School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Shandong Provincial Engineering Research Center of Vegetable Safety and Quality Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China; Zibo City Key Laboratory of Agricultural Product Safety Traceability, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
School of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong, 255049, China.
Talanta. 2024 Jan 15;267:125187. doi: 10.1016/j.talanta.2023.125187. Epub 2023 Sep 7.
In this study, a novel uniform manifold approximation and projection combined-improved simultaneous optimization genetic algorithm-convolutional neural network (UMAP-ISOGA-CNN) algorithm was proposed. The improved simultaneous optimization genetic algorithm (ISOGA) combined with convolutional neural network (CNN) to optimize the architecture, hyperparameters, and optimizer of the CNN model simultaneously. Additionally, a uniform manifold approximation and projection (UMAP) method was used to visualize the feature space of all feature layers of the ISOGA-CNN model. The UMAP-ISOGA-CNN algorithm combined with visible and near-infrared hyperspectral imaging was used to identify peanut kernels contaminated with Aspergillus flavus and to distinguish their storage time, which is essential for the food industry to monitor the freshness of products. Overall, the UMAP-ISOGA-CNN algorithm provides useful insights into the feature space of the ISOGA-CNN model, contributing to a better understanding of the model's internal mechanisms. This study has practical implications for the food industry and future research on deep learning optimization.
在本研究中，提出了一种新颖的统一流形逼近和投影结合改进的同时优化遗传算法-卷积神经网络（UMAP-ISOGA-CNN）算法。改进的同时优化遗传算法（ISOGA）与卷积神经网络（CNN）相结合，以同时优化 CNN 模型的架构、超参数和优化器。此外，还使用了统一流形逼近和投影（UMAP）方法来可视化 ISOGA-CNN 模型的所有特征层的特征空间。将 UMAP-ISOGA-CNN 算法与可见近红外高光谱成像相结合，用于识别受黄曲霉污染的花生仁，并区分其储存时间，这对于食品行业监测产品的新鲜度至关重要。总的来说，UMAP-ISOGA-CNN 算法提供了对 ISOGA-CNN 模型特征空间的有用见解，有助于更好地理解模型的内部机制。本研究对食品工业和未来的深度学习优化具有实际意义。
