Coffee aroma—Statistical analysis of compositional data

Solid-phase microextraction in headspace mode coupled with gas chromatography-mass spectrometry was applied to the determination of volatile compounds in 30 commercially available coffee samples. In order to differentiate and characterize Arabica and Robusta coffee, six major volatile compounds (acetic acid, 2-methylpyrazine, furfural, 2-furfuryl alcohol, 2,6-dimethylpyrazine, 5-methylfurfural) were chosen as the most relevant markers. Cluster analysis and principal component analysis (PCA) were applied to the raw chromatographic data and data processed by centred logratio transformation.     

Statistical analysis of thermoanalytical experimental data

A statistical technique based on the Wilcockson criterion is suggested for estimation of the reproducibility of thermoanalytical experiments. Reduction of the whole physicochemical process to a quasi-one-stage process is described.This work was carried out partially with the aid of International Science Foundation (Programme Students and Post-Graduate Students).     

Analytical artifacts, sample handling and preservation methods of environmental samples of synthetic pyrethroids

Sample handling and preservation methods of environmental samples of synthetic pyrethroids (SPs) are very important and must be controlled to maintain sample integrity during analytical determinations. However, published literature has treated this issue only partly, and, in many instances, with contradictory conclusions.The tendency of SPs to adsorb - to varying degrees under different conditions - to surfaces and solid particulates with which they come in contact may be responsible for this situation. It has become evident that SPs discharged to water bodies are present mainly in the adsorbed state, and that affects their bioavailability and the reliability of analytical results. Refrigeration and storage in the dark are prerequisites for stabilization of SPs in environmental samples.Several other factors that contribute to SPs instability include:(1) matrix composition;(2) container material; and,(3) sample acidity.Sample agitation prior to analysis may be useful to reduce losses due to adsorption. There are several chemical reagents that inhibit the degradative processes of SPs, but the efficiency of preservation depends - to a large extent - on the characteristics of sample matrix.This article reviews various aspects related to preservation of SPs and puts forward a preliminary guideline for proper practice during sampling, storage and sample preparation of SPs.     

Multiple factor analysis in environmental chemistry

In environmental chemistry studies, it may be necessary to analyze data sets constituted by different blocks of variables, possibly of different types, measured on the same samples. Multiple factor analysis (MFA) is presented as a tool for exploring such data. The most important features of MFA are shown on a real environmental data set, consisting of two blocks of data, namely heavy metals and polycyclic aromatic hydrocarbons, measured for sediment samples. They are discussed and compared to principal component analysis (PCA). The usefulness of the weighting scheme used in MFA as a preprocessing step for other chemometric methods, such as clustering, is also highlighted.     

Recovering incomplete data using Statistical Multiple Imputations (SMI): a case study in environmental chemistry

This paper presents a statistical technique that can be applied to environmental chemistry data where missing values and limit of detection levels prevent the application of statistics. A working example is taken from an environmental leaching study that was set up to determine if there were significant differences in levels of leached arsenic (As), chromium (Cr) and copper (Cu) between lysimeters containing preservative treated wood waste and those containing untreated wood. Fourteen lysimeters were setup and left in natural conditions for 21 weeks. The resultant leachate was analysed by ICP-OES to determine the As, Cr and Cu concentrations. However, due to the variation inherent in each lysimeter combined with the limits of detection offered by ICP-OES, the collected quantitative data was somewhat incomplete. Initial data analysis was hampered by the number of ‘missing values’ in the data. To recover the dataset, the statistical tool of Statistical Multiple Imputation (SMI) was applied, and the data was re-analysed successfully. It was demonstrated that using SMI did not affect the variance in the data, but facilitated analysis of the complete dataset.     

The role of thermal analysis in environmental protection

In the past industry has developed chemicals and products, optimized for the best suitable properties concerning different application fields. Now, ideas of environmental precaution are arising, on the one hand looking for reduction of materials flow to avoid or minimize the waste, on the other hand following the idea of process and product integrated environmental protection. That means to develop processes and products which are safer and more tolerant regarding the environment and its organisms.In this connection thermal analysis is a very successful tool for predicting the risk of burning processes or fire accidents. Thermal analysis in this context means the classical thermal analysis methods like DTA, TG, DSC and its couplings with gas analysis methods as well as the simulation of burning processes in different kind of furnaces with identification and quantification of the evolved gases.     

Explorative data analysis of two-dimensional electrophoresis gels

Methods for classification of two-dimensional (2-DE) electrophoresis gels based on multivariate data analysis are demonstrated. Two-dimensional gels of ten wheat varieties are analyzed and it is demonstrated how to classify the wheat varieties in two qualities and a method for initial screening of gels is presented. First, an approach is demonstrated in which no prior knowledge of the separated proteins is used. Alignment of the gels followed by a simple transformation of data makes it possible to analyze the gels in an automated explorative manner by principal component analysis, to determine if the gels should be further analyzed. A more detailed approach is done by analyzing spot volume lists by principal components analysis and partial least square regression. The use of spot volume data offers a mean to investigate the spot pattern and link the classified protein patterns to distinct spots on the gels for further investigation. The explorative approach in analysis of 2-D gels makes it possible, in a fast and convenient way, to screen many gels in order to determine the protein patterns that form clusters and could be selected for further examination.     

