Selectivity in separation using π electron-rich stationary phases for the comprehensive two-dimensional analysis of cafe espresso

The multidimensional high-performance liquid chromatography separations of the complex sample matrix found in café espresso coffee were completed on the propyl phenyl and butyl phenyl columns that contain 3 and 4 carbon atoms in the spacer chain, respectively. Phenyl type stationary phases are able to undergo unique π–π interactions with aromatic compounds. Previous works have found that there are differences in retention characteristics between these chain lengths and this was explored further here. It was found that when analysing the separations by quadrants, using a geometric approach to factor analysis and by measuring the normalised mean radius, subtle differences in the separations were observed and the butyl phenyl phase was more selective for the high to medium polarity species. However, there was very little difference in separation behaviour for the hydrophobic components within the coffee sample. Overall, the analysis of the entire separation showed very little difference.

Stationary phase characterisation and method development for fast two-dimensional HPLC

Fundamental improvements to the experimental protocol of two-dimensional high performance liquid chromatography were developed to accomplish truly quantitative analyses of complex natural products. This included developing novel measurement techniques to elucidate the retention behaviour of monolithic columns. Approaches to streamline chromatographic method development were also explored.

Objective classification of fabric pilling based on the two-dimensional discrete wavelet transform

A number of methods for automated objective ratings of fabric pilling based on image analysis are described in the literature. The periodic structure of fabrics makes them suitable candidates for frequency domain analysis. We propose a new method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure pilling intensity in sample images. We present a preliminary evaluation of the proposed method based on analysis of two series of standard pilling evaluation test images. The initial results suggest that the proposed method is feasible, and that the ability of the method to discriminate between levels of pilling intensity depends on the wavelet analysis scale being closely matched to the fabric interyarn pitch. We also present a heuristic method for optimal selection of an analysis wavelet and associated analysis scale.

Evaluating the robustness of objective pilling classification with the two-dimensional discrete wavelet transform

Previously, we proposed a new method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure pilling intensity in sample fabric images. We have further evaluated this method, and our results indicate that it is robust to small horizontal and/or vertical translations and to significant variations in the brightness of the image under analysis, and is sensitive to rotation and to dilation of the image. These results suggest that as long as precautions are taken to ensure fabric test samples are imaged under consistent conditions of weave/knit pattern alignment (rotation) and apparent interyarn pitch (dilation), the method will yield repeatable results.

Objective assessment of pilling of nonwoven fabrics using the two dimensional discrete wavelet transform

Fabric pilling is a serious problem for the apparel industry, causing an unsightly appearance and premature wear. Woolen products are particularly prone to pilling. Recently, a process for production of woolen nonwoven apparel fabrics has been commercialized in Australia, and may lead to new markets for Australian wool. However, the success of such nonwoven fabrics will partly rely on their propensity to pill. A key element in the control of fabric pilling is the evaluation of resistance to pilling by testing. Resistance to pilling is normally tested in the laboratory by processes that simulate accelerated wear, followed by a manual assessment of the degree of pilling by an expert based on a visual comparison of the sample to a set of test images. To bring more objectivity into the pilling rating process, a number of automated systems based on image analysis have been developed. The authors previously proposed a new method of image analysis based on the two-dimensional discrete wavelet transform to objectively measure the pilling intensity for woven fabrics. This paper presents preliminary work in extending this method to nonwoven fabrics.

Compositional dynamics of a commercial wine fermentation using two-dimensional FTIR correlation analysis

We have demonstrated that compositional changes occurring during a commercial red wine fermentation can be effectively monitored using FTIR spectroscopy and modelled with the aid of two-dimensional correlation techniques. This study represents a novel application of two-dimensional spectroscopy and showed that the reaction rates for the conversion of fructose and glucose to alcohol were different, with the latter being more rapid. The use of a simple three-component model serves to aid interpretation of the data and the results obtained confirm the value of two-dimensional FTIR correlation spectroscopy as a chemometric tool which has considerable potential for process monitoring.

The assessment of тт-тт selective stationary phases for two-dimensional HPLC analysis of foods : application to the analysis of coffee

Differences between alkyl, dipole–dipole, hydrogen bonding, and π-π selective surfaces represented by non-resonance and resonance π-stationary phases have been assessed for the separation of ‘Ristretto’ café espresso by employing 2DHPLC techniques with C18 phase selectivity detection. Geometric approach to factor analysis (GAFA) was used to measure the detected peaks (N), spreading angle (β), correlation, practical peak capacity (n_p) and percentage usage of the separations space, as an assessment of selectivity differences between regional quadrants of the two-dimensional separation plane. Although all tested systems were correlated to some degree to the C18 dimension, regional measurement of separation divergence revealed that performance of specific systems was better for certain sample components. The results illustrate that because of the complexity of the ‘real’ sample obtaining a truly orthogonal two-dimensional system for complex samples of natural origin may be practically impossible.

