Factor analytical study on water quality in Lake Saimaa, Finland

Multivariate data analysis methods (4-way Candecomp-PARAFAC model solved with Multilinear Engine (ME-1)) were used to interpret the data of over two decades to study the changes in the water of Lake Saimaa in Finland. Earlier studies have shown that it is difficult to extract the natural background from the other sources of variation. By using the multilinear model three interpretable factors representing natural and anthropogenic processes could be extracted. The natural long-term variation, seasonal fluctuation and dilution of discharges in the recipient area could be extracted into their own factors, which could be easily visualized. The variation could be also presented with estimated variation in the water quality parameters caused by each of these natural or anthropogenic processes. Received: 27 September 2000 / Revised: 11 January 2001 / Accepted: 14 January 2001     

Quantification of a number of biogenic amines and their metabolites in brain homogenates by gas chromatography--mass spectrometry

A sensitive method is described for the assay of various biogenic amines and their metabolites from brain tissue homogenates. Based on gas chromatography-mass spectrometry and selected ion monitoring, the method allows reliable low-level quantification of the compounds of interest. By careful study of reaction conditions both in the preparation of derivatives for gas chromatography and in the synthesis of deuterated analogues for internal standard use, relatively simple procedures could be applied with good analytical efficiency. Correlation of the results with those obtained by a high-performance liquid chromatographic procedure was also investigated, and a neurologic application of the method is briefly discussed.     

Bayesian predictive modeling and comparison of oil samples

Statistical comparison of oil samples is an integral part of oil spill identification, which deals with the process of linking an oil spill with its source of origin. In current practice, a frequentist hypothesis test is often used to evaluate evidence in support of a match between a spill and a source sample. As frequentist tests are only able to evaluate evidence against a hypothesis but not in support of it, we argue that this leads to unsound statistical reasoning. Moreover, currently only verbal conclusions on a very coarse scale can be made about the match between two samples, whereas a finer quantitative assessment would often be preferred. To address these issues, we propose a Bayesian predictive approach for evaluating the similarity between the chemical compositions of two oil samples. We derive the underlying statistical model from some basic assumptions on modeling assays in analytical chemistry, and to further facilitate and improve numerical evaluations, we develop analytical expressions for the key elements of Bayesian inference for this model. The approach is illustrated with both simulated and real data and is shown to have appealing properties in comparison with both standard frequentist and Bayesian approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Raman and ATR FTIR spectroscopy in reactive crystallization: Simultaneous monitoring of solute concentration and polymorphic state of the crystals

Batch-reactive crystallization of the two polymorphs of l-glutamic acid was studied using in-line Raman and ATR FTIR spectroscopy. It was observed that the barrier to the nucleation of the stable β-form was higher, and thus the occurrence of β-form nucleation requires a higher supersaturation level. The local supersaturation level inside the reactive crystallizer is significantly affected by the feeding manner of the reactant. When the reactant was fed to a poorly mixed zone, such as the surface of the liquid, a high local supersaturation level was generated near the feeding point. This high local supersaturation level drastically increased with the increase in the concentrations of the reactants. As a consequence, the fraction of the β-form increased with the increase in reactants concentrations. On the other hand, feeding the reactant to a well-mixed zone near the impeller can avoid the occurrence of high local supersaturation, and therefore the dependence of the polymorphic composition of the final product on the concentration of the reactants can be reduced. The information obtained from the spectroscopy leads to improved understanding of the precipitation process and offers great potential for process optimization and control of crystalline quality.     

Effect of missing values in estimation of mean of auto-correlated measurement series

Sampling and uncertainty of sampling are important tasks, when industrial processes are monitored. Missing values and unequal sources can cause problems in almost all industrial fields. One major problem is that during weekends samples may not be collected. On the other hand a composite sample may be collected during weekend. These systematically occurring missing values (gaps) will have an effect on the uncertainties of the measurements. Another type of missing values is random missing values. These random gaps are caused, for example, by instrument failures. Pierre Gy's sampling theory includes tools to evaluate all error components that are involved in sampling of heterogeneous materials. Variograms, introduced by Gy's sampling theory, have been developed to estimate the uncertainty of auto-correlated process measurements. Variographic experiments are utilized for estimating the variance for different sample selection strategies. The different sample selection strategies are random sampling, stratified random sampling and systematic sampling. In this paper both systematic and random gaps were estimated by using simulations and real process data. These process data were taken from bark boilers of pulp and paper mills (combustion processes). When systematic gaps were examined a linear interpolation was utilized. Also cases introducing composite sampling were studied. Aims of this paper are: (1) how reliable the variogram is to estimate the process variogram calculated from data with systematic gaps, (2) how the uncertainty of missing gap can be estimated in reporting time-averages of auto-correlated time series measurements. The results show that when systematic gaps were filled by linear interpolation only minor changes in the values of variogram were observed. The differences between the variograms were constantly smallest with composite samples. While estimating the effect of random gaps, the results show that for the non-periodic processes the stratified random sampling strategy gives more reliable results than systematic sampling strategy. Therefore stratified random sampling should be used while estimating the uncertainty of random gaps in reporting time-averages of auto-correlated time series measurements.     

Multivariate statistical analysis of a multi-step industrial processes

Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors. This approach will show how the process develops from a data point of view. The procedure is illustrated on a relatively simple industrial batch process, but it is also applicable in a general context, where knowledge about the variables is available.     

The active site of cellobiohydrolase Cel6A from Trichoderma reesei: the roles of aspartic acids D221 and D175

Trichoderma reesei cellobiohydrolase Cel6A is an inverting glycosidase. Structural studies have established that the tunnel-shaped active site of Cel6A contains two aspartic acids, D221 and D175, that are close to the glycosidic oxygen of the scissile bond and at hydrogen-bonding distance from each other. Here, site-directed mutagenesis, X-ray crystallography, and enzyme kinetic studies have been used to confirm the role of residue D221 as the catalytic acid. D175 is shown to affect protonation of D221 and to contribute to the electrostatic stabilization of the partial positive charge in the transition state. Structural and modeling studies suggest that the single-displacement mechanism of Cel6A may not directly involve a catalytic base. The value of (D2O)(V) of 1.16 +/- 0.14 for hydrolysis of cellotriose suggests that the large direct effect expected for proton transfer from the nucleophilic water through a water chain (Grotthus mechanism) is offset by an inverse effect arising from reversibly breaking the short, tight hydrogen bond between D221 and D175 before catalysis.