ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy

文献类型： 外文期刊

第一作者： Li, Xiaokun

作者： Li, Xiaokun;Zeng, Pan;Wu, Xunxun;Yang, Xintong;Liu, Peizhong;Diao, Yong;Lin, Jingcang;Liu, Peizhong;Wang, Yuanzhong

作者机构：

关键词： Antioxidant activity; FT-IR; Gentian; Deep Learning; Chemometrics

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.4； 五年影响因子：3.9 ）

ISSN： 1386-1425

年卷期： 2024 年 310 卷

页码：

收录情况： SCI

摘要： 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. 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 R2 of 0.933, an RMSE of 0.02, and an RPD of 3.856. These results support the accuracy and applicability of this method for rapidly predicting antioxidant activity in gentian root samples.

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