Recommendations for reporting thermal analysis data

Recommendations for reporting of thermal analysis data relating to differential thermal analysis, differential scanning calorimetry, thermogravimetry and thermomechanical analysis were developed some two decades ago. Since that time there have been significant changes in the techniques, as well as a greater understanding of the effect of experimental variables on the results obtained from thermonalytical experiments. This paper reports on a preliminary review of the Recommendations by the Task Group established by the ICTAC Committee on Standardization to undertake their revision. Particular attention has been paid to the properties of the sample; control of the instrument variables; and the data acquisition and manipulation by computers.     

Gravitation-driven stress-reduced cell handling

We present a simple lab-on-chip device for handling small samples of delicate cells, e.g. stem cells. It uses a combination of sedimentation and dielectrophoresis. The transport of cells is driven by gravitation. Dielectrophoresis uses radio-frequency electric fields for generating particle-selective forces dependent on size and polarisability. Electrodes along the channels hold particles and/or cells in a defined position and deflect them towards different outlets. The absence of external pumping and the integration of injection and sampling ports allow the processing of tiny sample volumes. Various functions are demonstrated, such as contact-free cell trapping and cell/particle sorting. Pairs of human cells and antibody-coated beads, as they are formed for T cell activation, are separated from unbound beads. The cells experience only low stress levels compared with the stress levels in dielectrophoresis systems, where transport depends on external pumping. Our device is a versatile yet simple tool that finds applications in cellular biotechnology, in particular when an economic solution is required. Figure A simple gravitation-driven lab-on-chip device for the separation of mixed populations of microparticles or cells by negative dielectrophoresis.     

Immunosorbents: A new tool for pesticide sample handling in environmental analysis

 Immunoaffinity techniques have been widely used for the determination of different analytes in the medical field. However the use of antibodies immobilized in an appropriate support material to preconcentrate pesticides from environmental samples is only recent. The production of antibodies, election of supports, antibody immobilization procedures, elution of analytes from immunosorbents and the more recent applications in the field of pesticide analysis are reviewed. The present review concludes that immunosorbents have great potential and discusses the present limitations and expected future trends. Received: 29 July 1996 / Accepted: 14 August 1996     

SPME in environmental analysis

Recent advances in the use of solid-phase microextraction (SPME) in environmental analysis, including fiber coatings, derivatization techniques, and in-tube SPME, are reviewed in this article. Several calibration methods for SPME, including traditional calibration methods, the equilibrium extraction method, the exhaustive extraction method, and several diffusion-based calibration methods, are presented. Recent developed SPME devices for on-site sampling and several applications of SPME in environmental analysis are also introduced.      

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.     

Membrane protein and peptide sample handling for MS analysis using a structured MALDI target

Different sample handling methods for hydrophobic proteins and peptides were evaluated in association with the utilization of a structured matrix-assisted laser/desorption ionization (MALDI) target for increased sensitivity. The fluorinated organic solvent hexafluoroisopropanol (HFIP) was used for the solubilization of both the full-length protein bacteriorhodopsin (BR) and a cyanogen bromide digest thereof, and compared to the performance of the non-ionic detergents octyl--d-glucopyranoside (OG), dodecyl--d-maltoside (DM), and Triton X-100. A concentrating effect was seen when using the structured MALDI plate for BR dissolved in all the different detergents, of which OG generated the best-quality spectra for the full-length integral membrane protein as well as for the hydrophobic peptides. However, the uneven analyte distribution obtained with the detergent preparations required selective and thus time-consuming acquisition of spectra. When instead HFIP was used as sample solvent, a tenfold increase in sensitivity was achieved for full-length BR. Addition of acids to the HFIP-solubilized sample, or to the MALDI matrix solution, improved the signals for a few of the peptides, while degrading the spectra of others. Consequently, the addition of acid could be used as a complementary sample preparation method for hydrophobic peptides. On-target washing to remove contaminants (e.g., salt) was performed, and a recrystallization protocol for signal improvement specifically suited for hydrophobic peptides is described. Results from digestion and solubilization in different micro centrifuge tubes were examined to determine the influence of different materials on the possible sample loss due to wall adhesion. Studies of sample solution storage times suggest immediate analysis after solubilization to obtain best results.     

An automatic data acquisition and handling system for activation analysis

An automatic measurement and data handling system for activation analysis is described. The system comprises five automatic -spectrometers with sample changers, a central computer with four workstations and two communication networks, one to connect the gamma-spectrometers with the central computer and another to provide communication from the workstations to the central computer. The software comprises programs for control of the -spectrometers, communication between the different units, data handling and storage, interpretation of -spectra, calculation of element concentrations and creating reports.     

