Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Comparison between visible/NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses

[+] Author Affiliations
Kurt C. Lawrence, William R. Windham, Bosoon Park, Douglas P. Smith

U.S. Dept. of Agriculture (USA)

Gavin H. Poole

Institute for Technology Development (USA)

Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, 35 (March 30, 2004); doi:10.1117/12.516153
Text Size: A A A
From Conference Volume 5271

  • Monitoring Food Safety, Agriculture, and Plant Health
  • Bent S. Bennedsen; Yud-Ren Chen; George E. Meyer; Andre G. Senecal; Shu-I Tu
  • Providence, RI | October 27, 2003

abstract

The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The patented method utilizes a three step approach to contaminant detection. Spectra of homogenous samples of feces, ingesta (undigested food particles), and skin were first collected. Then those spectra were evaluated with multivariate analysis techniques to identify significant wavelength regions for further analysis. Hyperspectral data were then collected on contaminated poultry carcasses and information learned from the spectroscopic data was used to aide in hyperspectral data analysis. Finally, the results of the hyperspectral data were used to identify a few optimum wavelengths for use in a real-time multispectral imaging system. In this work, two techniques for developing spectral datasets and algorithms for classifying surface contaminants on poultry carcasses were explored. The first consisted of a scanning monochrometer that measured the average spectra of uncontaminated breast skin and fecal and ingesta contaminants. The second technique used regions of interest (ROI) from a hyperspectral image to collect spatially averaged spectra. Comparison of the spectra from each instrument showed variations in the spectra collected from similar samples. There was an offset of absorption values between the two instruments and the hyperspectral imaging system had better resolution at higher absorption wavelengths. Although both systems were calibrated prior to measuring, there was also a slight shift in absorption peaks between the two systems. Both techniques were able to classify contaminated skin from uncontaminated skin in a full cross-validated test set with better than 99% accuracy. However, when the classification model developed from the monochrometer spectra was applied to whole-carcass hyperspectral images, numerous common carcass features, such as exposed meat and wing-shadowed skin, were wrongly identified as false positives. Since spectra of entire poultry carcasses were available in the original hyperspectral dataset, the hyperspectral ROI technique allowed researchers to easily add the spectra of these false positives to the calibration dataset. New partial least squares regression models with meat and skin shadow spectra resulted in different principal component loadings and improved classification models. The classification model with the combined ROI spectra from skin, feces, ingesta, meat, and skin shadows gave a classification accuracy of 99.5%. When this model was compared to the original model developed from the monochrometer dataset on a few hyperspectral images of contaminated carcasses, fewer false positives were classified with the hyperspectral ROI model without sacrificing the accuracy of contaminant detection. Further research must be done to fully characterize the accuracy of the model.

© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Kurt C. Lawrence ; William R. Windham ; Bosoon Park ; Douglas P. Smith and Gavin H. Poole
"Comparison between visible/NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses", Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, 35 (March 30, 2004); doi:10.1117/12.516153; http://dx.doi.org/10.1117/12.516153


Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).

Figures

Tables

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.