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Proceedings Paper

Classification of meat freshness based on deep learning using data from diffuse reflectance spectroscopy (Conference Presentation)

Paper Abstract

Met-myoglobin is a major component related to meat discoloration, and it gradually accumulates over time after the meat is slaughtered. Recently, studies have been conducted to observe the changes in the composition of met-myoglobin in the meat along with its storage time using Diffuse Reflectance Spectroscopy(DRS). DRS is an optical technique that is simple and can estimate the composition of chromophores without damaging the sample. However, since DRS requires high resolution and complicated fitting process, it is difficult to apply DRS to the mobile environment. Therefore, the purpose of our study is to classify the freshness of meat by extracting features from low spectral resolution diffuse reflectance spectrum by using the deep learning model. To improve the generality of the model, a data augmentation was used. To consider the applicability at low-resolution spectrometer, the diffuse reflectance spectrum was down-sampled 5, 10, 30 and 50 times.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII, 112431C (9 March 2020); doi: 10.1117/12.2545967
Show Author Affiliations
Youngjoo Lee, Gwangju Institute of Science and Technology (Korea, Republic of)
Sungho Shin, Gwangju Institute of Science and Technology (Korea, Republic of)
Sungchul Kim, Gwangju Institute of Science and Technology (Korea, Republic of)
Nguyen Thien, Gwangju Institute of Science and Technology (Viet Nam)
Kyoobin Lee, Gwangju Institute of Science and Technology (Korea, Republic of)
Jae Gwan Kim, Gwangju Institute of Science and Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 11243:
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII
Daniel L. Farkas; Attila Tarnok, Editor(s)

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