Dietary vitamin E inhibits the trans 10-18:1 shift in beef backfat

[EN]Forty feedlot steers were fed a barleygrain-based finishing diet typical for western Canada, with two levels of supplementary vitamin E (468 or 1068 IU head_1 d_1) and the effect on backfat trans-18:1 isomeric profile was determined. Feeding 1068 IU vitamin E reduced the total trans-18:1 content in backfat (P<0.01), as well as the percentage of trans 10-18:1 (P<0.001), which are related to an increased risk for cardiovascular diseases. On the other hand, trans 11-18:1 (vaccenic acid) the precursor for cis 9,trans 11- 18:2 (rumenic acid), which have several purported health benefits, increased (P<0.01). Vitamin E could, therefore, be used to decrease trans-18:1 in beef and improve its isomeric profile.

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification