Identifying the quality characteristics of pork floss structure based on deep learning framework

Che Shen; Meiqi Ding; Xinnan Wu; Guanhua Cai; Yun Cai; Shengmei Gai; Bo Wang; Dengyong Liu

doi:10.1016/j.crfs.2023.100587

Identifying the quality characteristics of pork floss structure based on deep learning framework

Curr Res Food Sci. 2023 Sep 9:7:100587. doi: 10.1016/j.crfs.2023.100587. eCollection 2023.

Authors

Che Shen^{1

2}, Meiqi Ding¹, Xinnan Wu¹, Guanhua Cai¹, Yun Cai¹, Shengmei Gai¹, Bo Wang^{1

3

4}, Dengyong Liu¹

Affiliations

¹ College of Food Science and Technology, Bohai University, Jinzhou 121013, China.
² Key Laboratory for Agricultural Products Processing of Anhui Province, School of Food Science and Engineering, Hefei University of Technology, Hefei, 230009, China.
³ Key Laboratory of Meat Processing and Quality Control, MOE, Key Laboratory of Meat Processing, MARA, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
⁴ Institute of Ocean Research, Bohai University, Jinzhou 121013, Liaoning, China.

Abstract

Pork floss is a traditional Chinese food with a long history. Nowadays, pork floss is known to consumers as a leisure food. It is made from pork through a unique process in which the muscle fibers become flaky or granular and tangled. In this study, a deep learning-based approach is proposed to detect the quality characteristics of pork floss structure. Describe that the experiments were conducted using widely recognized brands of pork floss available in the grocery market, omitting the use of abbreviations. A total of 8000 images of eight commercially available pork flosses were collected and processed using sharpening, image gray coloring, real-time shading correction, and binarization. After the machine learning model learned the features of the pork floss, the images were labeled using a manual mask. The coupling of residual enhancement mask and region-based convolutional neural network (CRE-MRCNN) based deep learning framework was used to segment the images. The results showed that CRE-MRCNN could be used to identify the knot features and pore features of different brands of pork floss to evaluate their quality. The combined results of the models based on the sensory tests and machine vision showed that the pork floss from TC was the best, followed by YJJ, DD and HQ. This also shows the potential of machine vision to help people recognize the quality characteristics of pork floss structure.

Keywords: Deep learning; Image segmentation; Machine vision; Pork floss; Sensory evaluation.