FTIR spectroscopic characterization of differently cultivated food related yeasts.

The application of Fourier Transform Infrared Spectroscopy for characterization of yeasts is growing rapidly. Since it is known that the phenotypic expression of yeast cells depends sensitively on the nutrients that are available in the growth medium, one standardized growth medium is usually used for identification and characterization purposes in order to obtain reproducible FTIR signals. Since our recently developed high-throughput micro-cultivation protocol has the capacity to use more than one standardized growth medium, we wanted to investigate if the parallel use of multiple growth media can improve identification results. For this purpose, five different cultivation media (YP, YPD, YMB, SAB and SD) were used. In total 91 food spoilage yeast strains of 12 different genera were cultivated in different cultivation media and subsequently characterized by FTIR spectroscopy. For spectral identifications, Radial Basis Function-Partial Least Squares (RBF-PLS) was used in combination with cross-model validation where an inner cross-validation loop was used to optimize the model, while in an outer loop an independent test set was kept aside to test the optimized model. Sensitivity and specificity were evaluated for each studied genus class. The results show that the YMB selective medium gave the best discrimination results for 9 of the 12 genera with sensitivity above 90%. Only three genera showed better identification results on other media (Clavispora and Metschnikowia on medium SD, Debaryomyces on medium YPD). We therefore suggest to use the media SD, YPD in combination with the YMB medium for the identification of food spoilage yeasts.

Near-infrared reflectance spectroscopy and multivariate calibration techniques applied to modelling the crude protein, fibre and fat content in rapeseed meal.

Near-infrared reflectance spectroscopy (NIRS) is often applied when a rapid quantification of major components in feed is required. This technique is preferred over the other analytical techniques due to the relatively few requirements concerning sample preparations, high efficiency and low costs of the analysis. In this study, NIRS was used to control the content of crude protein, fat and fibre in extracted rapeseed meal which was produced in the local industrial crushing plant. For modelling the NIR data, the partial least squares approach (PLS) was used. The satisfactory prediction errors were equal to 1.12, 0.13 and 0.45 (expressed in percentages referring to dry mass) for crude protein, fat and fibre content, respectively. To point out the key spectral regions which are important for modelling, uninformative variable elimination PLS, PLS with jackknife-based variable elimination, PLS with bootstrap-based variable elimination and the orthogonal partial least squares approach were compared for the data studied. They enabled an easier interpretation of the calibration models in terms of absorption bands and led to similar predictions for test samples compared to the initial models.

No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry.

Hyphenated techniques such as capillary electrophoresis-mass spectrometry (CE-MS) or high-performance liquid chromatography with diode array detection (HPLC-DAD), etc., are known to produce a huge amount of data since each sample is characterized by a two-way data table. In this paper different ways of obtaining sample-related information from a set of such tables are discussed. Working with original data requires alignment techniques due to time shifts caused by unavoidable variations in separation conditions. Other pre-processing techniques have been suggested to facilitate comparison among samples without prior peak alignment, for example, 'binning' and/or 'blurring' the data along the time dimension. All these techniques, however, require optimization of some parameters, and in this paper an alternative parameter-free method is proposed. The individual data tables (X) are represented as Gram matrices (XXT), where the summation is taken over the time dimension. Hence the possible variations in time scale are eliminated, while the time information is at least partly preserved by the correlation structure between the detection channels. For comparison among samples, a similarity matrix is constructed and explored by principal component analysis and hierarchical clustering. The Gram matrix approach was tested and compared to some other methods using 'binned' and 'blurred' data for a data set with CE-MS runs on urine samples. In addition to data exploration by principal component analysis and hierarchical clustering, a discriminant partial least squares model was constructed to discriminate between the samples that were taken with and without the prior intake of a drug. The result showed that the proposed method is at least as good as the others with respect to cluster identification and class prediction. A distinct advantage is that there is no need for parameter optimization, while a potential drawback is the large size of the Gram matrices for data with high mass resolution.

