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Data Fusion Strategy for Nondestructive Detection of Aflatoxin B1 Content in Single Maize Kernel Using Dual-Wavelength Laser-Induced Fluorescence Hyperspectral Imaging

文献类型： 外文期刊

作者： Yao, Xueying 1 ; Fan, Yaoyao 3 ; Wang, Qingyan 3 ; Huang, Wenqian 3 ; Zhao, Chunjiang 1 ; Tian, Xi 1 ;

作者机构： 1.Univ Sci & Technol Beijing, Res Inst Biol & Agr, Beijing 100083, Peoples R China

2.Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430072, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing, Peoples R China

关键词： Maize; Fluorescence hyperspectral imaging; Aflatoxin B1; Nondestructive detection; Data fusion strategy; Dual-wavelength laser-induced fluorescence

期刊名称：FOOD AND BIOPROCESS TECHNOLOGY （ 影响因子：5.8； 五年影响因子：6.1 ）

ISSN： 1935-5130

年卷期： 2025 年 18 卷 6 期

页码：

收录情况： SCI

摘要： Aflatoxin B1 (AFB1) is the most widespread, toxic, and harmful mycotoxin, and maize is highly susceptible to AFB1 contamination, posing significant risks to human and animal health. Therefore, precise detection of AFB1 is essential to ensuring food safety. In this study, we used fluorescence probe technology to track the infection process of Aspergillus flavus in maize, confirming the uneven distribution of AFB1 and proposing the use of a "full-surface scanning" spectral information acquisition mode to improve detection accuracy. Therefore, we developed a full-surface fluorescence hyperspectral imaging system with high excitation/emission characteristics, combining dual-wavelength laser-induced fluorescence hyperspectral imaging and data fusion strategy to enable nondestructive detection of AFB1 in individual maize kernels. To address fluorescence crosstalk between maize substance and AFB1, we analyzed three-dimensional fluorescence spectra of healthy maize and pure AFB1 samples, identifying 360 nm and 405 nm as the optimal excitation wavelengths for AFB1 detection in maize. Furthermore, a prediction model for AFB1 content was constructed by combining different levels of data fusion strategies with a partial least squares (PLS) regression algorithm. The results showed that the dual-wavelength data fusion model was superior to the single-wavelength model. Specifically, the decision-level fusion model based on the characteristic wavelength selected by competitive adaptive reweighted sampling (CARS) achieved the best predictive performance (Rp = 0.83). This approach provides a new method for quantitative detection of AFB1 and lays the foundation for the advancement of AFB1 detection technology to enhance food safety.

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