Development of a dairy-based exopolysaccharide bioingredient

An exopolysaccharide (EPS)-producing Streptococcus thermophilus was used to develop an “all-dairy” ingredient with increased EPS content and greater functionality for dairy applications such as Mozzarella cheese. In laboratory-scale trials, milk protein hydrolysate (MPH), whey protein isolate (WPI) and whey protein hydrolysate (WPH) in reconstituted low-heat skim milk powder (LHSMP) were examined as additional dairy protein sources to increase the production of EPS. WPH supplementation resulted in the greatest EPS production. In batch fermentation, a medium based on LHSMP and WPH, combined with optimum conditions of temperature and pH, was used to obtain maximum production of EPS from S. thermophilus. EPS production was growth associated. The fermented biomass was harvested at the end of the exponential phase and freeze-dried. The reduced viable cell count and the retention of ropiness of the powder from the drying process enabled a higher level of EPS inoculation in a preliminary Mozzarella cheese manufacturing trial.     

Monoclonal antibody for the demonstration by ELISA of antibodies to protein p24 of enzootic bovine leukosis virus in individual and pooled blood serum and milk samples

A sensitive and specific ELISA for the demonstration of antibodies to the protein p24 of enzootic bovine leukosis virus (EBVL) using a 'capture' monoclonal antibody to this protein (MAb p24) was developed. The method is sensitive enough to detect the international reference serum E4/10 in pooled blood serum samples collected from up to 50 cows, or, if a 10-fold concentrate of milk whey is tested, in samples of bulk milk collected from up to 400 cows. The application of MAb p24 has considerably increased not only the sensitivity, but also the specificity of ELISA. Moreover it is possible to differentiate reliably between positive and 'false positive' reagents by testing a suspicious sample in a pair of wells of which one is coated with MAb p24 alone and the other with the complex MAb p24 + EBLV antigen and the subsequent calculation of 'specific absorbance'. This method, showing the highest sensitivity of detection of antibodies to EBLV p24 described so far, can become an effective tool on the sanitation of infected herds as well as in checks of the EBL-free status. A diagnostic kit suitable for commercial manufacture has been devised.     

Confirmation of brand identity in foods by near infrared transflectance spectroscopy using classification and class-modelling chemometric techniques — The example of a Belgian beer

Food and beverage processors require tools to monitor conformance of finished goods to their defined specification; regulatory authorities need appropriate methods for detecting retail fraud. In this report, samples (n = 275) of Belgian and other European beers were collected and analysed using near infrared transflectance spectroscopy; three class-modelling techniques (soft independent modelling of class analogy, SIMCA; potential functions techniques, POTFUN; and unequal dispersed classes, UNEQ) were employed to characterise beer types (firstly Trappist and then Rochefort) while a classification method (partial least squares discriminant analysis, PLS-DA) was applied to discriminate between two final beer classes: Rochefort 8° and Rochefort 10°. The class-models and the classification rules developed were validated by means of an external prediction set. A discussion on the appropriate use of these chemometric approaches is included. Modelling of Trappist beers met with limited success while model efficiencies for Rochefort samples were highest for SIMCA and UNEQ applications i.e. 81.4% and 84.5% respectively. The classification of beers as Rochefort 8? or Rochefort 10? was possible with an average correct classification rate of 93.4%.     

Supercritical water oxidation of chemical agents,and solid propellants

Abstract

Supercritical water oxidation (SCWO), also referred to as hydrothermal oxidation (HTO), is a developing technology for the destruction of hazardous and nonhazardous wastes. SCWO destroys combustible materials using an oxidant in water at temperatures in the range of 350°C (662°F) to 600°C (1112°F) and pressures of 17 MPa (2500 psi) or greater. General Atomics and its subcontractors are currently conducting two comprehensive research and demonstration programs geared toward the destruction of Department of Defense (DoD) wastes utilizing SCWO technology. Wastes of primary interest include chemical agents and solid propellants. Technical challenges, particularly corrosion and solids handling, were overcome, and the destruction of chemical agents and solid propellants was demonstrated on a bench scale. SCWO results for chemical agents show destruction and removal efficiencies for GB, VX, and mustard agents to be in excess of 99.9999%, limited only by detection capability. SCWO results for hydrolyzed Class 1.1 solid propellant show destruction and removal efficiencies for total organic carbon (TOC) of >99.9%. Design and fabrication of a transportable SCWO pilot plant for chemical warfare agents, propellants and other DoD hazardous wastes and a prototype HTO system for solid rocket propellant disposal are complete and demonstration testing is underway.     

