Mid-infrared Laser Spectroscopy Research Articles

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Mid-infrared Laser Spectroscopy of Jet-Cooled Formic Acid Trimer: Mode-Dependent Line Broadening in the C-O Stretching Region.

Building on recent progress in the vibrational spectroscopy of the formic acid trimer, we present the first high-resolution measurements of the jet-cooled laser absorption spectrum of (HCOOH)3. The spectra of the lowest- and highest-frequency C-O stretching fundamentals are analyzed whereas the third band is not observed, complicated by monomer and dimer absorptions at 1219 cm-1 (8.2 μm). Vibration-rotation parameters are obtained for the band at 1172.31512(68) cm-1 whereas the C-O stretch at 1246.33(5) cm-1 exhibits a significantly larger breadth, allowing only resolution of the coarse PQR structure. Vibrational predissociation can be ruled out, and intramolecular vibrational redistribution mechanisms are discussed, particularly coupling to the concerted proton exchange within the cyclic dimer subunit. Ultimately, the question remains open. The prospects of high-resolution measurements of other trimer bands or isotope substitution experiments, which might assist in revealing the mode-specificity of the underlying broadening mechanisms, are discussed.

HONO and N2O4 Measurement Method Based on Mid-Infrared Quantum Cascade Laser Spectroscopy

HONO and N2O4 Measurement Method Based on Mid-Infrared Quantum Cascade Laser Spectroscopy

Characterizing centrosymmetric two-ring PAHs using jet-cooled high resolution mid-infrared laser spectroscopy and anharmonic quantum chemical calculations.

The presence of Polycyclic Aromatic Hydrocarbon (PAH) molecules in the interstellar medium, recently confirmed by the detection of cyano-naphthalenes, has renewed the interest of extensive spectroscopic and physical-chemistry studies on such large species. The present study reports the jet-cooled rovibrational infrared study of three centrosymmetric two-ring PAH molecules, viz., naphthalene (C10H8), [1,5] naphthyridine (C8H6N2), and biphenyl (C12H10), in the in-plane ring C-H bending (975-1035cm-1) and C-C ring stretching (1580-1620cm-1) regions. For the two most rigid PAHs, the accuracy of spectroscopic parameters derived in ground and several excited states (six for naphthalene and six for [1,5] naphthyridine) has significantly improved the literature values. In addition, comparison between experiments and quantum chemical calculations confirms the predictive power of the corrected calculated rotational parameters. The more flexible structure of biphenyl makes the analysis of high resolution jet-cooled spectra of ν19 and ν23 modes recorded at about 1601 and 1013cm-1, respectively, particularly challenging. The presence of three torsional vibrations below 120cm-1 together with small values of the rotational constants prevented us from determining the ground and v19 = 1 excited rotational constants independently. In the ν23 band region, the presence of two bands rotationally resolved and separated by only 0.8cm-1 raises the question of possible splittings due to a large amplitude motion, most probably the torsion of the aliphatic bond between the two phenyl rings.

Non-Destructive Measuring Systems for the Evaluation of High Oxygen Stored Poultry: Development of Headspace Gas Composition, Sensory and Microbiological Spoilage.

As poultry is known to be a perishable food, the use-by date is set in such a way that food safety is guaranteed even with a higher initial bacterial count. This means, however, that some products are wasted, even if they are still safe to eat. Therefore, non-destructive measurement devices might be a good opportunity for individual shelf-life prediction, e.g., in retail. The aim of this study was therefore to use non-destructive measurement devices based on fluorescence quenching (oxygen detection) and mid-infrared laser spectroscopy (carbon dioxide detection) for the monitoring of high-oxygen-packed poultry in different storage conditions. During 15 days of storage, the gas composition of the headspace was assessed (non-destructively and destructively), while total plate count was monitored and a comprehensive sensory evaluation was performed by a trained panel. We were able to demonstrate that in most cases, non-destructive devices have comparable precision to destructive devices. For both storage conditions, the sensory attribute slime was correlated with reaching the critical microbiological value of 107 CFU/g; the attribute buttery was also useful for the prediction of regularly stored poultry. The change in the gas atmosphere as a sign of premature spoilage, however, was only possible for samples stored in irregular conditions.