Two-dimensional correlation localized surface plasmon resonance spectroscopy for analysis of the interaction between metal nanoparticles and bovine serum albumin

Time-resolved extinction spectra assisted with two-dimensional correlation spectroscopy (2DCOS) analysis and principal component analysis (PCA) were employed to investigate the interaction between bovine serum albumin (BSA) and metal nanoparticles (NPs). A series of localized surface plasmon resonance (LSPR) spectra of metal NPs were measured just after a small amount of BSA was added into metal colloids. Through 2DCOS analysis, remarkable changes in the intensities of the LSPR were observed. The interaction process was totally divided into three periods according to the PCA. Transmission electron microscopy, dynamic light scattering, and ζ-potential measurements were also employed to characterize the interaction between BSA and metal NPs. The addition of BSA brings silver NPs to aggregate through the electrostatic interaction between them, but it has less effect on gold NPs. In a gold and silver mixed system, gold NPs can affect the interaction of silver NPs and BSA, leading it to weaken. The combination of 2DCOS analysis and LSPR spectroscopy is powerful for exploring the LSPR spectra of the metal NP involved systems. This combined technique holds great potential in LSPR sensing through analysis of slight, slim spectral changes of metal colloids

Two-dimensional string notation for representing video sequences

Most current work on video indexing concentrates on queries which operate over high level semantic information which must be entirely composed and entered manually. We propose an indexing system which is based on spatial information about key objects in a scene. These key objects may be detected automatically, with manual supervision, and tracked through a sequence using one of a number of recently developed techniques. This representation is highly compact and allows rapid resolution of queries specified by iconic example. A number of systems have been produced which use 2D string notations to index digital image libraries. Just as 2D strings provide a compact and tractable indexing notation for digital pictures, a sequence of 2D strings might provide an index for a video or image sequence. To improve further upon this we reduce the representation to the 2D string pair representing the initial frame, and a sequence of edits to these strings. This takes advantage of the continuity between frames to further reduce the size of the notation. By representing video sequences using string edits, a notation has been developed which is compact, and allows querying on the spatial relationships of objects to be performed without rebuilding the majority of the scene. Calculating ranks of objects directly from the edit sequence allows matching with minimal calculation, thus greatly reducing search time. This paper presents the edit sequence notation and algorithms for evaluating queries over image sequences. A number of optimizations which represent a considerably saving in search time is demonstrated in the paper.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

Random Subspace Two-Dimensional PCA for face recognition

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets -the ORL database, the Yale face database and the extended Yale face database B - confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.

Interobserver reliability of radial head fracture classification : two-dimensional compared with three-dimensional CT

Background: The Broberg and Morrey modification of the Mason classification of radial head fractures has substantial interobserver variation. This study used a large web-based collaborative of experienced orthopaedic surgeons to test the hypothesis that three-dimensional reconstructions of computed tomography (CT) scans improve the interobserver reliability of the classification of radial head fractures according to the Broberg and Morrey modification of the Mason classification.

Methods: Eighty-five orthopaedic surgeons evaluated twelve radial head fractures. They were randomly assigned to review either radiographs and two-dimensional CT scans or radiographs and three-dimensional CT images to determine the fracture classification, fracture characteristics, and treatment recommendations. The kappa multirater measure (κ) was calculated to estimate agreement between observers.

Results: Three-dimensional CT had moderate agreement and two-dimensional CT had fair agreement among observers for the Broberg and Morrey modification of the Mason classification, a difference that was significant. Observers assessed seven fracture characteristics, including fracture line, comminution, articular surface involvement, articular step or gap of ≥2 mm, central impaction, recognition of more than three fracture fragments, and fracture fragments too small to repair. There was a significant difference in kappa values between three-dimensional CT and two-dimensional CT for fracture fragments too small to repair, recognition of three fracture fragments, and central impaction. The difference between the other four fracture characteristics was not significant. Among treatment recommendations, there was fair agreement for both three-dimensional CT and two-dimensional CT.

Conclusions: Although three-dimensional CT led to some small but significant decreases in interobserver variation, there is still considerable disagreement regarding classification and characterization of radial head fractures. Three-dimensional CT may be insufficient to optimize interobserver agreement.

Interobserver reliability of coronoid fracture classification : two-dimensional versus three-dimensional computed tomography

Purpose : This study tests the hypothesis that 3-dimensional computed tomography (CT) reconstructions improve interobserver agreement on classification and treatment of coronoid fractures compared with 2-dimensional CT.

Methods : A total of 29 orthopedic surgeons evaluated 10 coronoid fractures on 2 occasions (first with radiographs and 2-dimensional CT and then with radiographs and 3-dimensional CT), separated by a minimum of 2 weeks. Surgeons classified fractures according to the classifications of Regan and Morrey and of O'Driscoll et al., identified specific characteristics, recommended the most appropriate treatment approach, and made treatment recommendations. The kappa multirater measure (κ) was calculated to estimate agreement between observers.

Results : Regardless of the imaging modality used, there was fair to moderate agreement for most of the observations. Three-dimensional CT improved interobserver agreement in Regan and Morrey's classsication (κ3-dimensional = 0.51 vs κ2-dimensional = 0.40; p < .001) and O'Driscoll et al.'s classifications (κ3-dimensional = 0.48 vs κ2-dimensional = 0.42; p = .009). There were trends toward better reliability for 3-dimensional reconstruction in recognition of coronoid tip fractures (κ3-dimensional = 0.19, κ2-dimensional = 0.03; p = .268), comminution (κ3-dimensional = 0.41 vs κ2-dimensional = 0.29; p = .133), and impacted fragments (κ3-dimensional = 0.39 vs κ2-dimensional = 0.27; p = .094), and in surgeons' opinions on the need for something other than screws or plate for surgical fixation (κ3-dimensional = 0.31 vs κ2-dimensional = 0.15; p = .138). Interobserver agreement on treatment approach was better with 2-dimensional CT (κ3-dimensional = 0.27, κ2-dimensional = 0.32; p = .015).

Conclusions : Three-dimensional CT reconstructions improve interobserver agreement with respect to fracture classification compared with 2-dimensional CT.