Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 38-48 |
| Number of pages | 11 |
| Journal | Journal of Food Engineering |
| Volume | 109 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 1 2012 |
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All Science Journal Classification (ASJC) codes
- Food Science
Cite this
Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia)-infected onions. / Wang, Weilin; Li, Changying; Tollner, Ernest W.; Gitaitis, Ronald D.; Rains, Glen Christopher.
In: Journal of Food Engineering, Vol. 109, No. 1, 01.03.2012, p. 38-48.Research output: Contribution to journal › Article
}
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 Christopher
PY - 2012/3/1
Y1 - 2012/3/1
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
VL - 109
SP - 38
EP - 48
JO - Journal of Food Engineering
JF - Journal of Food Engineering
SN - 0260-8774
IS - 1
ER -