Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.

Pioneered by Clark’s microelectrode more than half a century ago, there has been substantial interest in developing new, miniaturized optical methods to detect molecular oxygen inside cells. While extensively used for animal tissue measurements, applications of intracellular optical oxygen biosensors are still scarce in plant science. A critical aspect is the strong autofluorescence of the green plant tissue that interferes with optical signals of commonly used oxygen probes. A recently developed dual-frequency phase modulation technique can overcome this limitation, offering new perspectives for plant research. This review gives an overview on the latest optical sensing techniques and methods based on phosphorescence quenching in diverse tissues and discusses the potential pitfalls for applications in plants. The most promising oxygen sensitive probes are reviewed plus different oxygen sensing structures ranging from micro-optodes to soluble nanoparticles. Moreover, the applicability of using heterologously expressed oxygen binding proteins and fluorescent proteins to determine changes in the cellular oxygen concentration are discussed as potential non-invasive cellular oxygen reporters.

The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples.

On the basis of absorption measurements in the near-infrared (NIR) spectral range, a new method for the quantification of the ethanol content of beer is presented. Instead of the multivariate calibration models most commonly employed in NIR spectroscopic works, we use interpretive difference spectroscopy: Two wavelengths are selected according to the assignment of the absorption bands of the main substances of content of beer in the NIR region, and the difference between the absorbances at these wavelengths is used for ethanol quantification. Absorption spectra of the dominating beer ingredients are discussed and the calibration procedure with ethanol/water mixtures is shown. Robustness against the carbohydrate content of beer samples was demonstrated by analyzing solutions of ethanol and maltose in water. Validation of the method was performed with various beer samples with an ethanol concentration range between 0.5 and 7.7 vol %. The pertinent advantage of the procedure developed in this work is the indication that the results are independent from seasonal variations of the ingredients, which is of high interest for products with natural ingredients such as beer.