Drying process of microcrystalline cellulose studied by attenuated total reflection IR spectroscopy with two-dimensional correlation spectroscopy and principal component analysis

Molecular interactions between microcrystalline cellulose (MCC) and water were investigated by attenuated total reflection infrared (ATR/IR) spectroscopy. Moisture-content-dependent IR spectra during a drying process of wet MCC were measured. In order to distinguish overlapping O–H stretching bands arising from both cellulose and water, principal component analysis (PCA) and, generalized two-dimensional correlation spectroscopy (2DCOS) and second derivative analysis were applied to the obtained spectra. Four typical drying stages were clearly separated by PCA, and spectral variations in each stage were analyzed by 2DCOS. In the drying time range of 0–41 min, a decrease in the broad band around 3390 cm−1 was observed, indicating that bulk water was evaporated. In the drying time range of 49–195 min, decreases in the bands at 3412, 3344 and 3286 cm−1 assigned to the O6H6cdots, three dots, centeredO3′ interchain hydrogen bonds (H-bonds), the O3H3cdots, three dots, centeredO5 intrachain H-bonds and the H-bonds in Iβ phase in MCC, respectively, were observed. The result of the second derivative analysis suggests that water molecules mainly interact with the O6H6cdots, three dots, centeredO3′ interchain H-bonds. Thus, the H-bonding network in MCC is stabilized by H-bonds between OH groups constructing O6H6cdots, three dots, centeredO3′ interchain H-bonds and water, and the removal of the water molecules induces changes in the H-bonding network in MCC.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Evaluation of eight spring onion genotypes, sulphur nutrition and soil-type effects with an electronic nose

Genotype, sulphur (S) nutrition and soil-type effects on spring onion quality were assessed using a 32-conducting polymer sensor E-nose. Relative changes in sensor resistance ratio (% dR/R) varied among eight spring onion genotypes. The % dR/R was reduced by S application in four of the eight genotypes. For the other four genotypes, S application gave no change in % dR/R in three, and increased % dR/R in the other. E-nose classification of headspace volatiles by a two-dimensional principal component analysis (PCA) plot for spring onion genotypes differed for S fertilisation vs. no S fertilisation. Headspace volatiles data set clusters for cv. 'White Lisbon' grown on clay or on sandy loam overlapped when 2.9 [Mahalanobis distance value (D2) = 1.6], or 5.8-(D2 = 0.3) kg S ha-1 was added. In contrast, clear separation (D2 = 7.5) was recorded for headspace volatile clusters for 0 kg S hd-1 on clay vs. sandy loam. Addition of 5.8 kg S ha-1 increased pyruvic acid content (mmole g-1 fresh weight) by 1.7-fold on average across the eight genotypes. However, increased S from 2.9 to 5.8 kg ha-1 did not significantly (P > 0.05) influence % dR/R, % dry matter (DM) or total soluble solids (TSS) contents, but significantly (P < 0.05) increased pyruvic acid content. TSS was significantly (P < 0.05) reduced by S addition, while % DM was unaffected. In conclusion, the 32-conducting polymer E-nose discerned differences in spring onion quality that were attributable to genotype and to variations in growing conditions as shown by the significant (P < 0.05) interaction effects for % dR/R.

Non-linear principal components analysis: an alternative method for finding patterns in environmental data

The main purpose of this article is to gain an insight into the relationships between variables describing the environmental conditions of the Far Northern section of the Great Barrier Reef, Australia, Several of the variables describing these conditions had different measurement levels and often they had non-linear relationships. Using non-linear principal component analysis, it was possible to acquire an insight into these relationships. Furthermore. three geographical areas with unique environmental characteristics could be identified. Copyright (c) 2005 John Wiley & Sons, Ltd.

