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As a solid energy carrier, biomass generally has a few disadvantages, which limits its use for coal replacement and as a feedstock for entrained flow gasification. The hydrophilic and fibrous nature, the low calorific value and low bulk energy content imply high accumulated costs in the whole supply chain and severe challenges in more advanced conversion systems. By thermally pretreating the biomass by torrefaction, these properties may be significantly improved. A continuous torrefaction rotary drum reactor was designed, constructed and evaluated to enable an accurate process control and allow a homogeneous well-defined high quality product to be produced. The combined effects of torrefaction temperature (260–310 °C) and residence time (8–25 min) on a large number of product properties (> 25) were determined for Norway spruce. The resulting mass and energy yields were 46–97% and 62–99%, respectively. Exothermic reactions were evident both at low (260 °C) and high temperatures (310 °C) but with no thermal runaway observed. Increased torrefaction severity resulted in decreased milling energy consumption, angle of repose, mass and energy yield, content of volatile matter, hydrogen, cellulose and hemicellulose. Hydrophobicity, heating value, carbon and fixed carbon contents increased. For all responses, the effect of torrefaction temperature was larger than the effect of residence time. Substantial interaction effects were present for mass and energy yields, volatile matter and hydrogen content. Another correlation found was the relationship of hemicellulose degradation and the brittleness of the torrefied product. Data also suggest secondary char forming reactions during the torrefaction process, resulting in higher fixed carbon content in the torrefied material than expected. The results also suggest torrefaction temperature and residence time not to be totally interchangeable.

Visible and near infrared imaging techniques for analysing characteristics of torrefied biomass were evaluated for possible use in future online process control. The goal of such a control system is to identify products with the desired properties and reject products outside the specification. Two pushbroom hyperspectral cameras with different wavelength regions and a commercial digital colour camera were evaluated. The hyperspectrat cameras, short wave infrared (SWIR) and visible-near infrared (VNIR), covered the ranges of 1000-2500 nm and 400-1000 nm, respectively. The biomass was produced according to an experimental design in a torrefaction pilot plant at different temperatures, residence times, and nitrogen and steam flow rates to obtain a wide range of different characteristics and qualities of torrefied material. Chemical characteristics, heating values and milling energy of the different torrefied materials were analysed or calculated using standardized procedures and were used for calibration. For the hyperspectral images, a principal-component analysis was performed on the absorbance spectra. The score plots and score images were used interactively to separate background, outlier pixels and shading effects from sample signal. Averaged spectra of individual torrefied woodchips were used. Partial least-squares regression was used to relate average spectra to heating values and chemical characteristics of the torrefied biomass. Owing to the small size of the data sets, cross-validation using leave-one-out validation was used for testing the models. The ratio of standard error of prediction to sample standard deviation (RPD) values were used for comparing the imaging techniques. For ROB images, all RPD values were 4 or lower. The RPD values for the VNIR technique were all below 5, while the SWIR images produced RPD values above 5 for eight of the 13 properties. The promising results of the SWIR technique strongly suggested that the torrefied biomass undergoes changes to chemical structures, which are not necessarily manifested as changes to the colour of the material.

This study investigates the effect of imbalanced spectral data in the training set, when developing partial least squares discriminant analysis (PLS-DA) classification models for use in future predictions. The experimental study was performed using a real hyperspectral short-wavelength infrared image data set collected from bakery products (buns) containing contaminants (flies) but similar applications for other insects, paper and plastic were also tested. The contaminants represent a very small proportion of the images relative to the bun. The PLS-DA model aims at accurately detecting and classifying the contaminants and this requires a modification of the calibration data set. The paper deals with problems caused by unbalanced calibration data sets and how to remedy them. In the example it was demonstrated that, by balancing the calibration data from 58,476 bun pixels + 279 fly pixels to 279 bun + 279 fly pixels, the number of true predictions could be improved with a smaller number of PLS components used in the model. The improvement for flies increased from 65% true predictions with ten PLS components to >99% true prediction with five to six PLS components. The true prediction for bun went from 100% to 99.5% with six PLS components which is an acceptable reduction. Theoretical explanations are included.

2D (13)C-(1)H HSQC NMR spectroscopy of acetylated cell walls in solution gives a detailed fingerprint that can be used to assess the chemical composition of the complete wall without extensive degradation. We demonstrate how multivariate analysis of such spectra can be used to visualize cell wall changes between sample types as high-resolution 2D NMR loading spectra. Changes in composition and structure for both lignin and polysaccharides can subsequently be interpreted on a molecular level. The multivariate approach alleviates problems associated with peak picking of overlapping peaks, and it allows the deduction of the relative importance of each peak for sample discrimination. As a first proof of concept, we compare Populus tension wood to normal wood. All well established differences in cellulose, hemicellulose, and lignin compositions between these wood types were readily detected, confirming the reliability of the multivariate approach. In a second example, wood from transgenic Populus modified in their degree of pectin methylesterification was compared to that of wild-type trees. We show that differences in both lignin and polysaccharide composition that are difficult to detect with traditional spectral analysis and that could not be a priori predicted were revealed by the multivariate approach. 2D NMR of dissolved cell wall samples combined with multivariate analysis constitutes a novel approach in cell wall analysis and provides a new tool that will benefit cell wall research.

