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MALDI time-of-flight mass spectrometry (MALDI-TOF MS) has become a widely used tool for the classification of biological samples. The complex chemical composition of pollen grains leads to highly specific, fingerprint-like mass spectra, with respect to the pollen species. Beyond the species-specific composition, the variances in pollen chemistry can be hierarchically structured, including the level of different populations, of environmental conditions or different genotypes. We demonstrate here the sensitivity of MALDI-TOF MS regarding the adaption of the chemical composition of three Poaceae (grass) pollen for different populations of parent plants by analyzing the mass spectra with partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA). Thereby, variances in species, population and specific growth conditions of the plants were observed simultaneously. In particular, the chemical pattern revealed by the MALDI spectra enabled discrimination of the different populations of one species. Specifically, the role of environmental changes and their effect on the pollen chemistry of three different grass species is discussed. Analysis of the Group formation within the respective populations showed a varying influence of plant genotype on the classification, depending on the species, and permits conclusions regarding the respective rigidity or plasticity towards environmental changes.
The characterization of technical lignins is a key step for the efficient use and processing of this material into valuable chemicals and for quality control. In this study 31 lignin samples were prepared from different biomass sources (hardwood, softwood, straw, grass) and different pulping processes (sulfite, Kraft, organosolv). Each lignin was analysed by attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy. Statistical analysis of the ATR-FT-IR spectra by means of principal component analysis (PCA) showed significant differences between the lignins. Hence, the samples can be separated by PCA according to the original biomass. The differences observed in the ATR-FT-IR spectra result primarily from the relative ratios of the p-hydroxyphenyl, guaiacyl and syringyl units. Only limited influence of the pulping process is reflected by the spectral data. The spectra do not differ between samples processed by Kraft or organosolv processes. Lignosulfonates are clearly distinguishable by ATR-FT-IR from the other samples. For the classification a model was created using the k-nearest neighbor (k NN) algorithm. Different data pretreatment steps were compared for k=1…20. For validation purposes, a 5-fold cross-validation was chosen and the different quality criteria Accuracy (Acc), Error Rate (Err), Sensitivity (TPR) and specificity (TNR) were introduced. The optimized model for k=4 gives values for Acc = 98.9 %, Err = 1.1 %, TPR = 99.2 % and TNR = 99.6 %.
Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.
Identifying nanomaterials (NMs) according to European Union Legislation is challenging, as there is an enormous variety of materials, with different physico-chemical properties. The NanoDefiner Framework and its Decision Support Flow Scheme (DSFS) allow choosing the optimal method to measure the particle size distribution by matching the material properties and the performance of the particular measurement techniques. The DSFS leads to a reliable and economic decision whether a material is an NM or not based on scientific criteria and respecting regulatory requirements. The DSFS starts beyond regulatory requirements by identifying non-NMs by a proxy Approach based on their volume-specific surface area. In a second step, it identifies NMs. The DSFS is tested on real-world materials and is implemented in an e-tool. The DSFS is compared with a decision flowchart of the European Commission’s (EC) Joint Research Centre (JRC), which rigorously follows the explicit criteria of the EC NM definition with the focus on identifying NMs, and non-NMs are identified by exclusion. The two approaches build on the same scientific basis and measurement methods, but start from opposite ends: the JRC Flowchart starts by identifying NMs, whereas the NanoDefiner Framework first identifies non-NMs.
Evaluation of electron microscopy techniques for the purpose of classification of nanomaterials
(2016)
Electron microscopy techniques such as TEM, STEM, SEM or TSEM (transmission in SEM) are capable of assessing the size of individual nanoparticles accurately. Nevertheless, the challenging aspect is sample preparation from powder or liquid form on the substrate, so that a
homogeneous distribution of well-separated (deagglomerated) particles is attained. The systematic study in this work shows examples where the extraction of the critical, smallest particle dimension - as the decisive particle parameter for the classification as a NM - is possible by analysing the sample after ist simple, dry preparation. The consequences of additional typical issues like loss of information due to screening of smaller particles by larger ones or the (in)ability to access the constituent particles in aggregates are discussed.
In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macrocopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project.