Raman spectroscopy as a process analytical technology (PAT) tool for the in-line monitoring and understanding of a powder blending process.

The aim of this study is to propose a strategy to implement a PAT system in the blending step of pharmaceutical production processes. It was examined whether Raman spectroscopy can be used as PAT tool for the in-line and real-time endpoint monitoring and understanding of a powder blending process. A screening design was used to identify and understand the significant effects of two process variables (blending speed and loading of the blender) and of a formulation variable (concentration of active pharmaceutical ingredient (API): diltiazem hydrochloride) upon the required blending time (response variable). Interactions between the variables were investigated as well. A Soft Independent Modelling of Class Analogy (SIMCA) model was developed to determine the homogeneity of the blends in-line and real-time using Raman spectroscopy in combination with a fiber optical immersion probe. One blending experiment was monitored using Raman and NIR spectroscopy simultaneously. This was done to verify whether two independent monitoring tools can confirm each other's endpoint conclusions. The analysis of the experimental design results showed that the measured endpoints were excessively rounded due to the large measurement intervals relative to the first blending times. This resulted in effects and critical effects which cannot be interpreted properly. To be able to study the effects properly, the ratio between the blending times and the measurement intervals should be sufficiently high. In this study, it anyway was demonstrated that Raman spectroscopy is a suitable PAT tool for the endpoint control of a powder blending process. Raman spectroscopy not only allowed in-line and real-time monitoring of the blend homogeneity, but also helped to understand the process better in combination with experimental design. Furthermore, the correctness of the Raman endpoint conclusions was demonstrated for one process by using a second independent endpoint monitoring tool (NIR spectroscopy). Hence, the use of two independent techniques for the control of one response variable not only means a mutual confirmation of both methods, but also provides a higher certainty in the determined endpoint.

Target selection for alignment of chromatographic signals obtained using monochannel detectors.

The alignment of instrumental signals, such as chromatograms, is regarded as an important step before applying multivariate chemometric techniques for data analysis. Nowadays, many alignment techniques are available and they differ in achieving their goal. They can correct peak shifts in a set of chromatograms with differing degrees of success. Almost all alignment techniques, with few exceptions [e.g., W. Yu, B. Wu, N. Lin, K. Stone, K. Williams, H. Zhao, Comput. Biol. Chem. 30 (2006) 27], require a careful choice of the target profile. The selection of a target signal is not an easy task and some difficulties related to this selection are discussed in this paper. An analysis of several simulated sets of chromatographic signals showed that the target selection can be a crucial step if the aligned signals are then used as input to unsupervised approaches, such as, e.g., principal component analysis and to supervised methods like discriminant partial least squares. Different proposals for target selection known-to-date are reviewed. As demonstrated in our study the target profile with the highest correlation coefficient among all the signals studied gave the most satisfactory results.

Robust partial least squares model for prediction of green tea antioxidant capacity from chromatograms.

In this paper a robust version of the partial least squares model (partial robust M-regression, PRM) was built to predict the total antioxidant capacity of green tea extracts. In order to construct a calibration model, chromatograms obtained by a fast high-performance liquid chromatographic method on a monolithic silica column were related with the total antioxidant capacity of green tea extracts as determined by the Trolox antioxidant capacity method. Since natural samples are the subject of the study, some outlying samples are present in the data, as shown in an earlier work. Therefore, to construct reliable calibration models, they were detected and removed prior to modeling. With the applied robust partial least squares approach, where a weighting scheme is embedded to down-weight the negative influence of outliers upon the model it is possible to construct a robust calibration model, without prior identification of outlying objects. It was shown that a robust model, allowing satisfactory prediction for test samples, can be used in controlling green tea antioxidant capacity based on their chromatograms. The constructed robust partial least squares model was shown to have virtually the same fit and predictive power as the classical partial least squares model when outlying samples were removed from the data.

Start-to-end processing of two-dimensional gel electrophoretic images.

