Ding Haohan, Hou Haoke, Wang Long, Cui Xiaohui, Yu Wei, Wilson David I
Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Foods. 2025 Jan 14;14(2):247. doi: 10.3390/foods14020247.
This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model.
本综述探讨了卷积神经网络（CNN）和循环神经网络（RNN）在食品安全检测和风险预测中的应用。本文强调了CNN在图像处理和特征识别方面的优势，以及RNN（尤其是其变体LSTM）在时间序列数据建模方面的强大能力。本文还在多个方面进行了比较分析：首先，将传统食品安全检测和风险预测方法的优缺点与CNN和RNN等深度学习技术进行了比较。其次，分析了CNN与全连接神经网络在处理图像数据方面的异同。此外，还讨论了RNN与传统统计建模方法在处理时间序列数据方面的优缺点。最后，比较了CNN在食品安全检测中的应用方向和RNN在食品安全风险预测中的应用方向。本文还讨论了将这些深度学习模型与物联网（IoT）、区块链和联邦学习等技术相结合，以提高食品安全检测和风险预警的准确性和效率。最后，本文提到了RNN和CNN在食品安全领域的局限性，以及模型可解释性方面的挑战，并建议使用可解释人工智能（XAI）技术来提高模型的透明度。
