Anthocyanin profile of red fruits and black carrot juices,purees and concentrates by HPLC‐DAD‐ESI/MS‐QTOF

A fast and reliable method for anthocyanin extraction and identification by HPLC‐DAD‐ESI/MS‐QTOF was used to analyse the anthocyanin composition of commercial red fruit juices (blackberry, redcurrant and pomegranate), purees (strawberry, cherry and raspberry) and concentrates (elderberry, blueberry and red grape). The anthocyanin profile of black carrot juice is also reported. The extraction and analysis method allowed us to detect and quantify a wide range of individual anthocyanins in a simple and rapid way. Pelargonidin‐3‐glucoside was detected in redcurrant for the first time and petunidin‐3‐galactoside quantified for the first time in blueberries. Considering the health benefits that have been associated with anthocyanin consumption, all these fruit and vegetables processed products could appear as a good source of this group of phytochemical compounds for their direct consumption or their use as ingredients for the design of new food product or food supplements.     

Electrospinning and Imaging

Polymer processing via electrospinning is a cost effective and scalable method for preparing nanofibers with industrial, electrical, and biomedical applications, particularly tissue engineering and drug delivery. Characterization methods for these fibers include microscopy techniques for vitro surface morphology information, spectroscopy methods to determine in vitro chemical composition, and medical imaging tools for in vivo assessment of morphology and efficacy of implanted material. The focus of this paper is be on recent applications for electrospun nanofibers, in vitro characterization methods, and medical imaging modalities that can be used for in vivo assessment of the fibers, as well as insights in how to adapt existing techniques toward the characterization of electrospun materials.     

A hybrid root transformation and decision on belief approach to monitor multiattribute Poisson processes

Most of industrial applications of statistical process control involve more than one quality characteristics to be monitored. These characteristics are usually correlated, causing challenges for the monitoring methods. These challenges are resolved using multivariate quality control charts that have been widely developed in recent years. Nonetheless, multivariate process monitoring methods encounter a problem when the quality characteristics are of the attribute type and follow nonnormal distributions such as multivariate binomial or multivariate Poisson. Since the data analysis in the latter case is not as easy as the normal case, more complexities are involved to monitor multiattribute processes. In this paper, a hybrid procedure is developed to monitor multiattribute correlated processes, in which number of defects in each characteristic is important. Two phases are proposed to design the monitoring scheme. In the first phase, the inherent skewness of multiattribute Poisson data is almost removed using a root transformation technique. In the second phase, a method based on the decision on belief concept is employed. The transformed data obtained from the first phase are implemented on the decision on belief (DOB) method. Finally, some simulation experiments are performed to compare the performances of the proposed methodology with the ones obtained using the Hotelling T ² and the MEWMA charts in terms of in-control and out-of-control average run length criteria. The simulation results show that the proposed methodology outperforms the other two methods.     

Polar classification with correspondence analysis for fault isolation

Data collected from operating plants can be mined to extract information related to both normal and fault modes of operation. Correspondence analysis (CA), that decomposes a measure of row–column association, to generate the lower dimensional space has been recently proposed [1] for this task. CA represents the association between samples and variables in terms of angle based measures on a biplot. Thus, toward clearer resolution of the faults, polar clustering and classification procedures are necessary. In this paper, we develop a methodology to mine the operating data and build such clusters. We demonstrate the application of this methodology on data generated from simulations and experiments involving representative systems,for detecting parametric changes and resolving sensor and actuator biases.     

Synergistic effect of organoclay fillers based on fluorinated surfmers for preparation of polystyrene nanocomposites

Novel fluorinated reactive surfactants were used for the organic modification of monmorillonite clays. These organoclays were used for the preparation of polystyrene?clay nanocomposites by in‐situ free radical polymerization. Reference systems based on hydrocarbon homologous surfmer and nonpolymerizable surfactants were also used to deduce the effect of the fluorine moiety and the polymerizable function on the morphology and thermal stability of the prepared nanocomposites. Different structural parameters of the surfactants were investigated and modulated for the clay modification including: the nature of surfactant (surfmer/classical surfactant, fluorinated, or hydrocarbonated), the length of the fluorinated chain as well as the length of the hydrocarbon spacers linking the ammonium head to the fluorine chain or the polymerizable acrylic function. Wide angle‐X‐ray scattering (WAXD), thermogravimetric analysis (TGA), and electronic microcopies (TEM and SEM) were used to establish a structure‐morphology, thermal properties relationships, and to highlight the key parameters governing the exfoliation process. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42347.     

Final contractor selection using the analytical hierarchy process

Some contractor selection methods currently in existence are criticized as incomplete and biased, and lacking consideration in terms of the contractor's ability to achieve simultaneously, time, cost, quality and safety standards. This research examines an alternative contractor selection model called the analytical hierarchy process (AHP), which will help construction clients to identify contractors with the best potential to deliver satisfactory outcomes in a final contractor selection process which is not based simply on the lowest bid. The AHP comprises three parts: hierarchic structure, prioritization procedure, and calculation of results. This model is tested by a hypothetical scenario where three contractor candidates are evaluated. The criteria used for contractor selection in the model have been identified, and the significance of each criterion has been arrived at by conducting a questionnaire survey in public organizations in Hong Kong. Comparisons are made by ranking the aggregate scores of each candidate with regard to their performance against each of the criteria, and the candidate associated with the highest scores is the best contractor on this occasion.