-destructive analysis of Ganoderma lucidum composition using hyperspectral imaging and machine learning

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1534216

DOI=10.3389/fchem.2025.1534216

ISSN=2296-2646

ABSTRACT=BackgroundGanoderma lucidum is a widely used medicinal fungus whose quality is influenced by various factors, making traditional chemical detection methods complex and economically challenging. This study addresses the need for fast, noninvasive testing methods by combining hyperspectral imaging with machine learning to predict polysaccharide and ergosterol levels in Ganoderma lucidum cap and powder.MethodsHyperspectral images in the visible near-infrared (385–1009 nm) and short-wave infrared (899–1695 nm) ranges were collected, with ergosterol measured by high-performance liquid chromatography and polysaccharides assessed via the phenol-sulfuric acid method. Three machine learning models—a feedforward neural network, an extreme learning machine, and a decision tree—were tested.ResultsNotably, the extreme learning machine model, optimized by a genetic algorithm with voting, provided superior predictions, achieving R2 values of 0.96 and 0.97 for polysaccharides and ergosterol, respectively.ConclusionThis integration of hyperspectral imaging and machine learning offers a novel, nondestructive approach to assessing Ganoderma lucidum quality.