Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-011-0350-z) contains supplementary material, which is available to authorized users.

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With its excellent automation capability and localized energy input enabling precise, reproducible welds, laser beam welding represents a preferred industrial joining technology. Electro-mobility drastically increases the need for defect-free and automatable copper joining technologies. However, copper welds that are produced with state-of-the-art infrared lasers often suffer from spattering and porosity. Recent publications show distinct improvements using novel beam sources at visible wavelengths, attributing them to increased absorptivity. Nevertheless, this cannot fully explain the steadier process behavior. This wavelength-dependent process stability has not yet been investigated sufficiently. Therefore, we have developed a predictive material-dependent criterion indicating process stability based on the example of copper heat-conduction spot welding. For this purpose, we combined energy balances with thermo-physical material properties, taking into account the wavelength and temperature dependence of the optical properties. This paper presents the key mechanism that we identified as decisive for process stability. The criterion revealed that X-points (unique, material-specific wavelengths) represent critical stability indicators. Our calculations agree very well with experimental results on copper, steel and aluminum using two different wavelengths and demonstrate the decisive, material-dependent wavelength impact on process stability. This knowledge will help guide manufacturers and users to choose and develop beam sources that are tailored to the material being processed.

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