The safety characteristics of flammable gases and liquids are required when identifying potentially explosive mixtures and taking appropriate actions concerning explosion protection. Examples are given here of the safe handling and evaluation of hazards during the processing, storage, transport, and disposal of flammable liquids and gases. The CHEMSAFE database is presented as a reliable source of safety characteristic data, and its new open-access version is introduced. CHEMSAFE currently contains assessed properties for about 3000 liquids, gases and mixtures. The lack of a broad experimental foundation in the extensive field of non-atmospheric conditions shows the need for further investigation and standardization. This article summarizes experimental evidence and estimation methods for safety characteristic data under non-atmospheric conditions pointing out current limitations. Suggestions for pre-normative research on safety data under non-atmospheric conditions are given.
Bewertung von Oberflächenschäden auf dekorativen Beschichtungen mit digitaler Farbbildanalyse
(2002)
Für das Qualitätsmanagement der Lackhersteller wurde ein Verfahren entwickelt, das eine objektive Beschreibung des dekorativen Effektes freibewitterter Beschichtungen mit relevanten Messgrößen ermöglicht. Die Maßzahlen über den Farbkontrast bzw. Farbkontrastgradienten der Ungleichmäßigkeiten sowie über die Geometrie erkennbarer Strukturen werden mit Methoden digitaler Farbbildbearbeitung bzw. -analyse ermittelt. Der Einsatz dieser Technik erforderte die systematische Untersuchung und analytische Beschreibung der gerätespezifischen Farbtransformationseigenschaften ausgewählter konventioneller digitaler Ein- und Ausgabemedien (Farbmonitore und Flachbettscanner). In eine kommerzielle Farbbildanalysesoftware wurde ein Farbmanagementsystem implementiert, das auf den bereits ermittelten mathematischen Modellen basiert. Zur Bewertung der Beschichtungsschäden sind geeignete Klassifikatoren auszuarbeiten, die eine möglichst hohe Korrelation mit subjektiven Beurteilungsergebnissen visueller Abmusterungen durch Experten aufweisen. Visuelle Abmusterungen und Bewertungen von 150 ausgewählten bewitterten Beschichtungen durch Fachpersonal dienten zur quantitativen Untersuchung dieser Zusammenhänge.
A method for the quality management of paint producers was developed that allows for an objective description of inhomogeneous fading of paint coatings after free weathering using relevant metric quantities such as color contrast, gradient of color contrast, and geometric features of the inhomogeneous structures. These may be quantified with the method of digital color image analysis. The first step to apply this technique means a systematic investigation of the color transformation properties specific of the selected input/output devices (CRT display, flatbed scanner) used for digital imaging. To build a color management system mathematical models of the color transformation processes were optimized and embedded in a commercial color image analysis software. The needed metric parameters, that evaluate the damages on the coated surfaces, must be deduced for highest possible agreement with visual categorical judgements of the damages by experts. 150 samples of paint coatings after weathering were selected to investigate this correlation.
The composition of concrete determines its resistance to various degradation mechanisms such as ingress of ions, carbonation or reinforcement corrosion. Knowledge of the composition of the hardened concrete is therefore helpful to assess the remaining service life of an existing structure or evaluate the damage observed during inspections. For example, for most existing concrete structures the type of cement originally used is not known and must therefore be determined afterwards. This paper presents a preliminary study on the application of laser-induced breakdown spectroscopy (LIBS) to identify the type of cement. For this purpose, ten different types of cement were investigated. For every type, three cement paste prisms were produced: (i) prisms dried, ground and pressed into tablets, (ii) prisms dried and (iii) prisms untreated. LIBS measurements were performed with a diode-pumped low energy laser (1064 nm, 3 mJ, 1.5 ns, 100 Hz) in combination with two compact spectrometers which cover the UV and NIR spectral range. A reduced subset of spectral features was used to build a classification model based on linear discriminant analysis. The results show that the classification of homogenized pressed cement powder samples provides a high accuracy, however, factors such as a different sample matrix and moisture content can affect the accuracy of the classification. The study demonstrates that LIBS is a promising tool to identify the type of cement.