Grain Inspection, Packers and Stockyards Administration

Based on updates to signal and image processing technology made in the last two decades, this text examines the most recent research results pertaining to Quaternion Fourier Transforms. QFT is a central component of processing color images and complex valued signals. The book's attention to mathematical concepts, imaging applications, and Matlab compatibility render it an irreplaceable resource for students, scientists, researchers, and engineers.

Abstract Independent Component Analysis (ICA) plays an important role in biomedical engineering. Indeed, the complexity of processes involved in biomedicine and the lack of reference signals make this blind approach a powerful tool to extract sources of interest. However, in practice, only few ICA algorithms such as SOBI, (extended) InfoMax and FastICA are used nowadays to process biomedical signals. In this paper we raise the question whether other ICA methods could be better suited in terms of performance and computational complexity. We focus on ElectroEncephaloGraphy (EEG) data denoising, and more particularly on removal of muscle artifacts from interictal epileptiform activity. Assumptions required by ICA are discussed in such a context. Then fifteen ICA algorithms, namely JADE, CoM2, SOBI, SOBIrob, (extended) InfoMax, PICA, two different implementations of FastICA, ERICA, SIMBEC, FOBIUMJAD, TFBSS, ICAR3, FOOBI1 and 4- CANDHAPc are briefly described. Next they are studied in terms of performance and numerical complexity. Quantitative results are obtained on simulated epileptic data generated with a physiologically-plausible model. These results are also illustrated on real epileptic recordings.

New hyperspectral missions will collect huge amounts of hyperspectral data. In addition, it is possible now to acquire time series and multiangular hyperspectral images. The process and analysis of these big data collections will require common hyperspectral techniques to be adapted or reformulated. The tensor decomposition, which is also known as multiway analysis, is a technique to decompose multiway arrays, i.e., hypermatrices with more than two dimensions (ways). Hyperspectral time series and multiangular acquisitions can be represented as a three-way tensor. Here, we apply canonical polyadic (CP) tensor decomposition techniques to the blind analysis ohyperspectral big data. In order to do so, we use a novel compression-based nonnegative CP decomposition. We show that the proposed methodology can be interpreted as multilinear blind spectral unmixing, i.e., a higher order extension of the widely known spectral unmixing. In the proposed approach, the big hyperspectral tensor is decomposed in three sets of factors, which can be interpreted as spectral signatures, their spatial distribution, and temporal/angular changes. We provide experimental validation using a study case of the snow coverage of the French Alps during the snow season.

ABSTRACT The accuracy of using near‐infrared spectroscopy (NIRS) for predicting 186 grain, milling, flour, dough, and breadmaking quality parameters of 100 hard red winter (HRW) and 98 hard red spring (HRS) wheat and flour samples was evaluated. NIRS shows the potential for predicting protein content, moisture content, and flour color b * values with accuracies suitable for process control (R 2 > 0.97). Many other parameters were predicted with accuracies suitable for rough screening including test weight, average single kernel diameter and moisture content, SDS sedimentation volume, color a * values, total gluten content, mixograph, farinograph, and alveograph parameters, loaf volume, specific loaf volume, baking water absorption and mix time, gliadin and glutenin content, flour particle size, and the percentage of dark hard and vitreous kernels. Similar results were seen when analyzing data from either HRW or HRS wheat, and when predicting quality using spectra from either grain or flour. However, many attributes were correlated to protein content and this relationship influenced classification accuracies. When the influence of protein content was removed from the analyses, the only factors that could be predicted by NIRS with R 2 > 0.70 were moisture content, test weight, flour color, free lipids, flour particle size, and the percentage of dark hard and vitreous kernels. Thus, NIRS can be used to predict many grain quality and functionality traits, but mainly because of the high correlations of these traits to protein content.

In this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter. Then, a detailed analysis is carried out to evaluate the potential of this new multivariate distribution. It is implemented in a hierarchical maximum likelihood segmentation algorithm. The segmentation results are shown on both synthetic and high-resolution PolSAR data at the X- and L-bands. Finally, some methods are examined to determine automatically the “optimal” number of segments in the final partition.

Implementation of multivariate and 2D extensions of singular spectrum analysis (SSA) by means of the R package Rssa is considered. The extensions include MSSA for simultaneous analysis and forecasting of several time series and 2D-SSA for analysis of digital images. A new extension of 2D-SSA analysis called shaped 2D-SSA is introduced for analysis of images of arbitrary shape, not necessary rectangular. It is shown that implementation of shaped 2D-SSA can serve as a basis for implementation of MSSA and other generalizations. Efficient implementation of operations with Hankel and Hankel-block-Hankel matrices through the fast Fourier transform is suggested. Examples with code fragments in R, which explain the methodology and demonstrate the proper use of Rssa, are presented.<br /><br />

Abstract A resurgence of consolidation in the U.S. meat packing industry in the past few decades has stimulated academic and policy debate. Issues raised include the role of cost economies in driving these patterns, and the effects on the agricultural sector (cattle producers) from market power. Here, plant level cost and revenue data for U.S. beef packing plants are used to estimate a cost‐based model incorporating cattle‐ and output‐market pricing behavior. The robust results indicate little market power exploitation in either the cattle input or beef output markets, and that any apparent evidence is counteracted by cost efficiencies such as utilization and scope economies.

