A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

Overview

Journal Korean J Food Sci Anim Resour

Specialty Biotechnology

Date 2018 May 29

PMID 29805285

Citations 1

Authors

Yi Xu

Quansheng Chen

Yan Liu

Xin Sun

Qiping Huang

Qin OuYang

Jiewen Zhao

Affiliations

Soon will be listed here.

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.

Citing Articles

Application of Microspectral Imaging in Motor and Sensory Nerve Classification.

Xu D J Healthc Eng. 2021; 2021:4954540.

PMID: 34912533 PMC: 8668288. DOI: 10.1155/2021/4954540.

References

Girolami A, Napolitano F, Faraone D, Braghieri A . Measurement of meat color using a computer vision system. Meat Sci. 2012; 93(1):111-8. DOI: 10.1016/j.meatsci.2012.08.010. View

Huang L, Zhao J, Chen Q, Zhang Y . Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem. 2013; 145:228-36. DOI: 10.1016/j.foodchem.2013.06.073. View

Prieto N, Roehe R, Lavin P, Batten G, Andres S . Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Sci. 2010; 83(2):175-86. DOI: 10.1016/j.meatsci.2009.04.016. View

Zou X, Shi J, Hao L, Zhao J, Mao H, Chen Z . In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Anal Chim Acta. 2011; 706(1):105-12. DOI: 10.1016/j.aca.2011.08.026. View

Khulal U, Zhao J, Hu W, Chen Q . Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chem. 2015; 197 Pt B:1191-9. DOI: 10.1016/j.foodchem.2015.11.084. View

Liu L, Cozzolino D, Cynkar W, Gishen M, Colby C . Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J Agric Food Chem. 2006; 54(18):6754-9. DOI: 10.1021/jf061528b. View

Lin H, Chen Q, Zhao J, Zhou P . Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations. J Pharm Biomed Anal. 2009; 50(5):803-8. DOI: 10.1016/j.jpba.2009.06.040. View

Xiong Z, Sun D, Pu H, Xie A, Han Z, Luo M . Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem. 2015; 179:175-81. DOI: 10.1016/j.foodchem.2015.01.116. View

Kamruzzaman M, ElMasry G, Sun D, Allen P . Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta. 2012; 714:57-67. DOI: 10.1016/j.aca.2011.11.037. View

10.

Li H, Sun X, Pan W, Kutsanedzie F, Zhao J, Chen Q . Feasibility study on nondestructively sensing meat's freshness using light scattering imaging technique. Meat Sci. 2016; 119:102-9. DOI: 10.1016/j.meatsci.2016.04.031. View

11.

Saraiva C, Vasconcelos H, Almeida J . A chemometrics approach applied to Fourier transform infrared spectroscopy (FTIR) for monitoring the spoilage of fresh salmon (Salmo salar) stored under modified atmospheres. Int J Food Microbiol. 2016; 241:331-339. DOI: 10.1016/j.ijfoodmicro.2016.10.038. View

12.

Kutsanedzie F, Chen Q, Hassan M, Yang M, Sun H, Rahman M . Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. Food Chem. 2017; 240:231-238. DOI: 10.1016/j.foodchem.2017.07.117. View

13.

Ye X, Iino K, Zhang S . Monitoring of bacterial contamination on chicken meat surface using a novel narrowband spectral index derived from hyperspectral imagery data. Meat Sci. 2016; 122:25-31. DOI: 10.1016/j.meatsci.2016.07.015. View

14.

OuYang Q, Zhao J, Pan W, Chen Q . Real-time monitoring of process parameters in rice wine fermentation by a portable spectral analytical system combined with multivariate analysis. Food Chem. 2015; 190:135-141. DOI: 10.1016/j.foodchem.2015.05.074. View

15.

Pu H, Sun D, Ma J, Cheng J . Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. Meat Sci. 2014; 99:81-8. DOI: 10.1016/j.meatsci.2014.09.001. View

16.

Holl L, Behr J, Vogel R . Identification and growth dynamics of meat spoilage microorganisms in modified atmosphere packaged poultry meat by MALDI-TOF MS. Food Microbiol. 2016; 60:84-91. DOI: 10.1016/j.fm.2016.07.003. View

17.

Ahmed F . Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer. 2005; 4:29. PMC: 1208946. DOI: 10.1186/1476-4598-4-29. View

18.

Uhr J, Huebschman M, Frenkel E, Lane N, Ashfaq R, Liu H . Molecular profiling of individual tumor cells by hyperspectral microscopic imaging. Transl Res. 2012; 159(5):366-75. PMC: 3337082. DOI: 10.1016/j.trsl.2011.08.003. View

19.

Gaston E, Frias J, Cullen P, ODonnell C, Gowen A . Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem. 2010; 58(10):6226-33. DOI: 10.1021/jf100501q. View