Functional and sensory properties of chicken meat from spent-hen carcasses deboned manually or mechanically in Jordan

Samples of chicken meat from spent laying hens were obtained by four different methods: (1) manual deboning of whole carcasses; (2) manual deboning of skinned carcasses; (3) mechanical deboning of whole carcasses; and (4) mechanical deboning of skinned carcasses. The meat was packaged, frozen and stored at ?18 °C for up to 3 months. Functional properties studied were pH, emulsifying capacity (EC) and water‐holding capacity (WHC) and, in addition, pigment concentration was investigated. Sensory properties included aroma, colour, texture and overall acceptability, as judged by a trained panel using a nine‐point hedonic scale. Removal of the skin prior to deboning by either method gave significantly higher values (P < 0.05) for EC, while WHC values were not significantly different among the experimental treatments, despite higher pH values for mechanically‐deboned meat, with and without skin. Pigment concentrations were highest in meat from Treatment 4 and lowest for Treatment 1, but values for both methods of deboning were affected by the presence of skin. There were no significant differences between treatments for any of the sensory properties studied. Changes occurring in the meat during frozen storage are described.     

Predicting surface quality of γ-TiAl produced by additive manufacturing process using response surface method

Electron beam melting (EBM) has been found to be a promising technology for producing complex shaped parts from gamma titanium aluminide alloys (γ-TiAl). The parts produced by this process are projected to have dimensions very close to the desired final shapes. However, the surface roughness of the parts produced by EBM is excessively rough. In many applications, it is necessary to improve the quality of manufactured parts using a convenient post process. This paper determines process parameters of end milling when it is used as a post process for the parts produced by EBM. Design of experiments has been used to study the effect of the selected input parameters of end milling (spindle speed, feed rate, depth of cut and coolant type) on the surface roughness of γ-TiAl parts. Response surface methodology is used to develop a predictive model for surface roughness. Effects of the selected milling process are investigated. This paper also optimizes the selected process parameters to minimize the value of the obtained surface roughness.     

Assessment of quality of fruits using impedance spectroscopy

This study deals with the development of a nondestructive impedance spectroscopic technique that may assess the conditions of the fruits to pluck them with the help of robotic arms. Preliminary investigations are made with the help of two‐terminal probe and an accurate LCR meter. The bulk impedance of mango has been measured to characterise raw and ripe fruits. Effective resistance and effective capacitance vs. frequency characteristics have been determined. The bulk effective resistances, of the ripe fruits, are found to be more than those of the raw fruits, in the frequency range of 1–6 kHz. In the same frequency range, effective capacitances of the raw fruits are found more than those of the ripe fruits. In the light of the data obtained, it can be said that the effective resistance may be used to differentiate between raw and ripe fruits in the frequency range of 1–6 kHz.     

Beef and sheep mortadella: formulation, processing and quality aspects

Beef and lamb mortadella samples were prepared under controlled conditions and stored for 16 weeks under refrigeration. Moisture, protein, fat, ash and sodium chloride contents were measured initially and pH, percentage of free fatty acids, thiobarbituric acid values, total volatile nitrogen content, residual nitrite concentration and microbiological status were determined at regular intervals throughout storage. The sensory quality of the mortadellas during the first 2 months of storage was evaluated. The results of these tests permitted assessment of the quality and safety of beef and lamb mortadella. The products were satisfactory with regard to chemical and microbiological analyses, but after 2 months storage, the poor colour of lamb mortadella limited the sensory acceptability. Measurements of rancidity, caused by lipid deterioration and protein breakdown, were the best indicators of product quality.     

Laser beam machining of zirconia ceramic: An investigation of micro-machining geometry and surface roughness

Journal of Mechanical Science and Technology - Micro-machining of dental ceramics namely as zirconium oxide is carried out through laser beam machining. Micro-channels of different sizes are...     

Neural network modeling and analysis of the material removal process during laser machining

To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.     

Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different probabilistic and bivariate statistical approaches; namely, frequency ratio (FR), weights-of-evidence (WofE), index-of-entropy (IofE), and Dempster–Shafer (DS) models, at Wadi Itwad, Asir region, in the southwestern part of Saudi Arabia. Landslide locations were identified and mapped from interpretation of high-resolution satellite images, historical records, and extensive field surveys. In total, 326 landslide locations were mapped using ArcGIS and divided into two groups; 75 % and 25 % of landslide locations were used for training and validation of models, respectively. Twelve layers of landslide-related factors were prepared, including altitude, slope degree, slope length, topography wetness index, curvature, slope aspect, distance from lineaments, distance from roads, distance from streams, lithology, rainfall, and normalized difference vegetation index. The relationships between the landslide-related factors and the landslide inventory map were calculated using different statistical models (FR, WofE, IofE, and DS). The model results were verified with landslide locations, which were not used during the model training. In addition, receiver operating characteristic curves were applied, and area under the curve (AUC) was calculated for the different susceptibility maps using the success (training data) and prediction (validation data) rate curves. The results showed that the AUC for success rates are 0.813, 0.815, 0.800, and 0.777, while the prediction rates are 0.95, 0.952, 0.946, and 0.934 for FR, WofE, IofE, and DS models, respectively. Subsequently, landslide susceptibility maps were divided into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the percentage of training and validating landslides locations in high and very high landslide susceptibility classes in each map were calculated. The results revealed that the FR, WofE, IofE, and DS models produced reasonable accuracy. The outcomes will be useful for future general planned development activities and environmental protection.     

Denitrogenation of carbazole by a novel strain Bacillus Clausii BS1 isolated from Egyptian Coke

In this study, an effective enrichment technique was applied to isolate different bacterial strains with capabilities to denitrogenate carbazole (CAR), as a model compound for nitrogenous polyaromatic hydrocarbons (NPAHs). Twenty-four different CAR-denitrogenating bacterial strains were isolated from mineral coke samples. From all, a Gram +ve bacterial isolate designated BS1 showed a higher biodenitrogenation (BDN) efficiency relevant to the well-known biodenitrogenating bacterium strain Pseudomonas resinovorans CA10, recording 77.15 and 60.66% removal of 1000 ppm CAR with the production of 119.79 and 102.43 ppm anthranilic acid, and 121.19 and 90.33 ppm catechol, as by-products, respectively. BS1 was identified by 16S rDNA gene sequence analysis to be Bacillus clausii BS1 (NCBI GenBank Accession no. KF985981) with a similarity of 99.14%. The CAR-BDN pathway was also elucidated using high-performance liquid chromatography (HPLC) and gas chromatography mass spectrometry analysis (GC/MS), which proved further metabolism of catechol to muconic acid.