Increasing concerns about adulterated meat encouraged industry looking for new non-invasive methods for rapid accurate meat quality assessment. Main meat chromophores (myoglobin, oxy-myoglobin, fat, water, collagen) are characterized by close comparable absorption in visible to near-infrared (NIR) spectral region. Therefore, structural and compositional variations in meat may lead to relative differences in the absorption of light. Utilizing typical fiber-optic probes and integrating sphere, a degradation of pork samples freshness was observed at room temperature referring to the relative changes in absorbance of main meat chromophores. The application of principal component analysis (PCA) used for examination of measured absorbance spectra revealed more detailed sub-stages of freshness, which are not observed by the conventional analysis of the reflectance spectra. The results show a great potential of the combined application of optical-NIR spectroscopy with complementary use of PCA approach for assessing meat quality and monitoring relative absorbance alternation of oxymyoglobin and myoglobin in visible, and fat, water, collagen in NIR spectral ranges.
|Number of pages||11|
|Journal||Journal of Physics Communications|
|Publication status||Published - 11 Sep 2020|
Bibliographical noteOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Funding: This work received funding from the I4FUTURE, European Union’s Horizon 2020 research and innovation
program under the Marie Sklodowska-Curie (grant agreement No 713606) and partially from the Academy of
Finland (grants: 314369, 325097), the ATTRACT project funded by the EC under Grant Agreement 777222,
MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005), and National Research Tomsk State
University Academic D.I. Mendeleev Fund Program.
- Absorption spectra
- Meat freshness
- Monte Carlo simulation
- Principal component analysis (PCA)
- Probing depth
- Sampling volume
- Visible/NIR spectroscopy