Gel electrophoresis serves as a basic analytical tool in the proteomic studies. However, processing of gel electrophoretic images is still the main bottleneck of data analysis, and there is an increasing need for the fully automated approaches. The proposed start-to-end strategy of analyzing the gel images consists of chemometric tools, which allow their effective preprocessing, automatic warping, and data modeling. The image preprocessing techniques: denoising in the wavelet domain and the penalized asymmetric least squares approach for the background estimation are proposed. Matching of images is based on fuzzy warping of features, extracted from the gel images. For the classification or calibration purpose, multivariate approaches such, as partial least squares (PLS) or kernel-PLS methods are used. Performance of the proposed strategy is demonstrated on the real set of the two-dimensional gel images.

Behavioral effects of occupational exposure to organophosphorous pesticides in female greenhouse planting workers.

51 women employed in gardening enterprises were studied. Of these, 26 performed planting jobs in greenhouses and were occupationally exposed to several organophosphates. The comparison group consisted of 25 women not exposed to neurotoxic chemicals. The groups were similar in terms of age, education, place of habitation, and intake of stimulants and drugs. Exposure determinations were performed during the period when pesticides were intensively used in the greenhouses (March-June). Exposure measurements included air pollution, contamination of skin and clothes, and work timing. The level of total exposure in the planting worker group was low. Psychological examinations were conducted twice: before and after the spraying season, and the Neurobehavioral Core Test Battery recommended by the WHO was administered to all subjects. The results of the psychological tests did not reveal effects of exposure after a single spraying season. Analysis of the results, however, indicated differences between the exposed and control groups on both occasions. The exposed female workers were characterized by longer reaction times and reduced motor steadiness compared to the unexposed workers. In addition, increased tension, greater depression and fatigue, more frequent symptoms of CNS disturbances were observed in the exposed women compared to the controls.

Lessons learned--15 years of the WHO-NCTB: a review.

Based on expert recommendations, the World Health Organization (WHO) recommended the Neurobehavioral Core Test Battery (NCTB) in 1983 to detect neurotoxicity in world-wide populations. The NCTB consists of 7 neurobehavioral tests (Digit Symbol, Digit Span, Benton visual memory test/recognition form, Santa Ana dexterity test, Simple Reaction Time, Pursuit Aiming II, and Profile of Mood States). Research with the NCTB provides the context for the results of a mini-symposium held in 1999 to discuss the lessons learned about using the NCTB in working populations. Speakers from Africa, Asia, Europe, and North America reviewed data from 94 studies using the NCTB, including 63 from China, 13 from Korea, 11 from Poland, three from South Africa, three from the USA, and one from Ecuador. Although a consensus was not sought in the symposium, the key lessons learned that emerged from the various presentations, were: * The NCTB is effective in testing adults with 12 or more years of formal education, but can not reliably test persons with less than 9 years of education. * People from cultures very different from those in Europe and North America (eg, aboriginal and African cultures) may not be tested effectively by the NCTB, although others such as at least some Asian populations, can be. To address this problem, the construct validity of the NCTB should be established in a wide range of countries. * The POMS measures of emotion proved to be very sensitive to neurotoxic exposures in several countries, but the POMS was the NCTB test most affected by cultural differences. The Digit Symbol or the Milan modification of that test was also highly sensitive to neurotoxic exposures. * Examiner drift following training to administer the NCTB is a significant problem in technically trained Examiners. Pursuit Aiming II is very difficult to score reliably.

Principal component analysis of dissolution data with missing elements.

The use of principal component analysis (PCA) for incomplete dissolution data sets is examined. The PC space is constructed using a reference set and the test set is projected in that space. Several cases such as a reference set with missing data, an incomplete test set and both sets measured at different time points, are discussed using two examples: one simulation and one obtained from the pharmaceutical practice. From the many possibilities to deal with missing data, the expectation-maximization algorithm in combination with PCA was chosen. The influence on the similarity or f2 factor is examined too. The sampling with replacement or bootstrap technique, which can be used to obtain confidence limits, can also be used when missing data are present in one of the data sets.

