Metal–organic frameworks (MOFs) are crystalline porous materials composed of metal ions or clusters coordinated with organic linkers. Due to their extraordinary properties such as high porosity with homogeneous and tunable in size pores/cages, as well as high thermal and chemical stability, MOFs have gained attention in diverse analytical applications. MOFs have been coupled with a wide variety of extraction techniques including solid-phase extraction (SPE), dispersive solid-phase extraction (d-SPE), and magnetic solid-phase extraction (MSPE) for the extraction and preconcentration of metal ions from complex matrices. The low concentration levels of metal ions in real samples including food samples, environmental samples, and biological samples, as well as the increased number of potentially interfering ions, make the determination of trace levels of metal ions still challenging. A wide variety of MOF materials have been employed for the extraction of metals from sample matrices prior to their determination with spectrometric techniques.

A new method for on-line monitoring of fermentations using mid-infrared (MIR) spectroscopy has been developed. The method has been used to predict the concentrations of glucose and ethanol during a baker's yeast fermentations. A completely automated flow system was employed as an interface between the bioprocess under study and the Fourier transform infrared (FT-IR) spectrometer, which was equipped with a flow cell housing a diamond attenuated total reflection (ATR) element. By using the automated flow system, experimental problems related to adherence of CO ₂ bubbles to the ATR surface, as well as formation of biofilms on the ATR surface, could be efficiently eliminated. Gas bubbles were removed during sampling, and by using rinsing steps any biofilm could be removed from the ATR surface. In this way, constant measuring conditions could be guaranteed throughout prolonged fermentation times (∼8 h). As a reference method, high-performance liquid chromatography (HPLC) with refractive index detection was used. The recorded data from different fermentations were modeled by partial least-squares (PLS) regression comparing two different strategies for the calibration. On the one hand, calibration sets were constructed from spectra recorded from either synthetic standards or from samples drawn during fermentation. On the other hand, spectra from fermentation samples and synthetic standards were combined to form a calibration set. Differences in the kinetics of the studied fermentation processes used for calibration and prediction, as well as the precision of the HPLC reference method, were identified as the main chemometric sources of error. The optimal PLS regression method was obtained using the mixed calibration set of samples from fermentations and synthetic standards. The root mean square errors of prediction in this case were 0.267 and 0.336 g/L for glucose and ethanol concentration, respectively.

Graphene oxide (GO) is a chemical compound with a form similar to graphene that consists of one-atom-thick two-dimensional layers of sp²-bonded carbon. Graphene oxide exhibits high hydrophilicity and dispersibility. Thus, it is difficult to be separated from aqueous solutions. Therefore, functionalization with magnetic nanoparticles is performed in order to prepare a magnetic GO nanocomposite that combines the sufficient adsorption capacity of graphene oxide and the convenience of magnetic separation. Moreover, the magnetic material can be further functionalized with different groups to prevent aggregation and extends its potential application. Until today, a plethora of magnetic GO hybrid materials have been synthesized and successfully employed for the magnetic solid-phase extraction of organic compounds from environmental, agricultural, biological, and food samples. The developed GO nanocomposites exhibit satisfactory stability in aqueous solutions, as well as sufficient surface area. Thus, they are considered as an alternative to conventional sorbents by enriching the analytical toolbox for the analysis of trace organic compounds.