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.

Dimethylsulfide dehydrogenase from rhodovulum sulfidophilum: EPR spectroscopy and biochemical analysis reveal its place in the DMSO reductase family of molybdenum enzymes

Dimethylsulfide (DMS) dehydrogenase catalyses the oxidation of DMS to dimethylsulfoxide. The purified enzyme has three subunits of Mr = 94, 38 and 32 kDa and has an optical spectrum dominated by a b-type cytochrome. The metal ion and nucleotide analysis revealed 0.5 g-atom Mo, 9.8 g-atom Fe and 1.96 mol GMP per tool of enzyme. Taken together, these data indicate that DMS dehydrogenase contains a bis(MGD)Mo cofactor. A comparison of the Nterminal amino acid sequence of DMS dehydrogenase revealed that the Mo-containing ct-subunit was most closely related to the c~-subunits of nitrate reductase (NarG) and selenate reductase (SerA). Similarly, the [~-subunit of DMS dehydrogenase was most closely related to the [3-subunits of nitrate reductase (NarH) and selenate reductase (SerB). Variable temperature X-band EPR spectra (120-2K) of 'as isolated' DMS dehydrogenase showed resonances arising from multiple redox centres, Mo(V), [3Fe-4S] +, [4Fe-4S] ÷. A pH dependent EPR study of the Mo(V) centre in lH20 and 2H20 reveals the presence of three Mo(V) species in equilibrium, Mo(V)-OH2, Mo(V)-X and Mo(V)-OH. Between pH6 and 8.2 the dominant species is Mo(V)-OH2 and Mo(V)-X is a minor component. X is probably the anion, chloride. Comparison of the rhombicity and anisotropy parameters for the Mo(V) species in DMS dehydrogenase with other Mo(V) centres in metalloproteins showed that it was most similar to the low pH nitrite spectrum of E. coli nitrate reductase (NarGHI). The spin Hamiltonian parameters (2.0158, 1.8870, 1.8620) for the [4Fe-4S] + cluster suggests the presence of histidine (N) coordination to iron in this cluster. It is suggested that this unusual [Fe-S] cluster may be associated with a histidine-cysteine rich sequence at the N-terminus of the ct-subunit of DMS dehydrogenase.

Cluster analysis of high-dimensional data: A case study

Normal mixture models are often used to cluster continuous data. However, conventional approaches for fitting these models will have problems in producing nonsingular estimates of the component-covariance matrices when the dimension of the observations is large relative to the number of observations. In this case, methods such as principal components analysis (PCA) and the mixture of factor analyzers model can be adopted to avoid these estimation problems. We examine these approaches applied to the Cabernet wine data set of Ashenfelter (1999), considering the clustering of both the wines and the judges, and comparing our results with another analysis. The mixture of factor analyzers model proves particularly effective in clustering the wines, accurately classifying many of the wines by location.

Using multivariate analysis to predict the behaviour of soils under effluent irrigation

Onsite wastewater treatment systems aim to assimilate domestic effluent into the environment. Unfortunately failure of such systems is common and inadequate effluent treatment can have serious environmental implications. The capacity of a particular soil to treat wastewater will change over time. The physical properties influence the rate of effluent movement through the soil and its chemical properties dictate the ability to renovate effluent. A research project was undertaken to determine the role that physical and chemical soil properties play in predicting the long-term behaviour of soil under effluent irrigation and to determine if they have a potential function as early indicators of adverse effects of effluent irrigation on treatment sustainability. Principal Component Analysis (PCA) and Cluster Analysis grouped the soils independently of their soil classifications and allowed us to distinguish the most suitable soils for sustainable long term effluent irrigation and determine the most influential soil parameters to characterise them. Multivariate analysis allowed a clear distinction between soils based on the cation exchange capacities. This in turn correlated well with the soil mineralogy. Mixed mineralogy soils in particular sodium or magnesium dominant soils are the most susceptible to dispersion under effluent irrigation. The soil Exchangeable Sodium Percentage (ESP) was identified as a crucial parameter and was highly correlated with percentage clay, electrical conductivity, exchangeable sodium, exchangeable magnesium and low Ca:Mg ratios (less than 0.5).

Electronic nose evaluation of onion headspace volatiles and bulb quality as affected by nitrogen, sulphur and soil type

Edaphic factors affect the quality of onions (Allium cepa). Two experiments were carried out in the field and glasshouse to investigate the effects of N (field: 0, 120 kg ha(-1); glasshouse: 0, 108 kg ha(-1)), S (field: 0, 20 kg ha(-1); glasshouse: 0, 4.35 kg ha(-1)) and soil type (clay, sandy loam) on onion quality. A conducting polymer sensor electronic nose (E-nose) was used to classify onion headspace volatiles. Relative changes in the E-nose sensor resistance ratio (%dR/R) were reduced following N and S fertilisation. A 2D Principal Component Analysis (PCA) of the E-nose data sets accounted for c. 100% of the variations in onion headspace volatiles in both experiments. For the field experiment, E-nose data set clusters for headspace volatiles for no N-added onions overlapped (D-2 = 1.0) irrespective of S treatment. Headspace volatiles of N-fertilised onions for the glasshouse sandy loam also overlapped (D-2 = 1.1) irrespective of S treatment as compared with distinct separations among clusters for the clay soil. N fertilisation significantly (P < 0.01) reduced onion bulb pyruvic acid concentration (flavour) in both experiments. S fertilisation increased pyruvic acid concentration significantly (P < 0.01) in the glasshouse experiment, especially for the clay soil, but had no effect on pyruvic acid concentration in the field. N and S fertilisation significantly (P < 0.01) increased lachrymatory potency (pungency), but reduced total soluble solids (TSS) content in the field experiment. In the glasshouse experiment, N and S had no effect on TSS. TSS content was increased on the clay by 1.2-fold as compared with the sandy loam. Onion tissue N:water-soluble SO42- ratios of between five and eight were associated with greater %dR/R and pyruvic acid concentration values. N did not affect inner bulb tissue microbial load. In contrast, S fertilisation reduced inner bulb tissue microbial load by 80% in the field experiment and between 27% (sandy loam) and 92% (clay) in the glasshouse experiment. Overall, onion bulb quality discriminated by the E-nose responded to N, S and soil type treatments, and reflected their interactions. However, the conventional analytical and sensory measures of onion quality did not correlate with %dR/R.

Multivariate monitoring of a biological wastewater treatment process: a case study at Melbourne Water's Western Treatment Plant

Biological wastewater treatment is a complex, multivariate process, in which a number of physical and biological processes occur simultaneously. In this study, principal component analysis (PCA) and parallel factor analysis (PARAFAC) were used to profile and characterise Lagoon 115E, a multistage biological lagoon treatment system at Melbourne Water's Western Treatment Plant (WTP) in Melbourne, Australia. In this study, the objective was to increase our understanding of the multivariate processes taking place in the lagoon. The data used in the study span a 7-year period during which samples were collected as often as weekly from the ponds of Lagoon 115E and subjected to analysis. The resulting database, involving 19 chemical and physical variables, was studied using the multivariate data analysis methods PCA and PARAFAC. With these methods, alterations in the state of the wastewater due to intrinsic and extrinsic factors could be discerned. The methods were effective in illustrating and visually representing the complex purification stages and cyclic changes occurring along the lagoon system. The two methods proved complementary, with each having its own beneficial features. (C) 2003 Elsevier B.V. All rights reserved.

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.