Wave-generated flow on coral reefs - an analysis for two-dimensional horizontal reef-tops with steep faces

Waves breaking on the seaward rim of a coral reef generate a flow of water from the exposed side of the reef to the sheltered side and/or to either channels through the reef-rim or lower sections of the latter. This wave-generated flow is driven by the water surface gradient resulting from the wave set-up created by the breaking waves. This paper reviews previous approaches to modelling wave-generated flows across coral reefs and discusses the influence of reef morphology and roughness upon these flows. Laboratory measurements upon a two-dimensional horizontal reef platform with a steep reef face provide the basis for extending a previous theoretical analysis for wave set-up on a reef in the absence of a flow [Gourlay, M.R., 1996b. Wave set-up on coral reefs. 2. Set-up on reefs with various profiles. Coastal Engineering 28, 1755] to include the interaction between a unidirectional flow and the wave set-up. The laboratory model results are then used to demonstrate that there are two basic reef-top flow regimes-reef-top control and reef-rim control. Using open channel flow theory, analytical relationships are derived for the reef-top current velocity in terms of the offreef wave conditions, the reef-top water depth and the physical characteristics of the reef-top topography. The wave set-up and wave-generated flow relationships are found to predict experimental values with reasonable accuracy in most cases. The analytical relationships are used to investigate wave-generated flows into a boat harbour channel on Heron Reef in the southern Great Barrier Reef. (c) 2005 Elsevier B.V. All rights reserved.

Analysis of O-glycosylation site occupancy in bovine kappa-casein glycoforms separated by two-dimensional gel electrophoresis

The ability of two-dimensional gel electrophoresis (2-DE) to separate glycoproteins was exploited to separate distinct glycoforms of kappa-casein that differed only in the number of O-glycans that were attached. To determine where the glycans were attached, the individual glycoforms were digested in-gel with pepsin and the released glycopeptides were identified from characteristic sugar ions in the tandem mass spectrometry (MS) spectra. The O-glycosylation sites were identified by tandem MS after replacement of the glycans with ammonia/aminoethanethiol. The results showed that glycans were not randomly distributed among the five potential glycosylation sites in kappa-casein. Rather, glycosylation of the monoglycoform could only be detected at a single site, T-152. Similarly the diglycoform appeared to be modified exclusively at T-152 and T-163, while the triglycoform was modified at T-152, T-163 and T-154. While low levels of glycosylation at other sites cannot be excluded the hierarchy of site occupation between glycoforms was clearly evident and argues for an ordered addition of glycans to the protein. Since all five potential O-glycosylation sites can be glycosylated in vivo, it would appear that certain sites remain latent until other sites are occupied. The determination of glycosylation site occupancy in individual glycoforms separated by 2-DE revealed a distinct pattern of in vivo glycosylation that has not been recognized previously.

A two-dimensional fuzzy random model of soil pore structure

A new conceptual model for soil pore-solid structure is formalized. Soil pore-solid structure is proposed to comprise spatially abutting elements each with a value which is its membership to the fuzzy set ''pore,'' termed porosity. These values have a range between zero (all solid) and unity (all pore). Images are used to represent structures in which the elements are pixels and the value of each is a porosity. Two-dimensional random fields are generated by allocating each pixel a porosity by independently sampling a statistical distribution. These random fields are reorganized into other pore-solid structural types by selecting parent points which have a specified local region of influence. Pixels of larger or smaller porosity are aggregated about the parent points and within the region of interest by controlled swapping of pixels in the image. This creates local regions of homogeneity within the random field. This is similar to the process known as simulated annealing. The resulting structures are characterized using one-and two-dimensional variograms and functions describing their connectivity. A variety of examples of structures created by the model is presented and compared. Extension to three dimensions presents no theoretical difficulties and is currently under development.

Genetic variability in cultivated common bean beyond the two major gene pools

It is generally accepted that two major gene pools exist in cultivated common bean (Phaseolus vulgaris L.), a Middle American and an Andean one. Some evidence, based on unique phaseolin morphotypes and AFLP analysis, suggests that at least one more gene pool exists in cultivated common bean. To investigate this hypothesis, 1072 accessions from a common bean core collection from the primary centres of origin, held at CIAT, were investigated. Various agronomic and morphological attributes (14 categorical and 11 quantitative) were measured. Multivariate analyses, consisting of homogeneity analysis and clustering for categorical data, clustering and ordination techniques for quantitative data and nonlinear principal component analysis for mixed data, were undertaken. The results of most analyses supported the existence of the two major gene pools. However, the analysis of categorical data of protein types showed an additional minor gene pool. The minor gene pool is designated North Andean and includes phaseolin types CH, S and T; lectin types 312, Pr, B and K; and mostly A5, A6 and A4 types alpha-amylase inhibitor. Analysis of the combined categorical data of protein types and some plant categorical data also suggested that some other germplasm with C type phaseolin are distinguished from the major gene pools.