Statistical quality control of PCDD/PCDF analysis using a total measuring data set

 A random error and accuracy estimation approach for polychlorinated dibenzo-p-dioxins (PCDD) and dibenzofurans (PCDF) analysis is presented based on statistical treatment of all the data set registered in a run or in a bunch of uniform runs. Received: 18 August 1998 / Accepted: 21 October 1998     

Statistical design of experiments as a tool in mass spectrometry

This Tutorial is an introduction to statistical design of experiments (DOE) with focus on demonstration of how DOE can be useful to the mass spectrometrist. In contrast with the commonly used one factor at a time approach, DOE methods address the issue of interaction of variables and are generally more efficient. The complex problem of optimizing data-dependent acquisition parameters in a bottom-up proteomics LC-MS/MS analysis is used as an example of the power of the technique. Using DOE, a new data-dependent method was developed that improved the quantity of confidently identified peptides from rat serum.     

Topological data analysis: A promising big data exploration tool in biology,analytical chemistry and physical chemistry

An important feature of experimental science is that data of various kinds is being produced at an unprecedented rate. This is mainly due to the development of new instrumental concepts and experimental methodologies. It is also clear that the nature of acquired data is significantly different. Indeed in every areas of science, data take the form of always bigger tables, where all but a few of the columns (i.e. variables) turn out to be irrelevant to the questions of interest, and further that we do not necessary know which coordinates are the interesting ones. Big data in our lab of biology, analytical chemistry or physical chemistry is a future that might be closer than any of us suppose. It is in this sense that new tools have to be developed in order to explore and valorize such data sets. Topological data analysis (TDA) is one of these. It was developed recently by topologists who discovered that topological concept could be useful for data analysis. The main objective of this paper is to answer the question why topology is well suited for the analysis of big data set in many areas and even more efficient than conventional data analysis methods. Raman analysis of single bacteria should be providing a good opportunity to demonstrate the potential of TDA for the exploration of various spectroscopic data sets considering different experimental conditions (with high noise level, with/without spectral preprocessing, with wavelength shift, with different spectral resolution, with missing data).     

Statistical experimental design for bioprocess modeling and optimization analysis

The statistical design of experiments (DOE) is a collection of predetermined settings of the process variables of interest, which provides an efficient procedure for planning experiments. Experiments on biological processes typically produce long sequences of successive observations on each experimental unit (plant, animal, bioreactor, fermenter, or flask) in response to several treatments (combination of factors). Cell culture and other biotech-related experiments used to be performed by repeated-measures method of experimental design coupled with different levels of several process factors to investigate dynamic biological process. Data collected from this design can be analyzed by several kinds of general linear model (GLM) statistical methods such as multivariate analysis of variance (MANOVA), univariate ANOVA (timesplit-plot analysis with randomization restriction), and analysis of orthogonal polynomial contrasts of repeated factor (linear coefficient analysis). Last, regression model was introduced to describe responses over time to the different treatments along with model residual analysis. Statistical analysis of biprocess with repeated measurements can help investigate environmental factors and effects affecting physiological and bioprocesses in analyzing and optimizing biotechnology production.     

Applications of microfluidic systems in environmental analysis

During the past few decades, rapid growth in the applications of microfluidic systems to environmental analysis or environmentally related species has been observed. This review presents and critically discusses the published literature on the applications of microfluidic systems to real environmental samples or samples simulating environmental conditions. The subjects covered include integrated pretreatments, separation modes, detection methods and monitoring sensors used in these platforms. The main challenges in this field and the author’s perspectives on future directions for environmental analysis based on microfluidic systems are also proposed.     

Comparative studies of methylmercury determination in biological and environmental samples

Some parameters affecting the accuracy of various approaches to methylmercury (MeHg) determination in biological and environmental samples were studied. Different isolation techniques (ion-exchange, extraction, volatilization, distillation) and final measurement via cold vapour atomic absorption spectroscopy (CV AA) or gas chromatography (GC) were compared. Results obtained by the various isolation techniques are comparable for almost all biological and environmental samples, except for soils and some sediments, where disagreement between the results obtained by GC and CV AA was found. In order to resolve these problems, a new separation technique based on distillation of MeHg from the sample followed either by CV AA or GC was developed. The new method results in very good recovery and reproducibility (95 ± 2%) for all samples examined (fish, mussel, shrimp, blood, hair, algae, sediment, etc.), is specific for MeHg and provides for its differentiation from other species by an indirect CV AA determination. Gas-chromatographic measurement of the isolated MeHg using different packings and conditioning of the columns is also discussed. The distillation method with GC detection is advantageous in producing cleaner chromatograms and in prolonging the life-time of the packing and the intervals between reconditioning.