Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review

ABSTRACT

With improvement in people's living standards, many people nowadays pay more attention to quality and safety of meat. However, traditional methods for meat quality and safety detection and evaluation, such as manual inspection, mechanical methods, and chemical methods, are tedious, time-consuming, and destructive, which cannot meet the requirements of modern meat industry. Therefore, seeking out rapid, non-destructive, and accurate inspection techniques is important for the meat industry. In recent years, a number of novel and noninvasive imaging techniques, such as optical imaging, ultrasound imaging, tomographic imaging, thermal imaging, and odor imaging, have emerged and shown great potential in quality and safety assessment. In this paper, a detailed overview of advanced applications of these emerging imaging techniques for quality and safety assessment of different types of meat (pork, beef, lamb, chicken, and fish) is presented. In addition, advantages and disadvantages of each imaging technique are also summarized. Finally, future trends for these emerging imaging techniques are discussed, including integration of multiple imaging techniques, cost reduction, and developing powerful image-processing algorithms.

Keywords:

• Pork
• beef
• lamb
• chicken
• fish
• non-destructive
• optical imaging
• ultrasound imaging
• tomographic imaging
• thermal imaging

Acknowledgment

Authors' specially thanks for Nannan Wang from South China University of Technology for her kind suggestions.

Funding

This research was supported by the Key Projects of Administration of Ocean and Fisheries of Guangdong Province (A201401C04), the Collaborative Innovation Major Special Projects of Guangzhou City (201508020097), the International S&T Cooperation Programme of China (2015DFA71150), the International S&T Cooperation Projects of Guangdong Province (2013B051000010), and the Natural Science Foundation of Guangdong Province (2014A030313244). The authors also gratefully acknowledge the Guangdong Province Government (China) for its support through the program “Leading Talent of Guangdong Province (Da-Wen Sun).”