Mid-IR imaging combined with machine learning is a powerful combination for non-destructive, label free chemical imaging. Key applications include computational staining and tissue classification. These applications are enabled by information rich mid-IR hyperspectral images and reliable ground truth data. As novel, nano-scale spatial resolution mid-IR spectroscopy techniques are finding broader use we realize that ground truth datasets will be needed at the nano-scale as well. Here, we propose image fusion and registration of nano-scale images as a generic approach for establishing such datasets. We demonstrate the viability of this approach for imaging the sub-cellular distribution of proteins and specific enzymes. Furthermore, we demonstrate that image registration of AFM-IR spectral data is a key step in processing AFM-IR chemical imaging data in general.
Enantiomeric excess, the ratio between two enantiomers, is an important process variable in chiral catalysis. To increase the efficiency of such processes, this parameter needs to be monitored as close to real time as possible and ideally without elaborate sample preparation. Vibrational circular dichroism (VCD) provides chiral information in a pretreatment free and nondestructive manner. Since classical VCD suffers from a low time resolution, quantum cascade laser (QCL) based VCD was introduced to enable studying more dynamic processes. This significantly improved time resolution enables the use of EC-QCL VCD for monitoring the change of EE e.g. in a chemical process or a chemical reaction. In such applications, the classical approach of human interpretation of individual VCD spectra is no longer reasonable. Hence, chemometric evaluation of VCD spectral datasets is required. In this work we compare accuracy and stability of common multivariate regression algorithms for predicting EE from EC-QCL VCD spectra. Besides classical partial-least-squares regression, modified multiple linear regressions and models derived from chemical knowledge were investigated. We found that a combination of introducing chemical knowledge via spectral descriptor and a reduction of multicollinearity by a ridge regression model resulted in the most stable prediction. Additionally, least absolute shrinkage and selection operator (Lasso) revealed a potential for sensor design involving dedicated QCL arrays focused on a few relevant wavelengths. In summary, a more comprehensive chemometric perspective on QCL-VCD spectra can yield improvements in predictive performance and the shorter measurement times provided by QCL-VCD aid in acquiring datasets of appropriate size.
Photothermal Spectroscopy (PTS) is an indirect analytical technique in which the optical signal is directly proportional to the laser emission intensity. This direct dependence on the laser power means that - in contrast to more conventional transmission-absorption techniques - PTS fully benefits from the high power of novel tunable mid-infrared laser sources such as Quantum Cascade Lasers (QCLs). In particular, QCLs equipped with an external cavity (EC) allow broad tunability which can be exploited in the detection of liquids identified by broad absorption bands. To achieve high sensitivity in PTS it is also important to choose a sensitive mode of transducing photothermal signal. Among the PTS transduction techniques photothermal interferometry (i.e. the detection of the phase change resulting from sample heating) stands out due to its high sensitivity. In this work, we use an EC-QCL in a photothermal interferometry PTS setup for trace water detection. We employ a HeNe laser-based Mach-Zehnder Interferometer (MZI) with liquid flow-cells inserted in the two arms. An EC-QCL emitting in the range of 1570-1730 cm-1 is arranged co-linear to the analyte arm of the interferometer and used to target the bending mode (𝜈2 ~ 1645 cm-1) of water molecules in different matrices. Highest linearity and sensitivity are ensured by locking the MZI at its quadrature point via an active-feedback loop. Fluctuations and drifts are further minimized by means of temperature stabilization. When benchmarking the system against commercial FTIR spectrometers it is shown to be in excellent agreement with regards to band shapes, band positions and relative intensities and to compare favorably in terms of sensitivity. Achieved limits of detection (LODs) for water in chloroform and jet-fuel are in the low ppm range. Higher LODs orders of magnitude were obtained indeed for the case of water in ethanol. An analysis of the matrix influence on the PTS signal’s strength has been carried out. Results show how the choice of the matrix dramatically influences limits of detection and limits of quantification (LOQs).
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