Use of copper and gold electrodes as sensitive elements for fabrication of an electronic tongue: Discrimination of wines and whiskies

A low-cost method is proposed to classify wine and whisky samples using a disposable voltammetric electronic tongue that was fabricated using gold and copper substrates and a pattern recognition technique (Principal Component Analysis). The proposed device was successfully used to discriminate between expensive and cheap whisky samples and to detect adulteration processes using only a copper electrode. For wines, the electronic tongue was composed of copper and gold working electrodes and was able to classify three different brands of wine and to make distinctions regarding the wine type, i.e., dry red, soft red, dry white and soft white brands. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.

Discrimination of Brazilian red wines according to the viticultural region, varietal, and winery origin

This work evaluated the physicochemical composition of 171 red Brazilian wines from the 2006 vintage, which were represented by 21 varietals. These wines were produced by 58 Brazilian wineries in different regions of the country, with latitudes varying from 9º to 31º South. Physicochemical wine analysis was performed in the same year and discrimination in the viticultural regions, varietal wines, and wineries was performed by means of the principal component analysis (PCA). The main results show that wines from São Joaquim had higher values of A420, A520, A620, color intensity, total phenolic compounds, anthocyanins, and dry extracts, while those from Toledo had lower values of these variables; those from Vale do São Francisco had higher values of potassium, pH, density, and volatile acidity; from Serra do Nordeste A, they had higher titratable acidity; and from Planalto Superior B, higher hue. Regarding the varietal wines, PCA mainly discriminated the wines produced from the varieties Ancellotta, Teroldego, Egiodola, Refosco, Marselan, Cabernet Sauvignon, Pinotage, Pinot Noir, Malbec, Arinarnoa, Barbera, and Alfrocheiro. In relation to wineries, twenty two of them were discriminated by their higher values of some variables, i.e., three were characterized by color intensity; three by hue; eight by alcohol content; six by potassium, dry extract, density, and pH; and two by titratablel acidity.

Demonstration of Near-Optimal Discrimination of Optical Coherent States

The optimal discrimination of nonorthogonal quantum states with minimum error probability is a fundamental task in quantum measurement theory as well as an important primitive in optical communication. In this work, we propose and experimentally realize a new and simple quantum measurement strategy capable of discriminating two coherent states with smaller error probabilities than can be obtained using the standard measurement devices: the Kennedy receiver and the homodyne receiver.

Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia

Multispectral widefield optical imaging has the potential to improve early detection of oral cancer. The appropriate selection of illumination and collection conditions is required to maximize diagnostic ability. The goals of this study were to (i) evaluate image contrast between oral cancer/precancer and non-neoplastic mucosa for a variety of imaging modalities and illumination/collection conditions, and (ii) use classification algorithms to evaluate and compare the diagnostic utility of these modalities to discriminate cancers and precancers from normal tissue. Narrowband reflectance, autofluorescence, and polarized reflectance images were obtained from 61 patients and 11 normal volunteers. Image contrast was compared to identify modalities and conditions yielding greatest contrast. Image features were extracted and used to train and evaluate classification algorithms to discriminate tissue as non-neoplastic, dysplastic, or cancer; results were compared to histologic diagnosis. Autofluorescence imaging at 405-nm excitation provided the greatest image contrast, and the ratio of red-to-green fluorescence intensity computed from these images provided the best classification of dysplasia/cancer versus non-neoplastic tissue. A sensitivity of 100% and a specificity of 85% were achieved in the validation set. Multispectral widefield images can accurately distinguish neoplastic and non-neoplastic tissue; however, the ability to separate precancerous lesions from cancers with this technique was limited. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3516593]

Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification-k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)-and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

Discrimination of adsorption kinetic models for the description of hydrocarbon adsorption in activated carbon

Five kinetic models for adsorption of hydrocarbons on activated carbon are compared and investigated in this study. These models assume different mass transfer mechanisms within the porous carbon particle. They are: (a) dual pore and surface diffusion (MSD), (b) macropore, surface, and micropore diffusion (MSMD), (c) macropore, surface and finite mass exchange (FK), (d) finite mass exchange (LK), and (e) macropore, micropore diffusion (BM) models. These models are discriminated using the single component kinetic data of ethane and propane as well as the multicomponent kinetics data of their binary mixtures measured on two commercial activated carbon samples (Ajax and Norit) under various conditions. The adsorption energetic heterogeneity is considered for all models to account for the system. It is found that, in general, the models assuming diffusion flux of adsorbed phase along the particle scale give better description of the kinetic data.