Compound specific stable carbon isotope analysis of aromatic organic contaminants in water using gas chromatography coupled to mid-infrared laser spectroscopy

A mid-infrared laser based analytical approach is newly developed in this study for gas chromatography (GC) separation and carbon compound specific isotope analysis (CSIA) of aromatic organic pollutants in water.

A LabVIEW based virtual instrument system for mid infrared laser spectroscopy measurements

The development of a virtual instrument (VI) interface to acquire spectroscopic measurements using a quantum cascade laser-based on LabVIEWTM is presented. The interface can enable mid-infrared laser spectroscopic data acquisition using a fast response mercury-cadmium-telluride (MCT) detector. The MCT is connected to an analog/digital converter card, and the data is displayed in a virtual high-speed oscilloscope. The VI processes the acquired data, displays the information, and saves it to a file through several operations using a “state machine” architecture. Transmission measurements of a polystyrene film were used to calibrate the system. They were compared to the National Institute of Standards and Technology-based FT-IR reference spectrum, showing excellent agreement. The system was tested with four different optical setups for IR Transmission, Diffuse Reflectance Infrared Spectroscopy, Reflection Absorption Infrared Spectroscopy, and Attenuated Total Reflection. The VI can be easily customized, allowing users to adapt it according to their specific laboratory needs.

Detection of high explosives samples deposited on reflective and matte substrates using mid-infrared laser spectroscopy at the grazing angle of incidence assisted by multivariate analysis

Mid-infrared (MIR) laser spectroscopy measurements coupled with multivariate analysis (MVA) routines were applied to detection and classification of high explosives (HEs) deposited on aluminum and acrylonitrile butadiene styrene substrates. An MIR quantum cascade laser (QCL) was optically coupled to a grazing angle probe (QCL-GAP) mount to operate in diffuse reflectance mode at an incidence angle of 82 deg from the surface normal. The experimental conditions enabled reflection–absorption infrared reflectance spectroscopy of the laser light by the surface deposits of HEs and allowed obtaining spectra with high signal-to-noise ratios in a very short time. This optical arrangement permitted collecting data for identifying the vibrational signatures of the HEs to be detectable even at low surface concentrations. The HEs used included pentaerythritol tetranitrate, 1,3,5-trinitroperhydro-1,3,5-triazine, and 2,4,6-trinitrophenylmethylnitramine. Using principal components analysis, 100% of the HE samples deposited on the reflective and nonreflective substrate were correctly classified, avoiding false-positive responses in the analyses. The combination of QCL-GAP spectroscopy with MVA presented will also allow obtaining highly confident detections/identification of hazardous materials and other chemical/biological agents residing on both reflective and nonreflective surfaces. The methodology can easily be extended to other types of applications.

Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics.

Human exhaled breath consists of more than 3000 volatile organic compounds, many of which are relevant biomarkers for various diseases. Although gas chromatography has been the gold standard for volatile organic compound (VOC) detection in exhaled breath, recent developments in mid-infrared (MIR) laser spectroscopy have led to the promise of compact point-of-care (POC) optical instruments enabling even single breath diagnostics. In this review, we discuss the evolution of MIR sensing technologies with a special focus on photoacoustic spectroscopy, and its application in exhaled breath biomarker detection. While mid-infrared point-of-care instrumentation promises high sensitivity and inherent molecular selectivity, the lack of standardization of the various techniques has to be overcome for translating these techniques into more widespread real-time clinical use.

Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.

Optical interruption of a quantum cascade laser for cavity ring-down spectroscopy.

