Water Research – Contents Pages

Table of Contents |  Volume 74, Pages 1-266 (1 May 2015)

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Based on time and spatial-resolved sers mapping strategies for detection of pesticides

Talanta Available online 1 April 2015;  doi:10.1016/j.talanta.2015.03.053

For the sensitive and convenient detection of pesticides, several sensing methods and materials have been widely explored. However, it is still a challenge to obtain sensitive, simple detection techniques for pesticides. Here, the simple and sensitive Time-resolved SERS mapping (T-SERS) and Spatial-resolved SERS mapping (S-SERS) are presented for detection of pesticides by using Au@Ag NPs as SERS substrate. The Time-resolved SERS mapping (T-SERS) is based on state translation nanoparticles from the wet state to the dry state to realize SERS measurements. During the SERS measurement, adhesive force drives the particles closer together and then average interparticle gap becomes smaller. Following, air then begins to intersperse into the liquid network and the particles are held together by adhesive forces at the solid-liquid-air interface. In the late stage of water evaporation, all particles are uniformly distributed. Thus, so called hotspots matrix that can hold hotspots between every two adjacent particles in efficient space with minimal polydispersity of particle size are achieved, accompanying the red-shift of surface plasmon peak and appearance of an optimal SPR resonated sharply with excitation wavelength. Here, we found that the T-SERS method exhibits the detection limits of 1-2 orders of magnitude higher than that of S-SERS. On the other hand, the T-SERS is very simple method with high detection sensitivity, better reproducibility (RSD=10.8%) and is beneficial to construction of a calibration curve in comparison with that of Spatial-resolved SERS mapping (S-SERS). Most importantly, as a result of its remarkable sensitivity, T-SERS mapping strategies have been applied to detection of several pesticides and the detect limit can down to 1 nM for paraoxon, 0.5 nM for sumithion. In short, T-SERS mapping measurement promises to open a market for SERS practical detection with prominent advantages.

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Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Talanta Available online 27 March 2015;  doi:10.1016/j.talanta.2015.03.037

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA) are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination.

Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1-and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed.

The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

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Rapid Detection of Listeria monocytogenes in Milk Using Confocal Micro-Raman Spectroscopy and Chemometric Analysis

International Journal of Food Microbiology Available online 1 April 2015;  doi:10.1016/j.ijfoodmicro.2015.03.021

Listeria monocytogenes is a facultatively anaerobic, Gram-positive, rod-shape foodborne bacterium causing invasive infection, listeriosis, in susceptible populations. Rapid and high-throughput detection of this pathogen in dairy products is critical as milk and other dairy products have been implicated as food vehicles in several outbreaks. Here we evaluated confocal micro-Raman spectroscopy (785 nm laser) coupled with chemometric analysis to distinguish six closely related Listeria species, including L. monocytogenes, in both liquid media and milk. Raman spectra of different Listeria species and other bacteria (i.e., Staphylococcus aureus, Salmonella enterica and Escherichia coli) were collected to create two independent databases for detection in media and milk, respectively. Unsupervised chemometric models including principal component analysis and hierarchical cluster analysis were applied to differentiate L. monocytogenes from Listeria and other bacteria. To further evaluate the performance and reliability of unsupervised chemometric analyses, supervised chemometrics were performed, including two discriminant analyses (DA) and soft independent modeling of class analogies (SIMCA). By analyzing Raman spectra via two DA-based chemometric models, average identification accuracies of 97.78% and 98.33% for L. monocytogenes in media, and 95.28% and 96.11% in milk were obtained, respectively. SIMCA analysis also resulted in satisfied average classification accuracies (over 93% in both media and milk). This Raman spectroscopic-based detection of L. monocytogenes in media and milk can be finished within a few hours and requires no extensive sample preparation.

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Informatics and Autopsy Pathology

Surgical Pathology Clinics Available online 26 March 2015;  doi:10.1016/j.path.2015.02.010

Many health care providers believe that the autopsy is no longer relevant in high-technology medicine era. This has fueled a decline in the hospital autopsy rate. Although it seems that advanced diagnostic tests answer all clinical questions, studies repeatedly demonstrate that an autopsy uncovers as many undiagnosed conditions today as in the past. The forensic autopsy rate has also declined, although not as precipitously. Pathologists are still performing a nineteenth century autopsy procedure that remains essentially unchanged. Informatics offers several potential answers that will evolve the low-tech autopsy into the high-tech autopsy.

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Virtopsy CT in Trauma: Normal post mortem changes and pathological spectrum of findings

Current Problems in Diagnostic Radiology Available online 26 March 2015;  doi:10.1067/j.cpradiol.2015.03.005

Virtopsy or virtual autopsy is an emerging technique, developed to supplement traditional forensic autopsy. Virtopsy can be done by using imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). Virtopsy CT is comprised of a pan-body non-contrast CT scan obtained after death. Virtopsy CT is useful in trauma cases as it can provide an overview of injuries sustained by the victim, detect craniofacial, cerebral, thoracic and osseous injuries and suggest putative causes of death. This can reduce the time taken for forensic autopsy and sometimes obviate the need for a forensic autopsy. However, virtopsy CT reporting is not totally synonymous with interpreting ante-mortem contrast-enhanced CT images as post-mortem decomposition changes also occur. Awareness of imaging appearances of both post-mortem putrefactive changes and pathological findings is essential to avoid errors in interpretation and enable estimation of cause of death in trauma patients

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Catalyst: Our Chemical Lives

ABC TV Catalyst 31/3/2015

Dr Maryanne Demasi investigates the safety of chemicals found in everyday products and compares the level of chemicals in her own body with clean living convert and media personality Sarah Wilson.

Link to Catalyst Website

Watch it now on YouTube