Use of mass spectrometry for assessing similarity/diversity of natural products with unknown chemical structures.

An evaluation whether mass spectral data contain useful information for assessing similarity/diversity of drug compounds is presented. A comparative study was carried out between Ward's hierarchical agglomerative clustering, based on the 2D Daylight fingerprints or on the mass spectra, of a small database of 66 synthetic substances. The influence of normalization of the mass spectral data on the clustering result has also been studied. The results were subsequently compared with an expert's classification of the same small dataset, based on own evaluation according to known structure and pharmacological activity.

Exploratory analysis of data sets with missing elements and outliers.

The main goal of the presented paper was to develop a general strategy allowing exploration of contaminated data sets with missing elements, based on application of robust PLS for initial estimation of missing elements. Using robust distance, the outlying elements were identified. After their identification and replacing by missing elements, the expectation-maximization algorithm (which can be built in into different computational procedures, such as principal component analysis and its generalisation to the N-way data-the TUCKER3 model) was used for construction of the final model.

Self-organizing neural networks for modeling 3D QSAR--a comparative study.

Different architectures of self-organizing neural networks (SOM) have been used for modeling 3D QSAR. The atomic coordinates and partial atomic charges were used as input signals. In particular, the steroids complexing corticosteroid binding globulin (CBG) that are used as a benchmark measuring the performance of drug design methods have been applied to compare between individual methods. The sensitivity of the different architectures for changes of the alignments of the molecules within series, as well as the possibility for alignments based on the molecular inertial axes have been tested.

[Neuropsychologic effects of occupational exposure to manganese]. / Neuropsychologiczne skutki narazenia zawodowego na mangan.

A broad use of manganese and its compounds in industry generates adverse health effects in workers exposed. When setting environmental exposure criteria, changes in the central nervous system are considered as a critical health effect. The author describes these changes and reviews the latest reports on empirical studies employing psychological methods in detecting preclinical neurotoxic effects of manganese. Results of neuropsychological studies carried out in different countries and in different occupational groups exposed to rather low manganese concentrations evidence the range of changes in the function of the nervous system.

[Mental evaluation after chronic exposure to tetrachloroethylene]. / Ocena psychicznych skutków przewleklej ekspozycji na czterochloroetylen.

With the aim of determining the effects of chronic occupational exposure to Per upon the selected intellectual and psychomotor functions two groups of workers have been examined. The proper group was that of chemical laundry workers exposed to Per (concentrations within TLVs and above). The control group was composed of knitting plant workers nonexposed to chemical substances. The findings point to significant differences between the test groups, consisting in an extension of the time of response to homogeneous and complex light stimuli (psychomotor function) and in a decrease in the ability to see and reflect the visual material (intellectual function) in the Per-exposed group.

[Psychological evaluation of the effects of chronic occupational exposure of paint shop workers to the mixture of organic solvents]. / Psychologiczna ocena skutków przewleklego narazenia zawodowego lakierników pracujacych w stycznosci z mieszanina rozpuszczalników organicznych.

The aim of the study was to evaluate the psychological effects of chronic occupational exposure to the mixture of organic solvents containing xylene, toluene, n-butanol, butyl acetate, n-butyl acetate, ethyl acetate and cyclohexanone defined by MAC values. Thirty-four paint shop workers and 34 controls, matched according to sex, age, education and the type of work performed, were examined. The Neurobehavioral Core Test Battery (NCTB) was used in the study. In the exposed workers, a longer simple reaction time was observed, as well as reduced manual skills and impaired precision of the hand movements.

[Neurobehavioral effects of chronic occupational exposure to mixtures of solvents used in furniture varnishing. Part I--Psychological examination]. / Neurobehawioralne skutki przewleklego narazenia zawodowego na mieszaniny rozpuszczalników stosowanych w lakierowaniu mebli. Czesc I.--Badania psychologiczne.

