Based on near-infrared spectroscopy and chemometrics to rapidly evaluate the adulteration of Ganoderma lingzhi powder

Ganoderma lingzhi , the dry fruiting bodies of G. lucidum or G. sinensis , is a microbial food of high nutritional and health value. It is expensive but in high demand. In pursuit of higher profits, counterfeit products adulterated with G. lingzhi , such as G. applanatum , have appeared in the market. To identify the authenticity and forecast the degree of adulteration in Ganoderma lingzhi powder rapidly and non-destructively, the combination of near-infrared spectroscopy (NIRS) and chemometrics was used. Partial least squares discriminant analysis (PLS-DA), back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted as qualitative identification of G. lingzhi authenticity model methods, and partial least-squares (PLS) was developed as a quantitative prediction of adulteration content. Preprocessing and feature variables selection methods were developed to optimize the model and screen the best model. Among these experimental approaches, PLS-DA + first-order derivatives (D1), SVM + D1 + Competitive adaptive reweighted sampling (CARS), RF + standard normal variate transform (SNV) and BPNN + D1 + Uninformative variable elimination (UVE) + CARS achieved 100% classification accuracy. SVM + second-order derivatives (D2) + CARS and BPNN + D2 + CARS identified all adulterated G. lucidum , PLS-DA + D1 + UVE + CARS, RF + D2 + Genetic algorithm (GA), SVM + D2 + GA, and BPNN + D2 + CARS could distinguish all adulterated G. sinensis effectively.