Quantitative and non-destructive evaluation of ground beef based on multi-spectral imaging

被引:0
作者
Gutierrez-Navarro, Omar [1 ]
Campos-Delgado, Daniel U. [2 ]
Casillas Penuelas, Rafael A. [3 ]
Haubi Segura, Carlos U. [4 ]
机构
[1] Univ Autonoma Aguascalientes, Ctr Ciencias Ingn, Dept Biomed Engn, Aguascalientes, Aguascalientes, Mexico
[2] Univ Autonoma San Luis Potosi, Fac Sci, San Luis Potosi, San Luis Potosi, Mexico
[3] Univ Autonoma Aguascalientes, Ctr Ciencias Agr, Dept Food Sci, Aguascalientes, Aguascalientes, Mexico
[4] Univ Autonoma Aguascalientes, Ctr Ciencias Agr, Dept Vet Sci, Aguascalientes, Aguascalientes, Mexico
来源
2020 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) | 2020年
关键词
spectral unmixing; multispectral imaging; non-destructive evaluation; NEAR-INFRARED REFLECTANCE; MEAT; IDENTIFICATION;
D O I
10.1109/ICIT45562.2020.9067300
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The quality of meat-based products is usually tested by subjective and analytical methods. There are laboratory tests which can accurately estimate the content of a sample. Yet, they imply the sample destruction. In addition, they are time consuming and not suitable for industrial applications. Spectral unmixing is wide popular in remote sensing and biomedical applications for a quantitative analysis of an image. In this study, we apply an optical characterization of a ground-beef sample by blind linear unmixing. We prepare samples of ground beef with fixed fat/protein content. The samples are employed to evaluate the characterization provided by linear unmixing of multi-spectral data. We use an eight-band multi-spectral camera and halogen lamps as illumination source. A constrained quadratic optimization algorithm is employed to estimate end members and their abundances in the sample. The linear unmixing was applied to estimate four end-members and their abundances in the ground beef samples. These abundances match the visual characteristics of the sample such as positions with high concentration of fat.
引用
收藏
页码:680 / 685
页数:6
相关论文
共 21 条
[1]  
[Anonymous], 2010, Hyperspectral imaging for food quality analysis and control: Elsevier
[2]   Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [J].
Bioucas-Dias, Jose M. ;
Plaza, Antonio ;
Dobigeon, Nicolas ;
Parente, Mario ;
Du, Qian ;
Gader, Paul ;
Chanussot, Jocelyn .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (02) :354-379
[3]  
Campos-Delgado D. U., 2018, LATIN AM OPTICS PHOT, pTu4A
[4]  
Chang C.I., 2013, Hyperspectral Data Processing: Algorithm Design and Analysis
[5]   Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety [J].
Chen, Quansheng ;
Zhang, Chaojie ;
Zhao, Jiewen ;
Ouyang, Qin .
TRAC-TRENDS IN ANALYTICAL CHEMISTRY, 2013, 52 :261-274
[6]  
COLOR SHADES Lab, 2018, COLOR SHADES LAB
[7]  
Cornforth D.P. M.C. Hunt., 2008, AMSA WHITE PAPER SER, V2, P1
[8]   Identification of animal meat muscles by visible and near infrared reflectance spectroscopy [J].
Cozzolino, D ;
Murray, I .
LEBENSMITTEL-WISSENSCHAFT UND-TECHNOLOGIE-FOOD SCIENCE AND TECHNOLOGY, 2004, 37 (04) :447-452
[9]   Quadratic blind linear unmixing: A graphical user interface for tissue characterization [J].
Gutierrez-Navarro, O. ;
Campos-Delgado, D. U. ;
Arce-Santana, E. R. ;
Jo, Javier A. .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2016, 124 :148-160
[10]  
Holmer A., 2019, Journal of Spectral Imaging, V8, pa9, DOI 10.1255/jsi.2019.a9