Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analysis of maize kernels

Journal of Agricultural and Food Chemistry
Christian NansenXiquan Gao

Abstract

Development of robust analytical procedures is critical when using hyperspectral imaging technology in food technology and agriculture. This study used near-isogenic inbred corn lines to address two basic questions: (1) To what extent is classification accuracy increased by grinding maize kernels? (2) Can the classification accuracy of two near-isogenic inbred lines be increased by using a spectral filter to classify only certain hyperspectral profiles from each image cube? Whole kernels and ground kernels in two particle intervals, 0.250-0.354 mm (size 1) and 0.354-0.841 mm (size 2), were examined. Spectral profiles acquired from ground kernels had higher spectral repeatability than data collected from whole kernels. The classification error of discriminant functions from whole kernels was >3 times lower than that of size 1 ground particles. Applying a spectral filter to input data had negligible effect on classifications of hyperspectral profiles from whole kernels and size 2 ground particles, but for size 1 ground particles a considerable increase in accuracy was observed. Independent validation confirmed that distinction between wild type and mutant inbred maize lines could be conducted with >80% accuracy after the proposed...Continue Reading

Citations

Sep 22, 2011·Optics Express·Christian Nansen
Mar 11, 2015·Journal of Photochemistry and Photobiology. B, Biology·Christian NansenShane R Turner
Sep 14, 2016·Critical Reviews in Food Science and Nutrition·Kate SendinMarena Manley
Oct 29, 2013·Applied Spectroscopy·Christian NansenJose Roberto Parra
Jun 12, 2010·Applied Spectroscopy·Christian NansenRand Swanson
May 27, 2009·Journal of Agricultural and Food Chemistry·Paul R WileyRobert S Anderssen
Dec 24, 2013·The Journal of Experimental Biology·Christian NansenJose Roberto Postali Parra

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