The Bovine HapMap Consortium* The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.T he emergence of modern civilization was accompanied by adaptation, assimilation, and interbreeding of captive animals. In cattle (Bos taurus), this resulted in the development of individual breeds differing in, for example, milk yield, meat quality, draft ability, and tolerance or resistance to disease and pests. However, despite mapping and diversity studies (1-5) and the identification of mutations affecting some quantitative phenotypes (6-8), the detailed genetic structure and history of cattle are not known.Cattle occur as two major geographic types, the taurine (humpless-European, African, and Asian) and indicine (humped-South Asian, and East African), which diverged >250 thousand years ago (Kya) (3). We sampled individuals representing 14 taurine (n = 376), three indicine (n = 73) (table S1), and two hybrid breeds (n = 48), as well as two individuals each of Bubalus quarlesi and Bubalus bubalis, which diverged from Bos taurus~1.25 to 2.0 Mya (9, 10). All breeds except Red Angus (n = 12) were represented by at least 24 individuals. We preferred individuals that were unrelated for ≥4 generations; however, each breed had one or two sire, dam, and progeny trios to allow assessment of genotype quality.Single-nucleotide polymorphisms (SNPs) that were polymorphic in many populations were primarily derived by comparing whole-genome sequence reads representing five taurine and one indicine breed to the reference genome assembly obtained from a Hereford cow (10) (table S2). This led to the ascertainment of SNPs with high minor allele frequencies (MAFs) within the discovery breeds (table S5). Thus, as expected, with trio progeny removed, SNPs discovered within the taurine breeds had higher average MAFs

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Near infrared (NIR) hyperspectral imaging and hyperspectral image analysis were evaluated for their potential to distinguish between Fusarium verticillioides infected and sound whole maize (Zea mays L.) kernels. Hyperspectral images of infected and sound kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Background, bad pixels and shading were removed using exploratory principal component analysis (PCA) on absorbance images. PCA could be used effectively on the cleaned images to identify classes including infected and non-infected regions on individual kernels. A distinct difference between infected and sound kernels along principal component (PC) one with two distinguishable clusters was found. The loading line plot of the first PC of the sisuChema hypercube showed important absorbance peaks for the two classes, i.e. 1960 nm and 2100 nm for the infected class and 1450 nm, 2300 nm and 2350 nm for the non-infected class. Partial least squares discriminant analysis (PLS-DA) was applied. The coefficient of determination was 0.73 for the MatrixNIR image and 0.86 for the sisuChema image, both after three PLS components. These PLS-DA models could be used to calculate predictions on a test set image. The predictions could be shown as prediction images and an acceptable root mean square error of prediction was obtained (0.23). NIR hyperspectral imaging has the potential to be used as a rapid, objective means of indentifying fungal infected maize kernels and infected regions.

Near infrared hyperspectral images (HSI) were recorded for whole yellow maize kernels (commercial hybrids) defined as either hard, intermediate, or soft by experienced maize breeders. The images were acquired with a linescan (pushbroom) instrument using a HgCdTe detector. The final image size was 570 x 219 pixels in 239 wavelength bands from 1000 to 2498 nm in steps of approximately 6.5 nm. Multivariate image cleaning was used to remove background and optical errors, in which about two-thirds of all pixels were removed. The cleaned image was used to calculate a principal component analysis (PCA) model after multiplicative scatter correction (MSC) and mean-centering were applied. It was possible to find clusters representing vitreous and floury endosperm (different types of endosperm present in varying ratios in hard and soft kernels) as well as a third type of endosperm by interactively delineating polygon based clusters in the score plot of the second and fourth principal components and projecting the results on the image space. Chemical interpretation of the loading line plots shows the effect of starch density and the protein matrix. The vitreous and floury endosperm clusters were used to make a partial least-squares discriminant analysis (PLS-DA) model, using four components, with a coefficient of determination (R(2)) for the y data (kernel hardness category) for the training set of over 85%. This PLS-DA model could be used for prediction in a test set. We show how the prediction images can be interpreted, thus confirming the validity of the PCA classification. The technique presented here is very powerful for laboratory studies of small cereal samples in order to produce localized information.

The requirements of cereal research, as well as grading and evaluation of food products, have encouraged the development of nondestructive, rapid, and accurate analytical techniques to evaluate grain quality and safety. NIR hyperspectral imaging integrates spectroscopy and imaging techniques in one analytical system, allowing direct identification of chemical components and their distribution within the sample. It is a promising technique that may be implemented on-line, enabling the cereal industry to move away from subjective, manual classification and measuring methods. NIR hyperspectral imaging has gained popularity for rapidly acquiring information to enable the quantification, identification or differentiation of a variety of cereal properties. The technique can potentially replace multiple conventional chemical, microbial or physical tests with a single, automated image acquisition. Individual kernels can be analyzed nondestructively, enabling one to follow changes in the same kernel over time (e.g. fungal development). Although NIR hyperspectral imaging has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties. This review outlines the theory and principles of NIR hyperspectral imaging, and focuses specifically on its application in cereal science research and industry.