This review focuses on the recent applications of QCLs in mid-IR spectroscopy of clinically relevant samples.

This work was sparked by the reported identification of man-made cellulosic fibers (rayon/viscose) in the marine environment as a major fraction of plastic litter by Fourier transform infrared (FT-IR) transmission spectroscopy and library search. To assess the plausibility of such findings, both natural and man-made fibers were examined using FT-IR spectroscopy. Spectra acquired by transmission microscopy, attenuated total reflection (ATR) microscopy, and ATR spectroscopy were compared. Library search was employed and results show significant differences in the identification rate depending on the acquisition method of the spectra. Careful selection of search parameters and the choice of spectra acquisition method were found to be essential for optimization of the library search results. When using transmission spectra of fibers and ATR libraries it was not possible to differentiate between man-made and natural fibers. Successful differentiation of natural and man-made cellulosic fibers has been achieved for FT-IR spectra acquired by ATR microscopy and ATR spectroscopy, and application of ATR libraries. As an alternative, chemometric methods such as unsupervised hierarchical cluster analysis, principal component analysis, and partial least squares-discriminant analysis were employed to facilitate identification based on intrinsic relationships of sample spectra and successful discrimination of the fiber type could be achieved. Differences in the ATR spectra depending on the internal reflection element (Ge versus diamond) were observed as expected; however, these did not impair correct classification by chemometric analysis. Moreover, the effects of different levels of humidity on the IR spectra of natural and man-made fibers were investigated, too. It has been found that drying and re-humidification leads to intensity changes of absorption bands of the carbohydrate backbone, but does not impair the identification of the fiber type by library search or cluster analysis.

Mid-infrared spectroscopy is a sensitive and selective technique for probing molecules in the gas or liquid phase. Investigating chemical reactions in bio-medical applications such as drug production is recently gaining particular interest. However, monitoring dynamic processes in liquids is commonly limited to bulky systems and thus requires time-consuming offline analytics. In this work, we show a next-generation, fully-integrated and robust chip-scale sensor for online measurements of molecule dynamics in a liquid solution. Our fingertip-sized device utilizes quantum cascade technology, combining the emitter, sensing section and detector on a single chip. This enables real-time measurements probing only microliter amounts of analyte in an in situ configuration. We demonstrate time-resolved device operation by analyzing temperature-induced conformational changes of the model protein bovine serum albumin in heavy water. Quantitative measurements reveal excellent performance characteristics in terms of sensor linearity, wide coverage of concentrations, extending from 0.075 mg ml⁻¹ to 92 mg ml⁻¹ and a 55-times higher absorbance than state-of-the-art bulky and offline reference systems.

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.