# A rapid approach for discriminating Ganoderma species using attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometric analysis and convolutional neural network (CNN)

## TL;DR

This paper introduces a new method using infrared spectroscopy and machine learning to accurately identify different Ganoderma species.

## Contribution

A novel framework combining ATR-FTIR spectroscopy with chemometric analysis and CNN for Ganoderma species discrimination is proposed.

## Key findings

- OPLS-DA achieved 98.61% accuracy in classifying Ganoderma species.
- The CNN model showed 89.84% accuracy with consistent performance across tests.
- Both models demonstrated high reliability and robustness for species authentication.

## Abstract

The issue of adulteration and misclassification of Ganoderma species is addressed in this research. In the study, we present a novel and comprehensive framework for Ganoderma authentication by analyzing attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectra using a combined approach of a chemometric analysis and deep learning (DL) with a convolutional neural network (CNN). The three Ganoderma species involved in this study were as follows: Ganoderma lucidum, Ganoderma sinense, and Ganoderma tsugae. Among chemometric models, orthogonal partial least squares discriminant analysis (OPLS-DA) yielded a high accuracy of 98.61%, a sensitivity of 97.92%, and a specificity of 98.96%. Additionally, the root-mean-squared error of estimation (RMSEE), root-mean-squared error of prediction (RMSEP), and root-mean-squared error of cross-validation (RMSECV) values for the OPLS-DA model were <0.3, confirming its reliability. The CNN model also performed well, achieving 89.84% accuracy, 84.75% sensitivity, and 92.38% specificity, with minimal variation during random segregation testing. Additionally, the model exhibited a precision of 0.87 ± 0.02, a recall of 0.85 ± 0.03, and an F1 score of 0.86 ± 0.03 for 10 random segregation tests. As a conclusion, both chemometric and CNN models developed in this study are efficient and robust for classifying Ganoderma species. To further validate this combined approach, we aim to implement chemometric and CNN models in other medicinal herb authentication in the future.