ATR-FTIR Coupled With Machine Learning Provides a Fast Method for Identifying and Distinguishing 55 Varieties of Fruit-Derived Medicinal Materials.

INTRODUCTION

Fruit-derived medicinal materials (FDMM) are extensively utilized in daily life, yet the market is beset by substantial variety confusion, which undermines consumer rights and well-being. Consequently, accurate identification of these materials is essential for guaranteeing their quality, effectiveness, and safety.

OBJECTIVES

This study aimed to combine attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and machine learning (ML) techniques to differentiate and identify 55 kinds of FDMM.

MATERIALS AND

METHODS

A total of 861 sample batches were collected, with 721 allocated for model establishment and 140 for independent validation. A PLS-DA model alongside nine machine learning algorithms-namely support vector machine (SVM), tree, K-nearest neighbor (KNN), discriminant, ensemble, support vector machine kernel (SVMK), logistic regression kernel (LRK), naive Bayes (NB), and neural network (NN)-were constructed. Considering both accuracy and computational efficiency, the optimal model was selected and evaluated in terms of its accuracy, precision, recall, and F1-score. The optimal model was further validated using 140 newly collected samples to ensure its long-term stability after several months.

RESULTS

Among the 10 classification models, the KNN model showed exceptional classification capability, with all evaluation metric exceeding 0.98. The KNN model was validated by the new 140 samples with a prediction accuracy of 85.7%, confirming its capability in distinguishing most FDMM.

CONCLUSION

The application of ATR-FTIR technology combined with the robust classification capabilities of ML models enabled rapid and accurate differentiation and identification of 55 FDMM, which contributed to ensuring their quality.