We demonstrate optical unlocking of a cavity resonance in a mid-infrared (MIR) quantum cascade laser (QCL) cavity ring-down spectrometer using a low-power multimode near-infrared (NIR) laser diode. A NIR laser with a center wavelength of 1310 nm is injected into a QCL whose amplitude and frequency are modulated as a result. The shift in frequency leads to a rapid interruption of the cavity resonance. The optical method is compared to cavity interruption with a current step by measuring ring-down times in a high-finesse optical cavity, coupled with a QCL with a center wavelength of 4.5 μm. The results indicate the comparable performance of the all-optical method with the more conventional current modulation, but with significantly reduced bandwidth requirements for the QCL driver, opening the way to other potential applications in MIR laser spectroscopy.

Enhanced RDX Detection Studies on Various Types of Substrates via Tunable Quantum Cascade Laser Spectrometer Coupled with Grazing Angle Probe

Owing to scientific advances in the field of materials sciences and engineering, researchers have developed new energy sources used for spectroscopic applications and measurements of properties resulting from the interaction of matter and electromagnetic radiation in the mid-infrared (MIR) region. MIR lasers, such as quantum cascade lasers (QCLs), used for spectroscopy have quickly found numerous applications in a wide cadre of IR techniques. This provides the opportunity to study properties of highly energetic materials (HEM), among many other applications. MIR laser spectroscopy based detection experiments of HEMs were carried out using a QCL optically coupled to compact grazing angle probe mount (QCL-GAP) enabling reflection-absorption infrared spectroscopy (RAIRS) measurements of thin films of HEMs. A saturated solution of RDX in acetone was prepared, and aliquots of subsequent dilutions of the stock solutions were transferred to test surfaces for QCL-GAP back-reflectance measurements. RDX reflectance signals were monitored as function as the decreasing surface concentration until the signal/noise was ∼ 3. Stainless steel (SS) plates were used as reflective substrates, and anodized aluminum (AN-Al), cardboard, and Teflon were used as non-reflective (matte) substrates. Using generated calibration curves a low limit of detection (LOD) of 1.7 ng/cm2 for RDX/SS and 95 μg/cm2 for RDX/AN-Al were found. Based on the area of laser spot (0.3 cm2) we conclude the minimum masses detected were 490 pg (RDX/SS) and 28 μg (RDX/AN-Al).

Quantifying Isotopic Signatures of N₂O Using Quantum Cascade Laser Absorption Spectroscopy.

Nitrous oxide, N₂O, is the environmentally most relevant constituent of the biogeochemical nitrogen cycle. Human activities, e.g. the agricultural use of mineral fertilizers, accelerate nitrogen transformations, leading to higher emissions of this strong greenhouse gas. Investigating the stable isotopic composition of N₂O provides a better understanding of formation mechanisms to disentangle its variable source and sink processes. Mid-infrared (mid-IR) laser spectroscopy is a highly attractive technique to analyze N₂O isotopocules based on their specific ro-vibrational absorption characteristics. Specifically, quantum cascade laser absorption spectroscopy (QCLAS) in combination with preconcentration has shown to be powerful for simultaneous and high-precision analysis of the main N₂O isotopocules. Recently, in the scope of my PhD project, we have been advancing this analytical technique for the analysis of the very rare doubly substituted N₂O isotopic species 15N14N18O, 14N15N18O, and 15N15N16O, also known as clumped isotopes. Currently, we are investigating the potential of these novel isotopic tracers to track the complex N₂O production and consumption pathways. Improved understanding of the nitrogen cycle will be a major step towards N₂O emission reduction.

Quantifying Isotopic Signatures of N₂O Using Quantum Cascade Laser Absorption Spectroscopy.

