Advances and challenges in multidimensional architectural applications of 1D/2D/3D convolutional neural networks in food quality assessment.

Convolutional neural networks (CNNs) have attracted extensive attention in food quality analysis, owing to their outstanding ability to process multi-dimensional food quality data. This review summarizes the research progress and potential development trends of 1D-CNNs, 2D-CNNs, and 3D-CNNs in food quality evaluation, with a specific focus on their applications in three key data types: spectral data, image data, and spectral-spatial fused information. Nevertheless, the application of CNNs in food quality analysis still faces several persistent challenges, such as issues related to data quality, high model complexity coupled with poor interpretability, substantial computational costs, and inadequate model generalization. This review can shed new insights for promoting the wider adoption of CNNs in the food industry, and further drive the development of more intelligent and sustainable food quality perception systems.