Dataset Information

Analysis of subcutaneous swine fat via deep Raman spectroscopy using a fiber-optic probe.

ABSTRACT: Since the fat content of pork is a deciding factor in meat quality grading, the use of a noninvasive subcutaneous probe for real-time in situ monitoring of the fat components is of importance to vendors and other interested parties. In this work, we developed a spectroscopic method using a fiber-optic probe for subcutaneous fat analysis that utilizes spatially offset Raman spectroscopy (SORS). Here, normalized Raman spectra were acquired as a function of spatial offset, and the relative composition of fat-to-skin was determined. We found that the Raman intensity ratio varied disproportionately depending on the fat content and that the variations of the slope were correlated to the thickness of the fat layer. Furthermore, ordinary least square (OLS) regression using two components indicated that the depth-resolved SORS spectra reflected the relative thickness of the fat layer. We concluded that the local distribution of subcutaneous fat could be measured noninvasively using a pair of fiber-optic probes.

SUBMITTER: Kang JW

PROVIDER: S-EPMC7329574 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Analysis of subcutaneous swine fat via deep Raman spectroscopy using a fiber-optic probe.

Kang Jeon Woong JW Lim Soo Yeong SY Galindo Luis H LH Yoon Hongman H Dasari Ramachandra R RR So Peter T C PTC Kim Hyung Min HM

The Analyst 20200522 13

Since the fat content of pork is a deciding factor in meat quality grading, the use of a noninvasive subcutaneous probe for real-time in situ monitoring of the fat components is of importance to vendors and other interested parties. In this work, we developed a spectroscopic method using a fiber-optic probe for subcutaneous fat analysis that utilizes spatially offset Raman spectroscopy (SORS). Here, normalized Raman spectra were acquired as a function of spatial offset, and the relative composit ...[more]

PMID: 32441278

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Project description:Hyaline cartilage and mechanically inferior fibrocartilage consisting of mixed collagen types are frequently found together in repairing articular cartilage. The present study seeks to develop methodology to identify collagen type and other tissue components using Fourier transform infrared (FTIR) spectral evaluation of matrix composition in combination with multivariate analyses. FTIR spectra of the primary molecular components of repair cartilage, types I and II collagen, and aggrecan, were used to develop multivariate spectral models for discrimination of the matrix components of the tissues of interest. Infrared imaging data were collected from bovine bone, tendon, normal cartilage, meniscus and human repair cartilage tissues, and composition predicted using partial least squares analyses. Histology and immunohistochemistry results were used as standards for validation. Infrared fiber optic probe spectral data were also obtained from meniscus (a tissue with mixed collagen types) to evaluate the potential of this method for identification of collagen type in a minimally-invasive clinical application. Concentration profiles of the tissue components obtained from multivariate analysis were in excellent agreement with histology and immunohistochemistry results. Bone and tendon showed a uniform distribution of predominantly type I collagen through the tissue. Normal cartilage showed a distribution of type II collagen and proteoglycan similar to the known composition, while in repair cartilage, the spectral distribution of both types I and II collagen were similar to that observed via immunohistochemistry. Using the probe, the outer and inner regions of the meniscus were shown to be primarily composed of type I and II collagen, respectively, in accordance with immunohistochemistry data. In summary, multivariate analysis of infrared spectra can indeed be used to differentiate collagen type I and type II, even in the presence of proteoglycan, in connective tissues, using both imaging and fiber optic methodology. This has great potential for clinical in situ applications for monitoring tissue repair.

Dataset Information

Analysis of subcutaneous swine fat via deep Raman spectroscopy using a fiber-optic probe.

Publications

Analysis of subcutaneous swine fat via deep Raman spectroscopy using a fiber-optic probe.

Similar Datasets

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