您好，欢迎访问云南省农业科学院 机构知识库！

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

作者： Li, Xiaokun 1 ; Zeng, Pan 1 ; Wu, Xunxun 1 ; Yang, Xintong 1 ; Lin, Jingcang 2 ; Liu, Peizhong 1 ; Wang, Yuanzhong 3 ; Diao, Yong 1 ;

作者机构： 1.Huaqiao Univ, Sch Med, Quanzhou 362021, Peoples R China

2.Quanzhou Med Coll, Quanzhou 362000, Peoples R China

3.Yunnan Acad Agr Sci, Inst Med Plants, Kunming, Peoples R China

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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：云南省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台