Traditional Spectral Analysis

Figure 5. Relative Reflectance Spectra on Day 2 – 24 Hours Elapsed for 3 Filet Samples
Figure 6. Beef Filet Sample 1 Over Time

Taking an average of the spectral response of a small area of pixels from the resulting image data-cube, a representative spectral reflectance profile for a given sample can be obtained. A survey of the features indicates a change in the intensity just below 800 nm, a band-pass associated with water content in muscle tissue. The intensity decreases over time, which is consistent with the expected moisture loss due to aging. In addition, there is a shift of the entire spectra towards the longer wavelengths. This is consistent with the increased redness in the aged samples, likely due to the deoxymyoglobin transition to oxymyoglobin. These characteristics are seen for individual and groups of filet and ground beef samples (Figures 5, 6 and 7).

Figure 7. Relative Reflectance Spectra on Day 2 – 24 Hours Elapsed for 3 Ground Beef Samples

Figure 8 shows a clear spectral shift towards longer wavelengths (red) and a broadening (whitening) trend was observed along with some detail structural changes particularly in the 600-700 nm range. These results are consistent with published spectral and statistical studies[6].

Figure 8. Images and averaged spectra of lean ground beef with varying proportions of added minced fat

Improved Spectral Analysis using Hyperspectral and Machine Learning

While analysis of individual spectra can provide specific characteristics of a single region or area of a sample of meat, it is an inefficient way to evaluate or inspect the entire product. The full value of hyperspectral imaging is in providing quickly actionable information for the entire product. To do this, the classification techniques discussed earlier are leveraged.

Figure 9. (Top) filet samples at 24 hours elapsed. Spectra for each pixel are first normalized to remove illumination nonuniformity, a PCA with 10 components was then used for dimension reduction and a K-means algorithm with 6 clusters, initialized randomly used to classify and false color the end-members. Hence, each color in the image represents one cluster. (Bottom) An example spectrum for that cluster.
Figure 10. Ground beef samples at 24 hours. Elapsed classified using super-pixel segmentation method (PSMPS) in Gerbil. Note higher end-member (green and red false colors) overlap between Sample 2 and 3 (7 hours apart).

Applying machine learning techniques such as PCA to identify the most likely endmembers and K-Means Clustering to group or classify them, for example, can improve the visualization of these components markedly. In Figure 9, the spectra for each pixel are first normalized to remove illumination nonuniformity, a PCA was then used for dimension reduction and a K-means algorithm used to classify and false color the end-members. Hence, each color in the image represents one cluster. An example spectrum for that cluster is also shown. Figure 10 shows the ground beef sample classified by a super-pixel segmentation method (PSMPS) in Gerbil[7].

Figure 11. Ground beef samples with varying percentage of fat content (top) classified using K-Means clustering algorithm to cluster or group by similarity to the former image (bottom).

Similarly, images can be classified or segmented using machine learning algorithms by spectral components or end-members associated with each level of fat content. False coloring these grouped end-members shows a consistent trend associated with increasing percentages of fat with respect to lean red meat in the samples (Figure 11).

Summary

In addition to spatial information which can be useful for size sorting and other machine vision tasks, by identifying distinctions in images which are not only invisible to the eye but also to color (RGB) cameras and even multi-spectral imagers, hyperspectral imaging cameras can provide rapid assessment of product quality metrics such as moisture, aging, protein and fat content, marbling and more.

In the examples discussed, the HinaLea hyperspectral imaging system was able to detect a decrease in intensity near 800 nm indicating a loss of water content or moisture over time. In addition, a red shift of the spectral profile over time, likely due to deoxymyoglobin transition to oxymyoglobin, was also seen. The classified images show end-member overlap due to time-proximity. A spectral shift and broadening along with changes in the 600-700 nm range proportional to fat content was also observed.

These examples establish a basic framework for development of correlative classification models for hyperspectral determination of these quality parameters. They thus represent a means by which to provide intuitive actionable information at the operator level and show the potential for further refinement in the automated decision making and product sorting process.

The unique combination of portability and ability to dynamically change spectral range and band-pass capability of the HinaLea technology means that the same instrument can be configured for a wide variety of parameters and points of interest in the inspection line to both optimize accuracy and reduce the time to capture image. Finally, the cost advantages of the technology relative to other hyper-spectral imaging solutions also vastly improves access for all such potential uses.

This article was written by Alexandre Fong M.Sc., MBA; Mark Hsu, Ph.D.; Mersina Simanski, BSEE; and Monika Patel, Ph.D.; Hinalea Imaging (Emeryville, CA). For more information, contact Mr. Fong at This email address is being protected from spambots. You need JavaScript enabled to view it., or visit here.

References

  1. USDA/FSIS, “Water in Meat and Poultry”, Food Safety Information, May 2011
  2. M.J.A. den Hertog-Meischke , R.J.L.M. van Laack & F.J.M. Smulders (1997) “The water-holding capacity of fresh meat”, Veterinary Quarterly, 19:4, 175-181, DOI: 10.1080/01652176.1997.9694767
  3. Firtha, Ferenc, Anita Jasper and László Friedrich. “Spectral and Hyperspectral Inspection of Beef Ageing State.” (2012)
  4. C. H., Poole, G. H. , Parker, P. E. and Gottwald, T. R.(2010) 'Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging', Critical Reviews in Plant Sciences, 29: 2, 59 — 107, DOI: 10.1080/ 07352681003617285, http://dx.doi.org/ 10.1080/07352681003617285
  5. Hod Finkelstein, Ron R. Nissim and Mark J. Hsu, TruTag Technologies Inc., “Next- generation intelligent hyperspectral imagers”.
  6. Santosh Lohumi, S. D. Lee, H. S. Lee, M. H. Kim and W. K. Cho, “Application of Hyperspectral Imaging for Characterization of Intramuscular Fat Distribution in Beef”, Infrared Physics & Technology 74 · December 2015, DOI: 10.1016/ j.infrared.2015.11.004}
  7. Gerbil Hyperspectral Imaging and Visualization Framework, http://gerbilvis.org/