Application of ATR-FTIR Spectrum Combined With Ensemble Learning and Deep Learning for Identification of Amomum tsao-ko at Different Drying Temperatures

Abstract

Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is an important medicinal plant and flavoring spice. A. tsao-ko dried at different drying temperatures has different nutritional and medicinal values, leading to the phenomenon of substandard products in the market from time to time. In this study, attenuated total reflection–Fourier transform infrared spectroscopy (ATR-FTIR) data were pre-processed with SD, normalization, EWMA, SNV to compare their effects on the recognition ability of SVM, RF, XGBoost, and CatBoost models. Meanwhile, full-band and local-band 2DCOS profiles were obtained to characterize the differences in chemical features of A. tsao-ko dried by different drying temperatures and classified in conjunction with the ResNet model. The results show that although traditional machine learning can obtain better classification results, the classification efficiency is very unsatisfactory, and the correct classification rate is improved to 97% after derivative (SD) preprocessing. The 2DCOS atlas is able to visualize the feature information in the samples, which is further combined with the ResNet model to obtain 100% classification correctness with excellent generalization ability and convergence effect. The above study was able to provide new ideas for quality evaluation of A. tsao-ko.