Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

Abstract

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Keywords:

• Hyperspectral imaging
• deep learning
• convolutional neural network
• food quality
• food safety

Acknowledgments

The authors would like to appreciate all the authors listed in the references, and acknowledge the funding organizations that provided financial support.

Declaration of interest statement

The authors declare that there are no conflicts of interest.

Additional information

Funding

This work was supported by the project of China National Key Research and Development Program under Grant [2016YFD0700304]; and National Natural Science Foundation of China under Grant [61705195].