Comments on "Maintenance policies with two-dimensional warranty"

Chen and Popova [Res. Engng Syst. Saf. 77 (2002) 61] discuss maintenance policies for items sold with a two-dimensional warranty. However, their paper fails to give a proper review of the literature and it also contains errors. In this note we first review the relevant literature and then comment on the errors in their analysis. (C) 2003 Elsevier Ltd. All rights reserved.

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.

Capillary phenomena in the framework of the two-dimensional density functional theory

We present results of application of the density functional theory (DFT) to adsorption and desorption in finite and infinite cylindrical pores accounting for the density distribution in radial and axial directions. Capillary condensation via formation of bridges is considered using canonical and grand canonical versions of the 2D DFT. The potential barrier of nucleation is determined as a function of the bulk pressure and the pore diameter. In the framework of the conventional assumptions on intermolecular interactions both 1D and 2D DFT versions lead to the same results and confirm the classical scenario of condensation and evaporation: the condensation occurs at the vapor-like spinodal point, and the evaporation corresponds to the equilibrium transition pressure. The analysis of experimental data on argon and nitrogen adsorption on MCM-41 samples seems to not completely corroborate this scenario, with adsorption branch being better described by the equilibrium pressure - diameter dependence. This points to the necessity of the further development of basic representations on the hysteresis phenomena.

Modeling of adsorption and nucleation in infinite cylindrical pores by two-dimensional density functional theory

In this paper, we present an analysis of argon adsorption in cylindrical pores having amorphous silica structure by means of a nonlocal density functional theory (NLDFT). In the modeling, we account for the radial and longitudinal density distributions, which allow us to consider the interface between the liquidlike and vaporlike fluids separated by a hemispherical meniscus in the canonical ensemble. The Helmholtz free energy of the meniscus was determined as a function of pore diameter. The canonical NLDFT simulations show the details of density rearrangement at the vaporlike and liquidlike spinodal points. The limits of stability of the smallest bridge and the smallest bubble were also determined with the canonical NLDFT. The energy of nucleation as a function of the bulk pressure and the pore diameter was determined with the grand canonical NLDFT using an additional external potential field. It was shown that the experimentally observed reversibility of argon adsorption isotherms at its boiling point up to the pore diameter of 4 nm is possible if the potential barrier of 22kT is overcome due to density fluctuations.

Using two dimensional sectional distributions to infer three dimensional volumetric distributions - Validation using tomography

The critical process parameter for mineral separation is the degree of mineral liberation achieved by comminution. The degree of liberation provides an upper limit of efficiency for any physical separation process. The standard approach to measuring mineral liberation uses mineralogical analysis based two-dimensional sections of particles which may be acquired using a scanning electron microscope and back-scatter electron analysis or from an analysis of an image acquired using an optical microscope. Over the last 100 years, mathematical techniques have been developed to use this two dimensional information to infer three-dimensional information about the particles. For mineral processing, a particle that contains more than one mineral (a composite particle) may appear to be liberated (contain only one mineral) when analysed using only its revealed particle section. The mathematical techniques used to interpret three-dimensional information belong, to a branch of mathematics called stereology. However methods to obtain the full mineral liberation distribution of particles from particle sections are relatively new. To verify these adjustment methods, we require an experimental method which can accurately measure both sectional and three dimensional properties. Micro Cone Beam Tomography provides such a method for suitable particles and hence, provides a way to validate methods used to convert two-dimensional measurements to three dimensional estimates. For this study ore particles from a well-characterised sample were subjected to conventional mineralogical analysis (using particle sections) to estimate three-dimensional properties of the particles. A subset of these particles was analysed using a micro-cone beam tomograph. This paper presents a comparison of the three-dimensional properties predicted from measured two-dimensional sections with the measured three-dimensional properties.