Colour and colour stability of hot processed frozen minced beef. Results from chemical model experiments tested under storage conditions

Minced beef based on hot processed meat was found to show an improved red colour and, more significantly, to show a better colour stability with respect to brown discoloration, when compared to minced beef resulting from a traditional (cold) process. The colour was followed by tristimulus colorimetry during freezer storage (product temperature -18°C) in a display cabinet illuminated by fluorescent tubes. The development of brown discoloration seems to indicate other oxidative changes and to relate to rancidity as judged from a determination of thiobarbituric acid reactive substances in the mince surface. Fluorescent light and added salt, both known from chemical models to increase the autoxidation rate of red oxymyoglobin to brown metmyoglobin, greatly reduced the colour stability, especially for cold deboned meat. pH of the hot deboned meat was significantly higher (pH > 6·0) than that of the cold deboned meat (pH ranged from 5·5 to 5·8). The observations are discussed in relation to kinetic salt effect on the acid catalyzed autoxidation of oxymyoglobin and its role in the initiation of oxidative rancidity.     

Morse set classification and hierarchical refinement using Conley index

Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincare′ index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and realworld simulation data to demonstrate their utility.     

Probability-confidence-kernel-based localized multiple kernel learning with lp norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.     

A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.     

Symbolic dynamic filtering and language measure for behavior identification of mobile robots

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.     

Cubical Mass-Spring Model design based on a tensile deformation test and nonlinear material model

Mass-Spring Models (MSMs) are used to simulate the mechanical behavior of deformable bodies such as soft tissues in medical applications. Although they are fast to compute, they lack accuracy and their design remains still a great challenge. The major difficulties in building realistic MSMs lie on the spring stiffness estimation and the topology identification. In this work, the mechanical behavior of MSMs under tensile loads is analyzed before studying the spring stiffness estimation. In particular, the performed qualitative and quantitative analysis of the behavior of cubical MSMs shows that they have a nonlinear response similar to hyperelastic material models. According to this behavior, a new method for spring stiffness estimation valid for linear and nonlinear material models is proposed. This method adjusts the stress-strain and compressibility curves to a given reference behavior. The accuracy of the MSMs designed with this method is tested taking as reference some soft-tissue simulations based on nonlinear Finite Element Method (FEM). The obtained results show that MSMs can be designed to realistically model the behavior of hyperelastic materials such as soft tissues and can become an interesting alternative to other approaches such as nonlinear FEM.     

Analysis of time-dependent flow-sensitive PC-MRI data

Many flow visualization techniques, especially integration-based methods, are problematic when the measured data exhibit noise and discretization issues. Particularly, this is the case for flow-sensitive phase-contrast magnetic resonance imaging (PC-MRI) data sets which not only record anatomic information, but also time-varying flow information. We propose a novel approach for the visualization of such data sets using integration-based methods. Our ideas are based upon finite-time Lyapunov exponents (FTLE) and enable identification of vessel boundaries in the data as high regions of separation. This allows us to correctly restrict integration-based visualization to blood vessels. We validate our technique by comparing our approach to existing anatomy-based methods as well as addressing the benefits and limitations of using FTLE to restrict flow. We also discuss the importance of parameters, i.e., advection length and data resolution, in establishing a well-defined vessel boundary. We extract appropriate flow lines and surfaces that enable the visualization of blood flow within the vessels. We further enhance the visualization by analyzing flow behavior in the seeded region and generating simplified depictions.     

Stress-sensitivity mapping for surface acoustic waves on quartz

A model is presented, relating the velocity shifts of surface acoustic waves (SAW) to the six tensor components of quasistatic stresses. Stress sensitivity is then defined through six independent coefficients, whatever the origin of the stress (direct external forces, thermoelastic stresses) might be. These coefficients, depending on crystal anisotropy, are computed for different cut angles and propagation directions of quartz crystal, and represented as a contour-line mapping. The determination of SAW quartz cuts compensated for both planar isotropic stresses and first-order temperature effects make it possible to define a family of quartz cuts with potentially low stress and temperature sensitivities for oscillator applications.     

A theoretical approach for inverse filter design in ultrasonic applications

A comparison between simulation- and measurement-based characterization is provided for the design of one-dimensional, nonadaptive, inverse filters for ultrasonic applications. Both prefiltering and postfiltering are considered by means of two different filtering strategies performed in the time and frequency domains. The results clearly indicate that theoretical methods can be used successfully, provided that the physical limitations of the simulation process are fully appreciated. In some instances, the simulation-based approach is shown to provide superior results, and the reasons for this are explained.     

Electrical stimulation during exsanguination: Effects on the prevalence of blood splash and on sensory quality characteristics in veal

Twenty-four veal calves were stunned with a captive bolt. Twelve calves were shackled by the left, the other twelve by the right hindleg. Approximately 1 min after sticking, six carcasses of each of these subgroups were stimulated electrically with equipment relying on a combined high (3000 V, 0·83 pulses s(-1), 1·5 ms duration) and low 35 V, 14 pulsess(-1)) voltage action. The remaining carcasses were left unharmed. At 24 h post-mortem hindquarters were boned to commercial primal cuts. Surrounding connective tissue and cut-up surfaces of primals as well as certain locations vulnerable for blood splash were further dissected. Neither electrical stimulation nor shackling by either of the hindlegs significantly affected the prevalence and severity of blood splash. Post-mortem factors promoting blood splash and possibly related to electrical stimulation and shackling are discussed. At 7 days post-mortem the sensory meat quality was assessed. In general, electrical stimulation did not significantly affect waterbinding and colour characteristics in longissimus (M. longissimus dorsi) and semimembranosus samples. Sarcomere lengths were similar in stimulated and control groups in longissimus but not in semimembranosus muscle. In both muscles shear force values were lower after electrical stimulation. In general, neither longissimus nor semimembranosus samples excised ipsilaterally from the shackled leg side exhibited differences in tenderness and sarcomere length. It is concluded that shackling by one leg or the other does not interfere with the tenderising effect of electrical stimulation.