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

Youngjoo Lee; Sungho Shin; Sungchul Kim; Nguyen Thien; Kyoobin Lee; Jae Gwan Kim

doi:10.1117/12.2545967

9 March 2020 Classification of meat freshness based on deep learning using data from diffuse reflectance spectroscopy (Conference Presentation)

Youngjoo Lee, Sungho Shin, Sungchul Kim, Nguyen Thien, Kyoobin Lee, Jae Gwan Kim

Author Affiliations +

Proceedings Volume 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII; 112431C (2020) https://doi.org/10.1117/12.2545967
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Youngjoo Lee, Sungho Shin, Sungchul Kim, Nguyen Thien, Kyoobin Lee, and Jae Gwan Kim "Classification of meat freshness based on deep learning using data from diffuse reflectance spectroscopy (Conference Presentation)", Proc. SPIE 11243, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVIII, 112431C (9 March 2020); https://doi.org/10.1117/12.2545967

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KEYWORDS

Diffuse reflectance spectroscopy

Chromophores

Data modeling

Process modeling

Reflectance spectroscopy

Spectroscopy

Neural networks

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