Abstract
New research on Machine Learning is the subject of a report. According to news reporting out of Quanzhou, People’s Republic of China, by NewsRx editors, research stated, “Gentian, an herb resource known for its antioxidant properties, has garnered significant attention. However, existing methods are time-consuming and destructive for assessing the antioxidant activity in gentian root samples.” Our news journalists obtained a quote from the research from Huaqiao University, “In this study, we propose a method for swiftly predicting the antioxidant activity of gentian root using FT-IR spectroscopy combined with chemometrics. We employed machine learning and deep learning models to establish the relationship between FT-IR spectra and DPPH free radical scavenging activity. The results of model fitting reveal that the deep learning model outperforms the machine learning model. The model’s performance was enhanced by incorporating the Double-Net and residual connection strategy. The enhanced model, named ResD-Net, excels in feature extraction and also avoids gradient vanishing. The ResD-Net model achieves an R of 0.933, an RMSE of 0.02, and an RPD of 3.856.”
NETL
NSTL