Applying artificial neural network models to clinical decision making.

Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effect of different starches on rheological and microstructural properties of (II) commercial processed cheese

A range of commercial processed cheese samples containing starch were prepared on a Rapid Visco Analyser (RVA) and on a pilot plant scale. This work clearly demonstrated that it was possible to manufacture processed cheese with part of the protein replaced with potato starch, while maintaining similar rheological attributes (firmness) to those of the control and an acceptable melt index. Sensory evaluation showed that, although the reduced‐protein cheese samples had a good, clean, fresh flavour that was comparable with that of the control, at high starch concentrations the starch‐containing processed cheese had a pasty texture and tended to stick to the wrapper.     

Characteristics of 0.8- and 0.2-μm gate length InxGa1-xAs/In0.52Al0.48As/InP(0.53⩽x⩽0.70) modulation-doped field-effect transistorsat cryogenic temperatures

The performance characteristics of InP-based pseudomorphic MODFETs with varying the In composition (0.53⩽x⩽0.70), which changes the strain in the channel, were studied. The temperature was varied in the range of 40-300 K, and the devices had gate lengths L _g of 0.8 and 0.2 μm. The analysis predicts an increase in the intrinsic cutoff frequency with increasing In composition and decreasing temperature and gate length. Also, the analysis predicts that the increase in cutoff frequency with decreasing temperature is less significant with increasing In composition and decreasing gate length. Preliminary experimental results show that as In composition increases from 0.53 to 0.70, f_T increases by 30-40%, and as the temperature decreases from 300 to 40 K, f_T improves by 15-30%, both for 0.8- and 0.2-μm devices     

Distribution circuit state estimation using a probabilisticapproach

Past work on distribution circuit state estimation has focused on the adoption of a transmission state estimator approach, without necessarily accounting for the specific requirement of a distribution circuit-based analysis. On distribution circuits, typically, there are very few available real-time measurements, and thus, researchers have treated customer load demand estimates as pseudo-measurements in a weighted-least-squares formulation. This can lead to convergence problems and also, the approach effectively assumes that all bus load demands are normally distributed (Gaussian) which may not be valid on distribution circuits. This paper presents an alternative approach to distribution circuit state estimation using a probabilistic extension of the radial load flow algorithm while accounting for real-time measurements as solution constraint. The algorithm which takes advantage of the radial nature of distribution circuits also accounts for other issues specific to distribution circuits. Namely, the algorithm accounts for nonnormally distributed loads, incorporates the concept of load diversity (load correlation) and can interact with a load allocation routine. The effectiveness of the algorithm is illustrated through comparisons made with Monte Carlo simulations     

A model-free, fully automated baseline-removal method for Raman spectra

We present here a fully automated spectral baseline-removal procedure. The method uses a large-window moving average to estimate the baseline; thus, it is a model-free approach with a peak-stripping method to remove spectral peaks. After processing, the baseline-corrected spectrum should yield a flat baseline and this endpoint can be verified with the χ(2)-statistic. The approach provides for multiple passes or iterations, based on a given χ(2)-statistic for convergence. If the baseline is acceptably flat given the χ(2)-statistic after the first pass at correction, the problem is solved. If not, the non-flat baseline (i.e., after the first effort or first pass at correction) should provide an indication of where the first pass caused too much or too little baseline to be subtracted. The second pass thus permits one to compensate for the errors incurred on the first pass. Thus, one can use a very large window so as to avoid affecting spectral peaks--even if the window is so large that the baseline is inaccurately removed--because baseline-correction errors can be assessed and compensated for on subsequent passes. We start with the largest possible window and gradually reduce it until acceptable baseline correction based on the χ(2) statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.