Immunoassays for biotechnology engineered proteins are used by AgBiotech companies at numerous points in product development and by feed and food suppliers for compliance and contractual purposes. Although AgBiotech companies use the technology during product development and seed production, other stakeholders from the food and feed supply chains, such as commodity, food, and feed companies, as well as third-party diagnostic testing companies, also rely on immunoassays for a number of purposes. The primary use of immunoassays is to verify the presence or absence of genetically modified (GM) material in a product or to quantify the amount of GM material present in a product. This article describes the fundamental elements of GM analysis using immunoassays and especially its application to the testing of grains. The 2 most commonly used formats are lateral flow devices (LFD) and plate-based enzyme-linked immunosorbent assays (ELISA). The main applications of both formats are discussed in general, and the benefits and drawbacks are discussed in detail. The document highlights the many areas to which attention must be paid in order to produce reliable test results. These include sample preparation, method validation, choice of appropriate reference materials, and biological and instrumental sources of error. The article also discusses issues related to the analysis of different matrixes and the effects they may have on the accuracy of the immunoassays.

ABSTRACT This study measured the relationship between bread quality and 49 hard red spring (HRS) or 48 hard red winter (HRW) grain, flour, and dough quality characteristics. The estimated bread quality attributes included loaf volume, bake mix time, bake water absorption, and crumb grain score. The best‐fit models for loaf volume, bake mix time, and water absorption had R 2 values of 0.78–0.93 with five to eight variables. Crumb grain score was not well estimated, and had R 2 values ≈0.60. For loaf volume models, grain or flour protein content was the most important parameter included. Bake water absorption was best estimated when using mixograph water absorption, and flour or grain protein content. Bake water absorption models could generally be improved by including farinograph, mixograph, or alveograph measurements. Bake mix time was estimated best when using mixograph mix time, and models could be improved by including glutenin data. When the data set was divided into calibration and prediction sets, the loaf volume and bake mix time models still looked promising for screening samples. When including only variables that could be rapidly measured (protein content, test weight, single kernel moisture content, single kernel diameter, single kernel hardness, bulk moisture content, and dark hard and vitreous kernels), only loaf volume could be predicted with accuracies adequate for screening samples.

ABSTRACT Various whole‐kernel, milling, flour, dough, and breadmaking quality parameters were compared between hard red winter (HRW) and hard red spring (HRS) wheat. From the 50 quality parameters evaluated, values of only nine quality characteristics were found to be similar for both classes. These were test weight, grain moisture content, kernel size, polyphenol oxidase content, average gluten index, insoluble polymeric protein (%), free nonpolar lipids, loaf volume potential, and mixograph tolerance. Some of the quality characteristics that had significantly higher levels in HRS than in HRW wheat samples included grain protein content, grain hardness, most milling and flour quality measurements, most dough physicochemical properties, and most baking characteristics. When HRW and HRS wheat samples were grouped to be within the same wheat protein content range (11.4–15.8%), the average value of many grain and breadmaking quality characteristics were similar for both wheat classes but significant differences still existed. Values that were higher for HRW wheat flour were color b *, free polar lipids content, falling number, and farinograph tolerance. Values that were higher for HRS wheat flour were geometric mean diameter, quantity of insoluble polymeric proteins and gliadins, mixograph mix time, alveograph configuration ratio, dough weight, crumb grain score, and SDS sedimentation volume. This research showed that the grain and flour quality of HRS wheat generally exceeds that of HRW wheat whether or not samples are grouped to include a similar protein content range.

Canonical polyadic decomposition (CPD) of a higher-order tensor is an important tool in mathematical engineering. In many applications at least one of the matrix factors is constrained to be columnwise orthonormal. We first derive a relaxed condition that guarantees uniqueness of the CPD under this constraint. Second, we give a simple proof of the existence of the optimal low-rank approximation of a tensor in the case that a factor matrix is columnwise orthonormal. Third, we derive numerical algorithms for the computation of the constrained CPD. In particular, orthogonality-constrained versions of the CPD methods based on simultaneous matrix diagonalization and alternating least squares are presented. Numerical experiments are reported.

