Applications of Artificial Neural Networks (ANNs) in Food Science

Abstract

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.

Keywords:

• machine perception
• electronic nose
• machine vision
• spectroscopy
• process control
• microbiology

ACKNOWLEDGEMENTS

This work was sponsored in part by the United States Department of Agriculture National Research Competitive Grants Program (NRICGP Project #2000-01617 and 2000-01191, the City University of New York (PSC-CUNY #60122-3536), Washington State University, the WSU International Marketing Program for Agricultural Commodities and Trade (IMPACT Center), and the National Fisheries Institute.