The study aimed at detecting psychological effects of chronic occupational exposure to the mixtures of solvents used in furniture varnishing. 36 exposed workers and 34 controls selected so as to account for age, education level and the nature work were examined. The following tests were used in the examinations: the Neurobehavioral Core Test Battery, Orebro Symptom Questionnaire, Perceived Stress Scale. A slow-down of perceptive and motor activities, which intensified with the duration of exposure, was the only observed adverse effect of the exposure. The workers employed for periods longer than 15 years more often reported symptoms of psychic function disturbances and deterioration of their health condition.

[Ability to work--new approach to evaluation methods]. / Zdolnosc do pracy--nowe podejscie do sposobu oceny.

In Poland, the assessment of work ability has thus far been almost entirely objective, which means that it was based on the evaluation of the individual's health state. That is why a subjective method of work ability assessment with work ability index (WAI), developed by the Occupational Health Institute in Helsinki, was used in our present study. This method allows to indicate other possible factors which modify work ability. The study covered 189 men employed in five metallurgical plants, located in the region of Lódz. In the study population, WAI and work load on the basis of the expenditure of energy were measured, the health condition was evaluated, and information on life styles and non-occupational responsibilities was gathered. It was found that WAI values were inversely proportional to age and work load. They were also modified by individual characteristics, such as life style, body mass, and activities at leisure. It was found that the correlation between the general index of work ability and the objective health indicators was low with the concomitant considerably higher correlation with the values of the components which reflect subjective work abilities. In view of the results obtained, WAI can be recommended as a tool for assessing work ability. Due to this method it is possible to present conclusively all elements of individual characteristics and to identify at the same time links with working conditions.

What can go wrong at the data normalization step for identification of biomarkers?

Our study focuses on the removal of the so-called size effect, related to a different sample volume and/or concentration. This effect is associated with many types of instrumental signals, particularly with those originating from HPLC-DAD, LC-MS, and UPLC-MS. These signals do not carry any absolute information about the sample components. If the data comparison has to be performed based on sample fingerprints, then the size effect is undesired, and the shape effect is of main interest. With "shape", we refer to data information which is contained in the ratios between the variables. So far, different normalization methods have been applied to the removal of size effect. In our study, the performance of popular normalization methods is compared with those of the CODA (Compositional Data Analysis) methods, relying on log-ratio transformations, and the performance is evaluated through the prism of proper identification of biomarkers.

High-dimensional nested analysis of variance to assess the effect of production season, quality grade and steam pasteurization on the phenolic composition of fermented rooibos herbal tea.

A nested analysis of variance combined with simultaneous component analysis, ASCA, was proposed to model high-dimensional chromatographic data. The data were obtained from an experiment designed to investigate the effect of production season, quality grade and post-production processing (steam pasteurization) on the phenolic content of the infusion of the popular herbal tea, rooibos, at 'cup-of-tea' strength. Specifically, a four-way analysis of variance where the experimental design involves nesting in two of the three crossed factors was considered. For the purpose of the study, batches of fermented rooibos plant material were sampled from each of four quality grades during three production seasons (2009, 2010 and 2011) and a sub-sample of each batch was steam-pasteurized. The phenolic content of each rooibos infusion was characterized by high performance liquid chromatography (HPLC)-diode array detection (DAD). In contrast to previous studies, the complete HPLC-DAD signals were used in the chemometric analysis in order to take into account the entire phenolic profile. All factors had a significant effect on the phenolic content of a 'cup-of-tea' strength rooibos infusion. In particular, infusions prepared from the grade A (highest quality) samples contained a higher content of almost all phenolic compounds than the lower quality plant material. The variations of the content of isoorientin and orientin in the different quality grade infusions over production seasons are larger than the variations in the content of aspalathin and quercetin-3-O-robinobioside. Ferulic acid can be used as an indicator of the quality of rooibos tea as its content generally decreases with increasing tea quality. Steam pasteurization decreased the content of the majority of phenolic compounds in a 'cup-of-tea' strength rooibos infusion.