An innovative variant based on generative adversarial network (GAN): Regression GAN combined with hyperspectral imaging to predict pesticide residue content of Hami melon

文献类型： 外文期刊

第一作者： Tan, Haibo

作者： Tan, Haibo;Ma, Benxue;Xu, Ying;Yu, Guowei;Bian, Huitao;Ma, Benxue;Dang, Fumin

作者机构：

关键词： Hyperspectral imaging; Pesticide residue content; Generative adversarial network; Data augmentation; Hami melon

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.6； 五年影响因子：4.3 ）

ISSN： 1386-1425

年卷期： 2025 年 325 卷

页码：

收录情况： SCI

摘要： The rapid and non-destructive detection of pesticide residues in Hami melons plays substantial importance in protecting consumer health. However, the investment of time and resources needed to procure sample data poses a challenge, often resulting in limited data set and consequently leading insufficient accuracy of the established models. In this study, an innovative variant based on generative adversarial network (GAN) was proposed, named regression GAN (RGAN). It was used to synchronically extend the visible near-infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data and corresponding acetamiprid residue content data of Hami melon. The support vector regression (SVR) and partial least squares regression (PLSR) models were trained using the generated data, and subsequently validate them with real data to assess the reliability of the generated data. In addition, the generated data were added to the real data to extend the dataset. The SVR model based on SWIRHSI data achieved the optimal performance after data augmentation, yielding the values of R-p(2), RMSEP and RPD were 0.8781, 0.6962 and 2.7882, respectively. The RGAN extends the range of GAN applications from classification problems to regression problems. It serves as a valuable reference for the quantitative analysis of chemometrics.

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