Huang Yiqun, Kangas Lars J, Rasco Barbara A
Department of Family, Nutrition, and Exercise Sciences, Queens College, the City University of New York, Flushing, NY 11367-1597, USA.
Crit Rev Food Sci Nutr. 2007;47(2):113-26. doi: 10.1080/10408390600626453.
Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.
在过去二十年中，人工神经网络（ANN）已应用于食品科学的几乎每个领域，尽管大多数应用仍处于开发阶段。人工神经网络是食品安全和质量分析的有用工具，包括微生物生长建模以及据此预测食品安全、解释光谱数据，以及预测各种食品在加工和分销过程中的物理、化学、功能和感官特性。人工神经网络在过程控制和模拟中的复杂任务建模以及包括机器视觉和电子鼻在内的机器感知在食品安全和质量控制中的应用方面具有很大的前景。本文综述讨论了人工神经网络技术的基本理论及其在食品科学中的应用，为食品科学家和研究界提供了人工神经网络技术在该领域应用的当前研究和未来趋势的概述。
