Dear Gao Yu Qing,
Thks yr assist. I remember now, vertebrates and the others.
Certainly seems that commercial X-ray systems have not got so far on this one despite massive interest via the wheat industry, a review (2008) here –
x-ray detection contaminants in food industry 2008.pdf 367.72KB
14 downloads
For the benefit of anyone (still) interested in X-ray, this link seemed quite informative -
http://www.foodengin...000000000691953
Plus, some indications of (presumably research) projects getting somewhere, see below –
1.
Karunakaran, C. Jayas, D.S. White, N.D.G. This paper appears in: Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on
Issue Date: 2002 ,Volume: 2 ,On page(s): 902 - 907 vol.2
Abstract
The Canada Grain Act imposes a zero tolerance for stored-product insects in grain. Incoming and export grain in the primary and terminal elevators, respectively is inspected for the presence of insects using Berlese funnels. This method takes 5 to 6 h to extract the larval stages of the rusty grain beetle, Cryptolestes ferrugineus (Stephens), the most common stored-grain insect in Canada. During this time the grain may have been binned in the elevator or loaded on to ships. This results in manifestation of infestation and cross contamination of stored-grain in the grain handling system. The feasibility of using real-time soft X-ray images to detect insect infestations in wheat was determined in this study. Uninfested and infested Canadian Western Red Spring wheat kernels fed on by different life stages of C. ferrugineus were X-rayed at 15 kV potential and 65 μA current. Five hundred uninfested and 440 infested kernels were X-rayed at different times for the four larval instars, pupae, and adult stages of the insect infesting wheat kernels. Histogram groups, histogram and shape moments, and textural features using co-occurrence and run length matrices were extracted from the X-ray images. The 57 extracted features were used to identify uninfested and infested kernels by the non-parametric classifier and multi-layer feed-forward backpropagation neural network (BPNN). The non-parametric classifier correctly identified 83.3% of the sound kernels. The BPNN identified 75.7% of sound kernels and classed 24.3% as infested. More than 87% of wheat kernels infested by larvae were identified as infested by the nonparametric classifier and BPNN. More than 96% of kernels infested by the pupal and adult stages of C. ferrugineus were correctly classified by the nonparametric classifier and BPNN methods.
2.
Journal of Stored Products Research, Volume 38, Issue 1, 2002, Pages 75-86
A comparison of screening methods for insect contamination in wheat
Bob Brader , , a, Rachel C. Leea, Rudy Plarre1, , b, Wendell Burkholderb, G. Barrie Kittoc, Chuan Kao2, , d, Lynn Polstond, Eleanora Dorneanu3, , e, Ioana Szabof, Bill Meadg, Bob Rouseh, Don Sullinsh and Royal Denningi
Abstract
In collaboration with the United States Department of Agriculture and a number of major milling companies, the “Insect-Detect” immunoassay for analyzing insect contamination in grains has been compared with three more traditional methods, X-ray analysis, cracking and flotation, and the insect fragment test (IFT). Testing was carried out in blind fashion using clean wheat samples that were spiked with differing numbers of grain kernels infested with late instar larvae of the granary weevil (Sitophilus granarius (L.)). Three different laboratories analyzed the samples for each of the four methods. The collaborative trials showed that the insect immunoassay clearly provided the most accurate measurement of actual insect infestation, followed by X-ray analysis. While both cracking and flotation and IFT procedures provided a general measure of contamination, they showed much greater variability
(I guess, as described here, the last one is primarily an analytical tool rather than a screening system, despite the title)
This link is perhaps an estimate of the (human) problem magnitude –
http://www.ggbzw.com...t_knowing.shtml
(truly amazing website! )
Rgds / Charles.C
PS thanks Mike for the "link". Impressive but does seem to be a work in progress at the moment? Somewhat unusual scoped thank you plug on 1st page pdf also