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A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork
Korean J. Food Sci. An. 2018;38:362-375
Published online April 30, 2018
© 2018 Korean Society for Food Science of Animal Resources

Yi Xu1, Quansheng Chen1,2,*, Yan Liu1, Xin Sun3, Qiping Huang1, Qin Ouyang1, and Jiewen Zhao1

1School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
2State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 210036, China
3Animal Science Department, North Dakota State University, Fargo, United States
Correspondence to: Quansheng Chen
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Tel: +86-511-88790318 Fax: +86-511-88780201 E-mail: qschen@ujs.edu.cn
Received January 2, 2018; Revised March 15, 2018; Accepted March 16, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.
Keywords : pork, fresh degree evaluation, hyperspectral microscopic imaging, texture analysis


April 2018, 38 (2)