Correct identification of the origins of herbal medical products is becoming increasingly important in tandem with the growing interest in alternative medicine. However, visual inspection of raw material is still the most widely used method, and newer scientific approaches are needed. To develop a more objective and efficient tool for discriminating herbal origins, particularly Korean and Chinese, we employed a nuclear magnetic resonance (NMR)-based metabolomics approach combined with an orthogonal projections to latent structure-discriminant analysis (OPLS-DA) multivariate analysis. We first analyzed the constituent metabolites of Scutellaria baicalensis through NMR studies. Subsequent holistic data analysis with OPLS-DA yielded a statistical model that could cleanly discriminate between the sample groups even in the presence of large structured noise. An analysis of the statistical total correlation spectroscopy (STOCSY) spectrum identified citric acid and arginine as the key discriminating metabolites for Korean and Chinese samples. As a validation of the discrimination model, we performed blind prediction tests of sample origins using an external test set. Our model correctly predicted the origins of all of the 11 test samples, demonstrating its robustness. We tested the wider applicability of the developed method with three additional herbal medicines from Korea and China and obtained very high prediction accuracy. The solid discriminatory power and statistical validity of our method suggest its general applicability for determining the origins of herbal medicines.

Wood cells, unlike most other cells in plants, grow by a unique combination of intrusive and symplastic growth. Fibers grow in diameter by diffuse symplastic growth, but they elongate solely by intrusive apical growth penetrating the pectin-rich middle lamella that cements neighboring cells together. In contrast, vessel elements grow in diameter by a combination of intrusive and symplastic growth. We demonstrate that an abundant pectin methyl esterase (PME, EC 3.1.1.11) from wood-forming tissues of hybrid aspen (Populus tremula L. x tremuloides Michx.) acts as a negative regulator of both symplastic and intrusive growth of developing wood cells. When PttPME1expression was up- and down-regulated in transgenic aspen trees, the PME activity in wood-forming tissues was correspondingly altered. PME removes methyl ester groups from homogalacturonan, and the transgenic trees had modified homogalacturonan methylesterification patterns, as demonstrated by two-dimensional NMR and immunostaining using PAM1 and LM7 antibodies. The in situ distributions of PAM1 and LM7 epitopes revealed changes in pectin methylesterification in the transgenic trees that were specifically localized in expanding wood cells. The results show that en-block de-esterification of homogalacturonan by PttPME1 inhibits both symplastic growth and intrusive growth. PttPME1 is therefore involved in mechanisms determining fiber width and length in the wood of aspen trees.

Multivariate analysis on spectroscopic 1H NMR data is well established in metabolomics and other fields where the composition of complex samples is studied. However, biomarker identification can be hampered by overlapping resonances. 2D NMR data provides a more detailed “fingerprint” of the chemical structure and composition of the sample with greatly improved spectral resolution compared to 1H NMR data. In this report, we demonstrate a procedure for the construction of multivariate models based on frequency domain 2D NMR data where the loadings can be visualized as highly informative 2D loading spectra. This method is based on the analysis of raw spectral data without any need for peak picking or integration prior to analysis. Spectral features such as line widths and peak positions are thus retained. Hence, the loadings can be visualized and interpreted on a molecular level as pseudo 2D spectra in order to identify potential biomarkers. To demonstrate this strategy we have analyzed HSQC spectra acquired from populus phloem plant extracts originating from a set of designed experiments with OPLS regression.

Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.

During the last two decades, a number of methods have been developed and evaluated for selecting the optimal number of components in a PLS model. In this paper, a new method is introduced that is based on a randomization test. The advantage of using a randomization test is that in contrast to cross validation (CV), it requires no exclusion of data, thus avoiding problems related to data exclusion, for example in designed experiments. The method is tested using simulated data sets for which the true dimensionality is clearly defined and also compared to regularly used methods for 10 real data sets. The randomization test works as a good statistical selection tool in combination with other selection rules. It also works as an indicator when the data require a pre-treatment.

In our society in the 21^st century one of the greatest challenges is to provide raw materials to the industry in a sustainable way. This requires increased use of renewable raw materials such as wood. Wood is widely used in pulp, paper and textile industries and ongoing research efforts aim to extend the use of wood in e.g. liquid biofuels and green chemicals. At Umeå Plant Science Center (UPSC) poplar trees are used as model systems to study wood formation. The objective is to understand the function of the genes underlying the wood forming process. This knowledge could result in improved chemical and physical wood properties suitable for different industrial processes. This will in turn meet the demands for a sustainable development.

This thesis presents tools and strategies to unravel information regarding genetic perturbations in poplar trees by the use of nuclear magnetic resonance (NMR) spectroscopy and multivariate analysis (MVA). Furthermore, gas chromatography/mass spectrometry (GC/MS) is briefly discussed in this context. Multivariate methods to find patterns and trends in NMR data have been used for more than 30 years. In the beginning MVA was applied in pattern recognition studies in order to characterize chemical structures with different ligands and in different solvents. Today, the multivariate methods have developed and the research have changed focus towards the study of biofluids from plant extracts, urine, blood plasma, saliva etc. NMR spectra of biofluids can contain thousands of resonances, originating from hundreds of different compounds. This type of complex data can be hard to summarize and interpret without appropriate tools and require sophisticated strategies for data evaluation. Related fields of research are rapidly growing and are here referred to as metabolomics.

Five different research projects are presented which includes analysis of poplar samples where macromolecules such as pectin and also small molecules such as metabolites were analysed by high resolution magic angle spinning (HR/MAS) NMR spectroscopy, ¹H-¹³C HSQC NMR and GC/MS. The discussion topics include modelling of metabolomic time dependencies in combination with genetic variation, validation of orthogonal projections to latent structures (OPLS) models, selection of putative biomarkers related to genetic modification from OPLS-discriminant analysis (DA) models, measuring one of the main components found in the primary cell-walls of poplar i.e. pectin, the use of Fourier transformed two-dimensional (2D) NMR data in OPLS modelling and model complexity in a PLS model.