School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China.
Food Chem. 2023 May 30;409:135251. doi: 10.1016/j.foodchem.2022.135251. Epub 2022 Dec 20.
The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters R, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.
本研究旨在开发一种涉及小波变换（WT）和堆叠去噪自动编码器（SDAE）的深度学习方法，用于提取油菜叶片重金属铅（Pb）检测的深度特征。首先，建立标准归一化变量（SNV）算法作为最佳预处理算法，并将 SNV 处理后的荧光光谱数据用于进一步的数据分析。然后，WT 用于分解油菜叶片 SNV 处理后的荧光光谱，使用不同的小波基函数获得最佳的小波分解层，在最佳的小波分解层下使用 SDAE 进行深度特征学习。最后，使用 sym7 作为小波基函数，最佳建立的支持向量机回归（SVR）模型预测集参数 R、RMSEP 和 RPD 分别为 0.9388、0.0199mg/kg 和 3.275。本研究结果验证了荧光高光谱技术与深度学习算法相结合在重金属检测方面的巨大潜力。