Identification and minisequencing-based discrimination of SHV beta-lactamases in nosocomial infection-associated Klebsiella pneumoniae in Brisbane, Australia

Extended-spectrum beta-lactamases (ESBLs) are active against oxyimino cephalosporins and monobactams. Twenty-one Klebsiella pneumoniae isolates obtained between 1991 and 1995 at the Princess Alexandra Hospital in Brisbane, Australia, were subject to amplification and sequencing of the SHV beta-lactamase-encoding genes. Thirteen strains were phenotypically ESBL positive. Of these, six strains carried the bla(SHV-2a) gene and seven strains carried the bla(SHV-12) gene. Eight strains were phenotypically ESBL negative. Of these, seven strains carried the non-ESBL bla(SHV-11) gene and one strain carried the non-ESBL bla(SHV-1) gene. There was complete correspondence between the ESBL phenotype and the presence or absence of an ESBL-encoding gene(s). In addition, it was determined that of the 13 ESBL-positive strains, at least 4 carried copies of a non-ESBL-encoding gene in addition to the bla(SHV-2a) or bla(SHV12) gene. A minisequencing-based assay was developed to discriminate the different SHV classes. This technique, termed first-nucleotide change, involves the identification of the base added to a primer in a single-nucleotide extension reaction. The assay targeted polymorphisms at the first bases of codons 238 and 240 and reliably discriminated ESBL-positive strains from ESBL-negative strains and also distinguished strains carrying bla(SHV-2a) from strains carrying bla(SHV-12). In addition, this method was used to demonstrated an association between the relative copy numbers of bla(SHV) genes in individual strains and the levels of antibiotic resistance.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Discrimination of bacteriophage infected cells using locked nucleic acid fluorescent in situ hybridization (LNA-FISH)

Bacteriophage-host interaction studies in biofilm structures are still challenging due to the technical limitations of traditional methods. The aim of this study was to provide a direct fluorescence in situ hybridization (FISH) method based on locked nucleic acid (LNA) probes, which targets the phage replication phase, allowing the study of population dynamics during infection. Bacteriophages specific for two biofilm-forming bacteria, Pseudomonas aeruginosa and Acinetobacter, were selected. Four LNA probes were designed and optimized for phage-specific detection and for bacterial counterstaining. To validate the method, LNA-FISH counts were compared with the traditional plaque forming unit (PFU) technique. To visualize the progression of phage infection within a biofilm, colony-biofilms were formed and infected with bacteriophages. A good correlation (r=0.707) was observed between LNA-FISH and PFU techniques. In biofilm structures, LNA-FISH provided a good discrimination of the infected cells and also allowed the assessment of the spatial distribution of infected and non-infected populations.

The Discrimination of Colored Acrylic, Cotton, and Wool Textile Fibers Using Micro-Raman Spectroscopy. Part 1 : In Situ Detection and Characterization of Dyes

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633-nm laser did not provide Raman information. The 514-nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830-nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.

Discrimination of cortical laminae using MEG.

Typically MEG source reconstruction is used to estimate the distribution of current flow on a single anatomically derived cortical surface model. In this study we use two such models representing superficial and deep cortical laminae. We establish how well we can discriminate between these two different cortical layer models based on the same MEG data in the presence of different levels of co-registration noise, Signal-to-Noise Ratio (SNR) and cortical patch size. We demonstrate that it is possible to make a distinction between superficial and deep cortical laminae for levels of co-registration noise of less than 2mm translation and 2° rotation at SNR>11dB. We also show that an incorrect estimate of cortical patch size will tend to bias layer estimates. We then use a 3D printed head-cast (Troebinger et al., 2014) to achieve comparable levels of co-registration noise, in an auditory evoked response paradigm, and show that it is possible to discriminate between these cortical layer models in real data.

Morphometric discrimination of females of five species of Anopheles of the subgenus Nyssorhynchus from Southern and Northwest Colombia

The most important vectors of human Plasmodium in the neotropics belong to the subgenus Nyssorhynchus. These species are generally sympatric in terms of their geographical distributions. Some are difficult to identify based solely on examination of adult females using the available morphological keys, in these cases examination of immature stages and male genitalia is required to make correct determinations. However, in epidemiological studies it is necessary to identify the species of adult females which are found near humans, i.e. in studies of malaria transmission or evaluation of control measures. The purpose of the present study was to evaluate the discrimination of adult females of different species of Nyssorhynchus isolated mainly from Southern Colombia (department of Putumayo), using morphometric analysis. Adult females were obtained after rearing larvae collected in natural breeding places and from the progeny of females collected on humans. The morphological characteristics of the immature stages allowed the identification of four species of the subgroup Oswaldoi from Southern Colombia: Anopheles rangeli Gabaldon, Cova Garcia & Lopez, An. oswaldoi (Peryassu), An. benarrochi Gabaldon, Cova Garcia & Lopez and An. triannulatus (Neiva & Pinto). The species An. nuneztovari (Gabaldon) from the Northwest of Colombia was included for comparison. Morphometric analysis allowed differentiation of the females of all species to a confidence level approaching 90% using principal components analysis of 10 wing and leg variables, followed by canonical variate analysis of the first four principal components. We conclude that morphometrics may represent a useful taxonomic tool for this group and that its use should be further studied.