We report a mid-IR transmission setup for the analysis of the protein amide I and amide II band in aqueous solutions that achieves a limit of detection as low as 0.0025 mg mL –1 (outperforming our previous results and other state-of-the-art mid-IR-based techniques by almost an order of magnitude). This large improvement is made possible by combining the latest-generation external cavity-quantum cascade laser (EC-QCL) operated at room temperature with an optimized double-beam optical setup that adjusts the path length (26 μm) to ensure robust sample handling. For minimizing the noise introduced by the high-intensity laser light source, a thermoelectrically cooled mercury cadmium telluride balanced detection module was employed. In this way, noise levels better by a factor of up to 20 were achieved compared with single-channel measurements. Characteristic spectral features of proteins with different secondary structures were successfully identified at concentrations as low as 0.1 mg mL –1 . Furthermore, a highly linear response was demonstrated for concentrations between 0.05 and 10 mg mL –1 . The total acquisition time of the setup can be adapted to fulfill the required sensitivity of the protein measurements and to ensure maximum flexibility for future applications. The presented setup combines high sensitivity, large optical path lengths, and short measurement times and thus outperforms previous research type EC-QCL setups as well as commercially available instruments. This opens a wide range of future applications including protein–ligand interaction studies as well as qualitative and quantitative analyses of proteins in complex matrices such as those found in up- and downstream bioprocess monitoring and similar challenging applications which can not be readily met by conventional FT-IR spectroscopy.

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In the present study, a novel approach for mid-infrared (IR)-based prediction of bovine milk fatty acid composition is introduced. A rapid, solvent-free, two-step centrifugation method was applied in order to obtain representative milk fat fractions. IR spectra of pure milk lipids were recorded with attenuated total reflection Fourier-transform infrared (ATR-FT-IR) spectroscopy. Comparison to the IR transmission spectra of whole milk revealed a higher amount of significant spectral information for fatty acid analysis. Partial least squares (PLS) regression models were calculated to relate the IR spectra to gas chromatography/mass spectrometry (GC/MS) reference values, providing particularly good predictions for fatty acid sum parameters as well as for the following individual fatty acids: C10:0 (R2P = 0.99), C12:0 (R2P = 0.97), C14:0 (R2P = 0.88), C16:0 (R2P = 0.81), C18:0 (R2P = 0.93), and C18:1cis (R2P = 0.95). The IR wavenumber ranges for the individual regression models were optimized and validated by calculation of the PLS selectivity ratio. Based on a set of 45 milk samples, the obtained PLS figures of merit are significantly better than those reported in literature using whole milk transmission spectra and larger datasets. In this context, direct IR measurement of the milk fat fraction inherently eliminates covariation structures between fatty acids and total fat content, which poses a common problem in IR-based milk fat profiling. The combination of solvent-free lipid separation and ATR-FT-IR spectroscopy represents a novel approach for fast fatty acid prediction, with the potential for high-throughput application in routine lab operation.

In this study, an external cavity-quantum cascade laser-based mid-infrared (IR) spectrometer was applied for in-line monitoring of proteins from preparative ion-exchange chromatography. The large optical path length of 25 μm allowed for robust spectra acquisition in the broad tuning range between 1350 and 1750 cm –1 , covering the most important spectral region for protein secondary structure determination. A significant challenge was caused by the overlapping mid-IR bands of proteins and changes in the background absorption of water due to the NaCl gradient. Implementation of advanced background compensation strategies resulted in high-quality protein spectra in three different model case studies. In Case I, a reference blank run was directly subtracted from a sample run with the same NaCl gradient. Case II and III included sample runs with different gradient profiles than the one from the reference run. Here, a novel compensation approach based on a reference spectra matrix was introduced, where the signal from the conductivity detector was employed for correlating suitable reference spectra for correction of the sample run spectra. With this method, a single blank run was sufficient to correct various gradient profiles. The obtained IR spectra of hemoglobin and β-lactoglobulin were compared to off-line reference measurements, showing excellent agreement for all case studies. Moreover, the concentration values obtained from the mid-IR spectrometer agreed well with conventional UV detectors and high-performance liquid chromatography off-line measurements. LC–QCL–IR coupling thus holds high potential for replacing laborious and time-consuming off-line methods for protein monitoring in complex downstream processes.

This study introduces the first mid-infrared (IR)–based method for determining the fatty acid composition of human milk. A representative milk lipid fraction was obtained by applying a rapid and solvent-free two-step centrifugation method. Attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was applied to record absorbance spectra of pure milk fat. The obtained spectra were compared to whole human milk transmission spectra, revealing the significantly higher degree of fatty acid–related spectral features in ATR FT-IR spectra. Partial least squares (PLS)–based multivariate regression equations were established by relating ATR FT-IR spectra to fatty acid reference concentrations, obtained with gas chromatography–mass spectrometry (GC-MS). Good predictions were achieved for the most important fatty acid sum parameters: saturated fatty acids (SAT, R2CV = 0.94), monounsaturated fatty acids (MONO, R2CV = 0.85), polyunsaturated fatty acids (PUFA, R2CV = 0.87), unsaturated fatty acids (UNSAT, R2CV = 0.91), short-chain fatty acids (SCFA, R2CV = 0.79), medium-chain fatty acids (MCFA, R2CV = 0.97), and long-chain fatty acids (LCFA, R2CV = 0.88). The PLS selectivity ratio (SR) was calculated in order to optimize and verify each individual calibration model. All mid-IR regions with high SR could be assigned to absorbances from fatty acids, indicating high validity of the obtained models.