Shaneel Chandra

Minimizing fouling at hydrogenated conical-tip carbon electrodes during dopamine detection in vivo.

In this paper, physically small conical-tip carbon electrodes (∼2-5 μm diameter and ∼4 μm axial length) were hydrogenated to develop a probe capable of withstanding fouling during dopamine detection in vivo. Upon hydrogenation, the resultant hydrophobic sp(3) carbon surface deters adsorption of amphiphilic lipids, proteins, and peptides present in extracellular fluid and hence minimizes electrode fouling. These hydrogenated carbon electrodes showed a 35% decrease in sensitivity but little change in the limit of detection for dopamine over a 7-day incubation in a synthetic laboratory solution containing 1.0% (v/v) caproic acid (a lipid), 0.1% (w/v) bovine serum albumin and 0.01% (w/v) cytochrome C (both are proteins), and 0.002% (w/v) human fibrinopeptide B (a peptide). Subsequently, during dopamine detection in vivo, over 70% of the dopamine oxidation current remained after the first 30 min of a 60-min experiment, and at least 50% remained over the next half-period at the hydrogenated carbon electrodes. On the basis of these results, an initial average electrode surface fouling rate of 1.2% min(-1) was estimated, which gradually declined to 0.7% min(-1). These results support minimal fouling at hydrogenated carbon electrodes applied to dopamine detection in vivo.

Biomimetics for early stage biofouling prevention: templates from insect cuticles

A biomimetic antifouling material study was carried out utilising superhydrophobic cicada and dragonfly wings replicated with a polymer (epoxy resin). They were tested in a marine biofouling study for up to 1 week in addition to biofouling assays of protein, carbohydrate and DNA absorption. The materials were compared against a commercial antifouling paint and a polymeric smooth surface constituting a control sample. The replicated surfaces demonstrated superior antifouling properties in comparison to the control and similar efficiency in DNA (10% reduction), protein and carbohydrate adsorption (15%) to the commercial anti-fouling paint. As the fabricated surfaces have roughness at the nanometre scale it is probable that the low adsorption properties, at least in the early stages, may be related to air trapped at the surface. Interestingly the most disordered replicated surface (dragonfly wing replicate) demonstrated the lowest values of absorption.

Origin and Regionality of Wines—the Role of Molecular Spectroscopy

The origin of wine has an important relevance not only in relation to the sustainability of the production systems but also in relation to wine quality, having great impact in terms of the economy of small producer. Terms such as protected geographical status (PGS), protected designation of origin (PDO), protected geographical indication (PGI) and traditional speciality guaranteed (TSG) are been used to characterise wines from different regions and countries. Despite the number of papers and reports published in the literature in relation to issues such as authenticity and discrimination in wine, few reports can be found that explore the use of molecular spectroscopy to specifically target denomination of origin. This report reviews the use of molecular spectroscopy to address such issues within the wine industry.

There is gold in them hills: Predicting potential acid mine drainage events through the use of chemometrics

Disused mines and mining legacy require significant manpower to ameliorate the contaminated environmental surroundings following their disbanding coupled with extraordinary funding to manage these issues. Water (pH, temperature, dissolved oxygen, conductance, metals, sulphate) and total suspended solids (TSS) quality are environmental parameters that are affected by legacy mining activity and often require monitoring and rapid response if events (e.g. rainfall) occur which might affect the surrounding areas. In this study, we have monitored a famous mine site in Queensland, Australia for a number of water and sediment parameters known to be associated with acid mine drainage. This study performed analysis of water and sediment over three years, as well as rainfall data. Principal component analysis (PCA) and partial least squares (PLS) regression was undertaken to investigate the data obtained. It was found that the use of PCA can predict the effect of year and site on the environmental influence of the abandoned mine site, based on the combination of chemical properties and meteorological data.

Carbon nanomaterials and their application to electrochemical sensors: a review

Abstract Carbon has long been applied as an electrochemical sensing interface owing to its unique electrochemical properties. Moreover, recent advances in material design and synthesis, particularly nanomaterials, has produced robust electrochemical sensing systems that display superior analytical performance. Carbon nanotubes (CNTs) are one of the most extensively studied nanostructures because of their unique properties. In terms of electroanalysis, the ability of CNTs to augment the electrochemical reactivity of important biomolecules and promote electron transfer reactions of proteins is of particular interest. The remarkable sensitivity of CNTs to changes in surface conductivity due to the presence of adsorbates permits their application as highly sensitive nanoscale sensors. CNT-modified electrodes have also demonstrated their utility as anchors for biomolecules such as nucleic acids, and their ability to diminish surface fouling effects. Consequently, CNTs are highly attractive to researchers as a basis for many electrochemical sensors. Similarly, synthetic diamonds electrochemical properties, such as superior chemical inertness and biocompatibility, make it desirable both for (bio) chemical sensing and as the electrochemical interface for biological systems. This is highlighted by the recent development of multiple electrochemical diamond-based biosensors and bio interfaces.

