TY - JOUR
T1 - Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia)-infected onions
AU - Wang, Weilin
AU - Li, Changying
AU - Tollner, Ernest W.
AU - Gitaitis, Ronald D.
AU - Rains, Glen C.
N1 - Funding Information:
The authors gratefully acknowledge the financial support from the USDA NIFA Specialty Crop Research Initiative (Award No. 2009-51181-06010), Georgia Food Industry Partnership, and Vidalia Onion Committee. The authors also sincerely thank Dr. Chi Thai, Dr. Seung-Chul Yoon, Mr. Gary Burnham, and Mr. Tim Rutland for their insights and assistance on this study.
PY - 2012/3
Y1 - 2012/3
N2 - Sour skin (Burkholderia cepacia) is a major postharvest disease for onions and causes substantial production and economic losses in onion postharvest. In this study, a shortwave infrared hyperspectral imaging system was explored to detect sour skin. The hyperspectral reflectance images (950-1650 nm) of onions were obtained for the healthy and sour skin-infected onions. Principal component analysis conducted on the spectra of the healthy and sour skin-infected onions suggested that the neck area of the onion at two wavelengths (1070 and 1400 nm) was most indicative of the sour skin. Log-ratio images utilizing the two optimal wavelengths were used for two different image analysis approaches. The first method applied a global threshold (0.45) to segregate the sour skin-infected areas from log-ratio images. Using the pixel number of the segregated areas, Fisher's discriminant analysis recognized 80% healthy and sour skin-infected onions. The second classification approach used three parameters (max, contrast, and homogeneity) of the log-ratio images as the input features of support vector machine (Gaussian kernel, γ = 1.5), which discriminated 87.14% healthy and sour skin-infected onions. The result of this study can be used to further develop a multispectral imaging system to detect sour skin-infected onions on packing lines.
AB - Sour skin (Burkholderia cepacia) is a major postharvest disease for onions and causes substantial production and economic losses in onion postharvest. In this study, a shortwave infrared hyperspectral imaging system was explored to detect sour skin. The hyperspectral reflectance images (950-1650 nm) of onions were obtained for the healthy and sour skin-infected onions. Principal component analysis conducted on the spectra of the healthy and sour skin-infected onions suggested that the neck area of the onion at two wavelengths (1070 and 1400 nm) was most indicative of the sour skin. Log-ratio images utilizing the two optimal wavelengths were used for two different image analysis approaches. The first method applied a global threshold (0.45) to segregate the sour skin-infected areas from log-ratio images. Using the pixel number of the segregated areas, Fisher's discriminant analysis recognized 80% healthy and sour skin-infected onions. The second classification approach used three parameters (max, contrast, and homogeneity) of the log-ratio images as the input features of support vector machine (Gaussian kernel, γ = 1.5), which discriminated 87.14% healthy and sour skin-infected onions. The result of this study can be used to further develop a multispectral imaging system to detect sour skin-infected onions on packing lines.
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U2 - 10.1016/j.jfoodeng.2011.10.001
DO - 10.1016/j.jfoodeng.2011.10.001
M3 - Article
AN - SCOPUS:81055140090
VL - 109
SP - 38
EP - 48
JO - Journal of Food Engineering
JF - Journal of Food Engineering
SN - 0260-8774
IS - 1
ER -