Nitrous oxide, N₂O, is the environmentally most relevant constituent of the biogeochemical nitrogen cycle. Human activities, e.g. the agricultural use of mineral fertilizers, accelerate nitrogen transformations, leading to higher emissions of this strong greenhouse gas. Investigating the stable isotopic composition of N₂O provides a better understanding of formation mechanisms to disentangle its variable source and sink processes. Mid-infrared (mid-IR) laser spectroscopy is a highly attractive technique to analyze N₂O isotopocules based on their specific ro-vibrational absorption characteristics. Specifically, quantum cascade laser absorption spectroscopy (QCLAS) in combination with preconcentration has shown to be powerful for simultaneous and high-precision analysis of the main N₂O isotopocules. Recently, in the scope of my PhD project, we have been advancing this analytical technique for the analysis of the very rare doubly substituted N₂O isotopic species 15N14N18O, 14N15N18O, and 15N15N16O, also known as clumped isotopes. Currently, we are investigating the potential of these novel isotopic tracers to track the complex N₂O production and consumption pathways. Improved understanding of the nitrogen cycle will be a major step towards N₂O emission reduction.

Mid-infrared quantum cascade laser spectroscopy of the Ar–NO complex: Fine and hyperfine structure

The rovibrational spectrum of the Ar-NO open-shell complex has been measured in the 5.3 µm region using distributed feed-back quantum lasers to probe the direct absorption in a slit-jet supersonic expansion. Five P-subbands, namely, P'←P″:1/2←3/2,1/2←1/2,3/2←1/2,5/2←3/2, and 7/2←5/2, are observed, with J up to 15.5. The hyperfine structure due to the nuclei spin of 14N (I = 1) can be partially resolved in the P'←P″:1/2←3/2,1/2←1/2, and 3/2←1/2 subbands. The fine structure of the observed spectrum is analyzed using a modified semi-rigid rotor Hamiltonian [W. M. Fawzy and J. T. Hougen, J. Mol. Spectrosc. 137, 154-165 (1989)] and an empirical Hamiltonian [Y. Kim and H. Meyer, Int. Rev. Phys. Chem. 20, 219-282 (2001)] separately. The hyperfine structure can be simulated successfully by including hyperfine terms to the semi-rigid rotor Hamiltonian. A linear J-dependence of the angle between the inertial a-axis of the complex and the intramolecular axis of the NO subunit is also introduced in order to model the strong structure relaxation effect in the P = 1/2 state.

Applying reverse stable isotope labeling analysis by mid-infrared laser spectroscopy to monitor BDOC in recycled wastewater

Biological stability of treated wastewater is currently determined by methods such as biological oxygen demand, ATP-quantification, or flow-cytometric cell counting. However, the continuous increase in water reclamation for wastewater reuse requires new methods for quantifying degradation of biodegradable dissolved organic carbon (BDOC) ranging from very small to high concentrations of dissolved organic carbon (DOC). Furthermore, direct activity measures or absolute concentrations of BDOC are needed that produce comparable and reproducible results in all laboratories. Measuring carbon mineralization by CO2 evolution presents a suitable approach for directly measuring the microbial degradation activity. In this work, we investigated the extent of BDOC in water samples from effluent of a wastewater treatment plant and after purification by ultrafiltration over 204 days. BDOC monitoring was performed with the recently introduced reverse stable isotope labeling (RIL) analysis using mid-infrared spectroscopy for the monitoring of microbial CO2 production. Average BDOC degradation rates ranged from 0.11 to 0.32 mg L−1 d−1 for wastewater treatment plant effluent and from 0.03 to 0.22 mg L−1 d−1 after ultrafiltration. BDOC was degraded over >90 days indicating the long-term instability of the DOC. Degradation experiments over 88 days revealed first order kinetic rate constants for BDOC which corresponded to 12.7 · 10−3 d−1 for wastewater treatment plant effluent and 2.7 · 10−3 d−1 after ultrafiltration, respectively. A thorough sensitivity analysis of the RIL showed that the method is very accurate and sensitive with method detection limits down to 10 μg· L−1 of measured CO2.

Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics.