The Use of Electrochemical Biosensors in Food Analysis

Rapid and accurate analysis of food produce is essential to screen for species that may cause significant health risks like bacteria, pesticides and other toxins. Considerable developments in analytical techniques and instrumentation, for example chromatography, have enabled the analyses and quantitation of these contaminants. However, these traditional technologies are constrained by high cost, delayed analysis times, expensive and laborious sample preparation stages and the need for highly-trained personnel. Therefore, emerging, alternative technologies, for example biosensors may provide viable alternatives. Rapid advances in electrochemical biosensors have enabled significant gains in quantitative detection and screening and show incredible potential as a means of countering such limitations. Apart from demonstrating high specificity towards the analyte, these biosensors also address the challenge of the multifactorial food industry of providing high analytical accuracy amidst complex food matrices, while also overcoming differing densities, pH and temperatures. This (public and Industry) demand for faster, reliable and cost-efficient analysis of food samples, has driven investment into biosensor design. Here, we discuss some of the recent work in this area and critique the role and contributions biosensors play in the food industry. We also appraise the challenges we believe biosensors need to overcome to become the industry standard.

Handling complexity in animal and plant science research-from single to functional traits: Are we there yet?

The current knowledge of the main factors governing livestock, crop and plant quality as well as yield in different species is incomplete. For example, this can be evidenced by the persistence of benchmark crop varieties for many decades in spite of the gains achieved over the same period. In recent years, it has been demonstrated that molecular breeding based on DNA markers has led to advances in breeding (animal and crops). However, these advances are not in the way that it was anticipated initially by the researcher in the field. According to several scientists, one of the main reasons for this was related to the evidence that complex target traits such as grain yield, composition or nutritional quality depend on multiple factors in addition to genetics. Therefore, some questions need to be asked: are the current approaches in molecular genetics the most appropriate to deal with complex traits such as yield or quality? Are the current tools for phenotyping complex traits enough to differentiate among genotypes? Do we need to change the way that data is collected and analysed?

Meat Consumption and Green Gas Emissions: a Chemometrics Analysis

The aim of this study was to relate greenhouse gas emissions (GHGE) from both livestock production (enteric) and agriculture emissions with the consumption of meat from meat producer and importer countries. Data for meat consumption and emission levels of agriculture and livestock production were sourced from the Food and Agriculture Organisation (FAO) database statistics (1961 to 2013). This data is freely available to the public and research community from the FAO webpage. Statistical data was analysed using principal component analysis (PCA), and regression models between GHGE and meat consumption were developed using partial least squares regression (PLS) and validated using cross-validation. Results of this study confirmed observations and anecdotal evidence that enteric and green gas emissions contribute to the perception of meat consumption. Although the results presented in this study are based on the data collected by an international organisation, the authors believe that results from this study can be utilised and incorporated to climate change modelling systems, in order to better understand and define the effect of GHGE on the environmental and economical sustainabilities of the meat production.

Unfrazzled by Fizziness: Identification of Beers Using Attenuated Total Reflectance Mid-infrared Spectroscopy and Multivariate Analysis

Mid-infrared (MIR) spectroscopy coupled with attenuated total reflectance (ATR) was used to analyse a series of different beer types in order to confirm their identity (e.g. ale vs lager, commercial vs craft beer). Multivariate data analyses such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse and to discriminate the beer samples analysed based on their infrared spectra. Correct classification rates of 100% were achieved in order to differentiate between ale and lager and also between commercial and craft beer sample types, respectively. Overall, the results of this study demonstrated the capability of MIR spectroscopy combined with PLS-DA to classify beer samples according to style (ale vs lager) and production (commercial vs craft). Furthermore, dissolved gases in the beer products were proven not to interfere as overlapping artefacts in the analysis. The benefits of using MIR-ATR for rapid and detailed analysis coupled with multivariate analysis can be considered a valuable tool for researchers and brewers interested in quality control, traceability and food adulteration. The novelty of this study is potentially far reaching, whereby customs and agencies can utilise these methods to mitigate beverage fraud.

Comparison of Ultrasound-Assisted Extraction with Static Extraction as Pre-Processing Method Before Gas Chromatography Analysis of Cereal Lipids

A pre-processing method for the extraction of fatty acids from cereal grains is described. Ultrasound (sonication)-assisted extraction was compared to a static extraction method as the pre-processing method before gas chromatography mass spectrometry (GCMS) analysis of barley, sorghum and corn grain samples. Principal component analysis (PCA) was applied to identify patterns in the chromatographic data and classify the samples based on the extraction treatment (sonication vs static). The results of the PCA clearly distinguish between the sonication and static samples. It was observed that the ultrasound-assisted extraction impacted the chromatographs of the grain samples, a factor which simplifies the interpretation of the data. In addition, the results showed the ability of using multivariate technique such as PCA to improve data interpretation.