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A microfluidic high-resolution NMR flow probe based on a novel stripline detector chip is demonstrated. This tool is invaluable for the in situ monitoring of reactions performed in microreactors. As an example, the acetylation of benzyl alcohol with acetyl chloride was monitored. Because of the uncompromised (sub-Hz) resolution, this probe holds great promise for metabolomics studies, as shown by an analysis of 600 nL of human cerebrospinal fluid.

The predominant means to detect nuclear magnetic resonance (NMR) is to monitor the voltage induced in a radiofrequency coil by the precessing magnetization. To address the sensitivity of NMR for mass-limited samples it is worthwhile to miniaturize this detector coil. Although making smaller coils seems a trivial step, the challenges in the design of microcoil probeheads are to get the highest possible sensitivity while maintaining high resolution and keeping the versatility to apply all known NMR experiments. This means that the coils have to be optimized for a given sample geometry, circuit losses should be avoided, susceptibility broadening due to probe materials has to be minimized, and finally the B(1)-fields generated by the rf coils should be homogeneous over the sample volume. This contribution compares three designs that have been miniaturized for NMR detection: solenoid coils, flat helical coils, and the novel stripline and microslot designs. So far most emphasis in microcoil research was in liquid-state NMR. This contribution gives an overview of the state of the art of microcoil solid-state NMR by reviewing literature data and showing the latest results in the development of static and micro magic angle spinning (microMAS) solenoid-based probeheads. Besides their mass sensitivity, microcoils can also generate tremendously high rf fields which are very useful in various solid-state NMR experiments. The benefits of the stripline geometry for studying thin films are shown. This geometry also proves to be a superior solution for microfluidic NMR implementations in terms of sensitivity and resolution.

The selection of optimal preprocessing is among the main bottlenecks in chemometric data analysis. Preprocessing currently is a burden, since a multitude of different preprocessing methods is available for, e.g., baseline correction, smoothing, and alignment, but it is not clear beforehand which method(s) should be used for which data set. The process of preprocessing selection is often limited to trial-and-error and is therefore considered somewhat subjective. In this paper, we present a novel, simple, and effective approach for preprocessing selection. The defining feature of this approach is a design of experiments. On the basis of the design, model performance of a few well-chosen preprocessing methods, and combinations thereof (called strategies) is evaluated. Interpretation of the main effects and interactions subsequently enables the selection of an optimal preprocessing strategy. The presented approach is applied to eight different spectroscopic data sets, covering both calibration and classification challenges. We show that the approach is able to select a preprocessing strategy which improves model performance by at least 50% compared to the raw data; in most cases, it leads to a strategy very close to the true optimum. Our approach makes preprocessing selection fast, insightful, and objective.

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