| dc.contributor.author |
Wang, W |
en |
| dc.contributor.author |
Li, C |
en |
| dc.contributor.author |
Tollner, EW |
en |
| dc.contributor.author |
Gitaitis, RD |
en |
| dc.contributor.author |
Rains, GC |
en |
| dc.date.accessioned |
2014-06-06T06:52:05Z |
|
| dc.date.available |
2014-06-06T06:52:05Z |
|
| dc.date.issued |
2012 |
en |
| dc.identifier.issn |
02608774 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.jfoodeng.2011.10.001 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5834 |
|
| dc.subject |
Food quality and safety |
en |
| dc.subject |
Hyperspectral imaging |
en |
| dc.subject |
Log-ratio image |
en |
| dc.subject |
Onion |
en |
| dc.subject |
Sour skin |
en |
| dc.subject |
Support vector machine |
en |
| dc.subject.other |
Food quality and safeties |
en |
| dc.subject.other |
Hyperspectral imaging |
en |
| dc.subject.other |
Log-ratio images |
en |
| dc.subject.other |
Onion |
en |
| dc.subject.other |
Support vector |
en |
| dc.subject.other |
Discriminant analysis |
en |
| dc.subject.other |
Food safety |
en |
| dc.subject.other |
Imaging systems |
en |
| dc.subject.other |
Losses |
en |
| dc.subject.other |
Support vector machines |
en |
| dc.subject.other |
Principal component analysis |
en |
| dc.subject.other |
Allium cepa |
en |
| dc.subject.other |
Burkholderia cepacia |
en |
| dc.title |
Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia)-infected onions |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.jfoodeng.2011.10.001 |
en |
| heal.publicationDate |
2012 |
en |
| heal.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. © 2011 Elsevier Ltd. All rights reserved. |
en |
| heal.journalName |
Journal of Food Engineering |
en |
| dc.identifier.issue |
1 |
en |
| dc.identifier.volume |
109 |
en |
| dc.identifier.doi |
10.1016/j.jfoodeng.2011.10.001 |
en |
| dc.identifier.spage |
38 |
en |
| dc.identifier.epage |
48 |
en |