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ARS Home » Southeast Area » New Orleans, Louisiana » Southern Regional Research Center » Food and Feed Safety Research » Research » Publications at this Location » Publication #267496
Title: Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

Author
item YAO, HAIBO - Mississippi State University
item HRUSKA, ZUZANA - Mississippi State University
item KINCAID, RUSSELL - Mississippi State University
item ONONYE, AMBROSE - Mississippi State University
item Brown, Robert
item Bhatnagar, Deepak
item Cleveland, Thomas

Submitted to: Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Publication Type: Proceedings
Publication Acceptance Date: 5/18/2011
Publication Date: 5/31/2011
Citation: Yao, H., Hruska, Z., Kincaid, R., Ononye, A., Brown, R.L., Bhatnagar, D., Cleveland, T.E. 2011. Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn. Proceedings of IEEE 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Conference, June 6-9, 2011, Lisbon, Portugal. No. 94.

Interpretive Summary:

Technical Abstract: Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression correlation as its fitness function. This algorithm was used for analyzing fluorescence hyperspectral images of aflatoxin contaminated corn kernels. The results showed that SPCR could produce results similar to the standard PCR approach. However, the data dimension was much less for the SPCR process. The SPCR correlation coefficient was 0.8 when 33 of the original 74 bands were used for the SPCR transformation. The results demonstrated that SPCR could be used as a combined dimension reduction and data analysis tool for high dimensionality data processing.