粮食安全
计算机科学
食物供应
数据科学
食品安全
人工神经网络
供应链
分析
风险分析(工程)
人工智能
农业
业务
营销
地理
食品科学
环境科学
农业科学
考古
化学
作者
Peihua Ma,Zhikun Zhang,Xiaoxue Jia,Xiaoke Peng,Zhi Zhang,Kevin Tarwa,Cheng‐I Wei,Fuguo Liu,Qin Wang
标识
DOI:10.1080/10408398.2022.2139217
摘要
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
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