Sensitive and accurate testing for trace amounts of biotechnology-derived DNA from plant material requires pure, high-quality genomic DNA as template for subsequent amplification using the polymerase chain reaction (PCR). Six methodologies were evaluated for extracting DNA from ground corn kernels spiked with 0.1% (m/m) CBH351 (StarLink) corn. DNA preparations were evaluated for purity and fragment size. Extraction efficiency was determined. The alcohol dehydrogenase gene (adh1) and the CBH351 (cry9C, 35S promoter) genes in the genomic DNA were detected using PCR. DNA isolated by two of the methods proved unsuitable for performing PCR amplification. All other methods produced some DNA preparations that gave false negative PCR results. We observed that cornstarch, a primary component of corn kernels, was not an inhibitor of PCR, while acidic polysaccharides were. Our data suggest that amplification of an endogenous positive control gene, as an indicator for the absence of PCR inhibitors, is not always valid. This study points out aspects of DNA isolation that need to be considered when choosing a method for a particular plant/tissue type.

Abstract While basal icequakes associated with glacier motion have been detected under Antarctica for several decades, there remains very little evidence of stick‐slip motion for Alpine glaciers. Here we analyzed 2357 basal icequakes that were recorded at Glacier d'Argentière (Mont‐Blanc Massif) between February and November of 2012 and that are likely to be associated with basal sliding. These events have been classified into 18 multiplets, based on their waveforms. The strong similarity of the waveforms within each multiplet suggests an isolated repeating source. Despite this similarity, the peak amplitude within each multiplet varies gradually in time, by up to a factor of 18. The distribution of these events in time is relatively complex. For long time scales, we observe progressive variations in the amplitudes of events within each multiplet. For intermediate time scales (hours), the events occur regularly in time, with typical return times of several minutes up to several hours. For short time scales (from 0.01 to 100 s), the largest multiplet shows clustering in time, with a power law distribution of the interevent times. The location of these events and their focal mechanisms are not well constrained, because most of these events were detected by a single seismometer. Nevertheless, the locations can be estimated with an accuracy of a few tens of meters using a polarization analysis. The estimated average depth of the basal events is 179 m, which is in good agreement with the estimated glacier thickness. The relative changes in distance between the source and the sensor can be measured accurately by correlating separately the P wave and S wave parts of the seismograms of each event with the template waveforms, which are obtained by averaging the signals within each multiplet. We observed small variations in the times between the P wave and the S wave of up to 0.6 ms over 50 days. These variations cannot be explained by displacement of the sensor with respect to the glacier but might be due to small changes in the seismic wave velocities with time. Finally, we found using numerical simulations that the observed signals are better explained by a horizontal shear fault with slip parallel to the glacier flow than by a tensile fault. These results suggest that the basal events are associated with stick‐slip motion of the glacier over rough bedrock. The rupture length and the slip are difficult to estimate. Nonetheless, the rupture length is likely to be of the order of meters, and the total seismic slip accumulated over one day might be as large as the glacier motion during the most active bursts.

Implementation of multivariate and 2D extensions of Singular Spectrum Analysis (SSA) by means of the R-package Rssa is considered. The extensions include MSSA for simultaneous analysis and forecasting of several time series and 2D-SSA for analysis of digital images. A new extension of 2D-SSA analysis called Shaped 2D-SSA is introduced for analysis of images of arbitrary shape, not necessary rectangular. It is shown that implementation of Shaped 2D-SSA can serve as a base for implementation of MSSA and other generalizations. Efficient implementation of operations with Hankel and Hankel-block-Hankel matrices through FFT is suggested. Examples with code fragments in R, which explain the methodology and demonstrate the proper use of Rssa, are presented.

Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.

In this paper we present an overview of LIPS2008: Visual Speech Synthesis Challenge.The aim of this challenge is to bring together researchers in the field of visual speech synthesis to firstly evaluate their systems within a common framework, and secondly to identify the needs of the wider community in terms of evaluation.In doing so we hope to better understand the differences between the various approaches and to identify the strengths/weaknesses of the competing approaches.In this paper we firstly motivate the need for the challenge, before describing the capture and preparation of the training data, the evaluation framework, and conclude with an outline of possible directions for standardising the evaluation of talking heads.

Abstract Seven laboratories participated in a collaborative study to extend the applicability of the AOAC generic combustion method for determination of crude protein in animal feed (990.03) to include determination in cereal grains and oilseeds. In the study, method 990.03 was compared with the AOAC mercury catalyst Kjeldahl method for determination of protein in grains (979.09) and crude protein in animal feed (954.01). The study also evaluated the effect on the results of fineness of grind. For determination of crude protein in grains and oilseeds by the combustion method, standard deviations for repeatability and reproducibility ranged from 0.10 to 0.37 and from 0.25 to 0.54, respectively, and relative standard deviations for repeatability and reproducibility ranged from 0.77 to 2.57% and from 1.24 to 3.15%, respectively. The combustion method was adopted first action by AOAC International for determination of crude protein in cereal grains and oilseeds containing 0.2- 20% nitrogen.

Collusive Practices in Repeated English Auctions: Experimental Evidence on Bidding Rings by Owen R. Phillips, Dale J. Menkhaus and Kalyn T. Coatney. Published in volume 93, issue 3, pages 965-979 of American Economic Review, June 2003

International audience

International audience