Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside

The potential of Raman spectroscopy for the determination of meat quality attributes has been investigated using data from a set of 52 cooked beef samples, which were rated by trained taste panels. The Raman spectra, shear force and cooking loss were measured and PLS used to correlate the attributes with the Raman data. Good correlations and standard errors of prediction were found when the Raman data were used to predict the panels' rating of acceptability of texture (R²=0.71, Residual Mean Standard Error of Prediction (RMSEP)% of the mean (μ)=15%), degree of tenderness (R²=0.65, RMSEP% of μ=18%), degree of juiciness (R²=0.62, RMSEP% of μ=16%), and overall acceptability (R²=0.67, RMSEP% of μ=11%). In contrast, the mechanically determined shear force was poorly correlated with tenderness (R²=0.15). Tentative interpretation of the plots of the regression coefficients suggests that the -helix to β-sheet ratio of the proteins and the hydrophobicity of the myofibrillar environment are important factors contributing to the shear force, tenderness, texture and overall acceptability of the beef. In summary, this work demonstrates that Raman spectroscopy can be used to predict consumer-perceived beef quality. In part, this overall success is due to the fact that the Raman method predicts texture and tenderness, which are the predominant factors in determining overall acceptability in the Western world. Nonetheless, it is clear that Raman spectroscopy has considerable potential as a method for non-destructive and rapid determination of beef quality parameters.     

Network switching and voltage evaluation during power system restoration

In this work, voltage evaluation after power components energization such as transmission line, transformer and shunt reactor is analyzed using artificial neural network (ANN)-based approach. Throughout the initial phase of system restoration, unexpected overvoltage may happen due to nonlinear interaction between the unloaded transformer and the transmission system. Such an overvoltage might damage some equipment and delay power system restoration. In the cases of transformer and shunt reactor energization, ANN is trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Moreover, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of overvoltages during network switching with good accuracy.     

Effect of Phosphate on the Particle Size of Ferric Oxyhydroxides Anchored onto Activated Carbon: As(V) Removal from Water

The surface area of iron oxyhydroxides is a key factor when removing As from water. However, research related to this matter shows that this issue has not been explored in detail. The use of capping agents is a viable method to synthesize ferric oxyhydroxide nanoparticles; however, this method to our knowledge has not been applied for the anchorage of iron oxyhydroxide nanoparticles on activated carbon (AC). In the present work, the addition of PO(4) (as a capping agent) in forced hydrolysis of FeCl(3) in AC was investigated. Results revealed that the surface area of modified materials reached a maximum of about 900 m(2)/g with a molar ratio PO(4)/Fe of 0.1. Moreover, microscopy studies indicate a size range of iron nanoparticles from 2 to 300 nm, where the smallest particles are attained with the highest concentration of PO(4). The surface charge distribution of modified samples became less positive; however, the As removal increased, indicating that electrostatic interaction is not the controlling sorption mechanism. Modified samples showed a 40% increase on As(V) adsorption capacity when using a molar ratio PO(4)/Fe of 1.5. The proposed method allowed anchoring of iron oxyhydroxides nanoparticles on AC, which have a high As(V) adsorption capacity (5 mg/g).     

Effect of chaotic mixing on enhanced biological growth and implications for wastewater treatment: a test case with Saccharomyces cerevisiae

Mixing patterns and modes have a great influence on the efficiency of biological treatment systems. A series of laboratory experiments was conducted with a controlled, small-scale analog of a pilot wastewater aeration tank, consisting of two eccentrically placed cylinders. By controlling the rotation direction and speed of the two cylinders, it has been possible to develop chaotic flow fields in the space between the walls of the cylinders. Our experiments utilized Saccharomyces cerevisiae as the biological oxidation organism and air bubbles as the mixing agent supplied by a large fine pore diffuser to the cells in their exponential growth phase. The effect of various mixing patterns on cell growth was studied at different cylinder eccentricities, rotation directions and speeds. It was found that chaotic advection flow patterns: (a) enhanced growth, and (b) sped up the onset of maximal growth of the organism by 15-18% and 14-20%, respectively.     

