A new analytical method for discrimination of species in Ganodermataceae mushrooms

Abstract

A new analytical approach for the species discrimination of Ganodermataceae mushroom was developed by using data fusion strategy based on attenuated total reflectance Fourier transform infrared ATR-FTIR and ultraviolet–visible (UV–vis) spectroscopy, and applying the chemometric tools. The optimization for determination of UV–vis spectra was described. The multivariate discrimination ways used were t-distributed Stochastic Neighbor Embedding (t-SNE), Partial Least Squares-Discriminant Analysis (PLS-DA), and Random Forest (RF). The data fusion levels used were low- and mid-data fusion. The performance of the model was assessed by several parameters as root mean square error of estimation (RMSEE), root mean square error of cross validation (RMSECV), R2Y(cum), and Q2(cum). The discrimination results were evaluated by accuracy from the test set, which was composed of samples with unknown origin. The new proposed method took shorter time and lower cost, and the results showed good discrimination power among various species. PLS-DA and RF models based on mid-level fusion data were able to classify mushrooms according to real origin, confirming the potential of data fusion and chemometrics in mushrooms species discrimination.

Keywords

• ganodermataceae mushrooms
• atr-ftir spectroscopy
• uv–vis spectroscopy
• data fusion
• discrimination analysis