Polysaccharide prediction in Ganoderma lucidum fruiting body by hyperspectral imaging

Ganoderma lucidum is a traditional Chinese healthy food with many kinds of nutritious activities, and polysaccharide is one of its main active components. Ganoderma lucidum polysaccharide plays a vital role in improving human immunity and anti-oxidation. At present, the methods of detecting polysaccharide content of Ganoderma lucidum are destructive, and the steps are complicated and time-consuming. This study aims to explore the possibility of using hyperspectral imaging (HSI) to predict polysaccharide content in a nondestructive way during the growth of Ganoderma lucidum. The partial least square regression (PLSR) model shows good performance for Ganoderma lucidum (R-p(2)= 0.924, RPDp = 3.622) with pretreatment method of Savitzky-Golay (SG) and standard normal variate (SNV), and feature selection method of successive projections algorithm (SPA). This study indicates that HSI can quickly and nondestructive detect the polysaccharide content of Ganoderma lucidum, provide guidance for the cultivation industry and improve the economic benefits of Ganoderma lucidum.

Automated summary

This study explores the possibility of using hyperspectral imaging (HSI) to predict the polysaccharide content of Ganoderma lucidum in a nondestructive way during its growth. The study shows that the partial least square regression (PLSR) model, with the pretreatment method of Savitzky-Golay (SG) and standard normal variate (SNV), and the feature selection method of successive projections algorithm (SPA), performs well in predicting polysaccharide content. This study indicates that HSI can quickly and nondestructively detect the polysaccharide content of Ganoderma lucidum, providing guidance for the cultivation industry and improving economic benefits.