Anchorage of iron hydro(oxide) nanoparticles onto activated carbon to remove As(V) from water

The adsorption of arsenic (V) by granular iron hydro(oxides) has been proven to be a reliable technique. However, due to the low mechanical properties of this material, it is difficult to apply it in full scale water treatment. Hence, the aim of this research is to develop a methodology to anchor iron hydro(oxide) nanoparticles onto activated carbon, in which the iron hydro(oxide) nanoparticles will give the activated carbon an elevated active surface area for arsenic adsorption and also help avoid the blockage of the activated carbon pores. Three activated carbons were modified by employing the thermal hydrolysis of iron as the anchorage procedure. The effects of hydrolysis temperature (60-120 °C), hydrolysis time (4-16 h), and FeCl₃ concentration (0.4-3 mol Fe/L) were studied by the surface response methodology. The iron content of the modified samples ranged from 0.73 to 5.27%, with the higher end of the range pertaining to the carbons with high oxygen content. The materials containing smaller iron hydro(oxide) particles exhibited an enhanced arsenic adsorption capacity. The best adsorbent material reported an arsenic adsorption capacity of 4.56 mg As/g at 1.5 ppm As at equilibrium and pH 7.     

Microbial technologies for heavy metal remediation: effect of process conditions and current practices

Clean Technologies and Environmental Policy - Heavy metal (HM) contamination is a persisting environmental problem in many countries. The major sources of soil contamination due to heavy metals...     

Hydrogenated amorphous silicon p–i–n solar cells deposited under well controlled ion bombardment using pulse‐shaped substrate biasing

We applied pulse‐shaped biasing (PSB) to the expanding thermal plasma deposition of intrinsic hydrogenated amorphous silicon layers at substrate temperatures of 200 °C and growth rates of about 1 nm/s. Fourier transform infrared spectroscopy of intrinsic films showed a densification with increasing deposited energy and a reduction in void content, whereas dual‐beam photoconductivity measurements showed an increase in Urbach energy above 4.8 eV/Si atom. From dark conductivity and photoconductivity measurements, we determined a maximum photoresponse of 2 × 10⁶ at 3 eV/Si atom, which decreased at higher deposited energies because of a higher dark conductivity as a result of a lower band gap. p–i–n solar cells with PSB applied during the intrinsic layer deposition showed initial energy conversion efficiencies of 7.4% at around 1 eV/Si atom. Decreasing open‐circuit voltage at >1 eV/Si atom can be related to a lower band gap, whereas the short‐circuit current drops at >4.8 eV/Si atom, predominantly because of hole collection losses as determined from quantum efficiency measurements. The reduced fill factor for >1 eV/Si atom was presumably related to a decrease in mobility‐lifetime product because of an increase in defect density. Copyright © 2011 John Wiley & Sons, Ltd.     

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).     

Facile de Novo Sequencing of Tetrazine-Cyclized Peptides through UV-Induced Ring-Opening and Cleavage from the Solid Phase

While most FDA-approved peptide drugs are cyclic, the robust cyclization chemistry of peptides and the deconvolution of cyclic peptide sequences by using tandem mass spectrometry render cyclic peptide drug discovery difficult. Here we present the successful design of cyclic peptides on solid phase that addresses both of these problems. We demonstrate that this peptide cyclization method using dichloro-s-tetrazine on solid phase allows successful cyclization of a panel of random peptide sequences with various charges and hydrophobicities. The cyclic peptides can be linearized and cleaved from the solid phase by simple UV light irradiation, and we demonstrate that accurate sequence information can be obtained for the UV-cleaved linearized peptides by using tandem mass spectrometry. The tetrazine linker used in the cyclic peptides can further be explored for inverse electron-demand Diels-Alder (IEDDA) reactions for screening or bioconjugation applications in the future.     

An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

Abstract: Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property. The precise classification of cyclone tracks is significantly important in the field of weather forecasting. In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape. Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the TC characteristics. A set of SWRL (Semantic Web Rule Language) rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology. Furthermore, we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset. According to experiments on the IBTrACS dataset, the proposed ontology based SVM classifier achieves an accuracy of 98.3% with reduced classification error rates.