Mid-infrared (MIR) laser spectroscopy was used to detect the presence of residues of high explosives (HEs) on fabrics. The discrimination of the vibrational signals of HEs from a highly MIR-absorbing substrate was achieved by a simple and fast spectral evaluation without preparation of standards using the classical least squares (CLS) algorithm. Classical least squares focuses on minimizing the differences between the spectral features of the actual spectra acquired using MIR spectroscopy and the spectral features of calculated spectra modeled from linear combinations of the spectra of neat components: HEs, fabrics, and bias. Samples in several combinations of cotton fabrics/HEs were used to validate the methodology. Several experiments were performed focusing on binary, ternary, and quaternary mixtures of TNT, RDX, PETN, and fabrics. The parameters obtained from linear combinations of the calculated spectra were used to perform discrimination analyses and to determine the sensitivity and selectivity of HEs with respect to the substrates and to each other. However, discrimination analysis was not necessary to achieve successful detection of HEs on cotton fabric substrates. The RDX signals ( mRDX > 0.02 mg) on cotton were used to calculate the limit of detection (LOD). The signal-to-noise ratios (S/N) calculated from the spectra of cotton dosed with decreasing masses of RDX until S/N ≈ 3 resulted in a LOD of 15-33 µg, depending on the vibrational band used. Linear fits generated by comparing the mass dosed RDX with the fraction predicted were also used to calculate the LOD based on the uncertainty of the blank and the slope. This procedure resulted in a LOD of 58 µg. Probably the most representative value of the method LOD was calculated using an interpolation of a threshold determined using the predicted average value for the blank plus 3.28 times the standard deviations ( p-value threshold) for low surface dosages of RDX (LOD = 40 µg). The contribution demonstrates that to achieve HE detection on fabrics using the proposed algorithm, i.e., determining the presence/absence of HEs on the substrates, the library must contain the spectra of HEs, substrates, and potential interferents or that these spectra be added to the models in the field. If the model does not contain the spectra of the fabric components, there is a high probability of finding false positives for clean samples (no HEs) and a low probability for failed detection in samples with HEs. More work will be required to demonstrate that these new approaches to HE detection work on real-world samples and when contaminating materials are present in the samples.

Quartz tuning fork-based photodetector for mid-infrared laser spectroscopy

In this paper, we report a new type of photoelectric detector based on a standard quartz crystal tuning fork (QCTF) with resonant frequency of ~ 32 kHz for spectroscopic applications. Analogous to the photoelectric effect of traditional semiconductor detectors, we utilize the piezoelectric effect of the QCTF to gauge the light intensity. To explore the capabilities of this technique, the impact of incident light beam excitation positions with respect to QCTF on signal amplitude, resonant frequency and Q factor, as well as the dependence on incident light intensity, ambient pressure and temperature, was investigated in detail. Finally, the QCTF-based photodetector was successfully demonstrated for qualitative analysis of gasoline components by combing a broadband tunable external cavity quantum cascade laser.

Portable Infrared Laser Spectroscopy for On-site Mycotoxin Analysis

Mycotoxins are toxic secondary metabolites of fungi that spoil food, and severely impact human health (e.g., causing cancer). Therefore, the rapid determination of mycotoxin contamination including deoxynivalenol and aflatoxin B1 in food and feed samples is of prime interest for commodity importers and processors. While chromatography-based techniques are well established in laboratory environments, only very few (i.e., mostly immunochemical) techniques exist enabling direct on-site analysis for traders and manufacturers. In this study, we present MYCOSPEC - an innovative approach for spectroscopic mycotoxin contamination analysis at EU regulatory limits for the first time utilizing mid-infrared tunable quantum cascade laser (QCL) spectroscopy. This analysis technique facilitates on-site mycotoxin analysis by combining QCL technology with GaAs/AlGaAs thin-film waveguides. Multivariate data mining strategies (i.e., principal component analysis) enabled the classification of deoxynivalenol-contaminated maize and wheat samples, and of aflatoxin B1 affected peanuts at EU regulatory limits of 1250 μg kg−1 and 8 μg kg−1, respectively.