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In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4–13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in individuals with obesity. Sexual dimorphism is present in MASLD. A noninvasive test to diagnose the severity of the disease, in particular the presence of Metabolic dysfunction-associated steatohepatitis (MASH), is lacking. This European multicenter prospective study uses a blood test based on mid-infrared (MIR) metabolic fingerprinting of individuals with severe or morbid obesity to diagnose MASH. Three hundred eighty-two individuals with severe or morbid obesity undergoing bariatric surgery were enrolled prospectively. Liver biopsies were obtained during surgery and assessed centrally. An algorithm was defined to calculate a score from the recorded MIR spectrum and to establish a diagnostic threshold to classify patients with MASH. Among the women (n = 217), MASH was diagnosed in 14.3% of cases. For women, the performance in terms of AUC were 0.83 and 0.82 in the calibration and validation groups, respectively. For a threshold of 0.1817, sensitivities were 86% and 70%, specificities were 81% and 75%, PPV were 43% and 32%, NPV were 97% and 94% and ACC were 82% and 74% for the calibration and validation groups, respectively. For men (n = 78; MASH: 33.3%), the performance of the spectral model was poor. The metabolic fingerprint obtained by MIR spectroscopy can rule out MASH in women with severe or morbid obesity. Its value in men needs new studies. Trial registration : ClinicalTrials.gov identifier: ClinicalTrials.gov identifier: NCT03978247 (04/06/2019)

Characterizing and ultimately controlling the heterogeneity underlying biomolecular functions, quantum behavior of complex matter, photonic materials, or catalysis requires large-scale spectroscopic imaging with simultaneous specificity to structure, phase, and chemical composition at nanometer spatial resolution. However, as with any ultrahigh spatial resolution microscopy technique, the associated demand for an increase in both spatial and spectral bandwidth often leads to a decrease in desired sensitivity. We overcome this limitation in infrared vibrational scattering-scanning probe near-field optical microscopy using synchrotron midinfrared radiation. Tip-enhanced localized light–matter interaction is induced by low-noise, broadband, and spatially coherent synchrotron light of high spectral irradiance, and the near-field signal is sensitively detected using heterodyne interferometric amplification. We achieve sub-40-nm spatially resolved, molecular, and phonon vibrational spectroscopic imaging, with rapid spectral acquisition, spanning the full midinfrared (700–5,000 cm ⁻¹) with few cm ⁻¹ spectral resolution. We demonstrate the performance of synchrotron infrared nanospectroscopy on semiconductor, biomineral, and protein nanostructures, providing vibrational chemical imaging with subzeptomole sensitivity.

Gas samples relevant to health 1 , 2 – 3 and the environment 4 , 5 – 6 typically contain many molecular species that span a huge concentration dynamic range. Mid-infrared frequency comb spectroscopy with high-finesse cavity enhancement has allowed the most sensitive multispecies trace-gas detections so far 2 , 7 , 8 , 9 , 10 , 11 , 12 – 13 . However, the robust performance of this technique depends critically on ensuring absorption-path-length enhancement over a broad spectral coverage, which is severely limited by comb–cavity frequency mismatch if strongly absorbing compounds are present. Here we introduce modulated ringdown comb interferometry, a technique that resolves the vulnerability of comb–cavity enhancement to strong intracavity absorption or dispersion. This technique works by measuring ringdown dynamics carried by massively parallel comb lines transmitted through a length-modulated cavity, making use of both the periodicity of the field dynamics and the Doppler frequency shifts introduced from a Michelson interferometer. As a demonstration, we measure highly dispersive exhaled human breath samples and ambient air in the mid-infrared with finesse improved to 23,000 and coverage to 1,010 cm −1 . Such a product of finesse and spectral coverage is orders of magnitude better than all previous demonstrations 2 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 – 20 , enabling us to simultaneously quantify 20 distinct molecular species at above 1-part-per-trillion sensitivity varying in concentrations by seven orders of magnitude. This technique unlocks next-generation sensing performance for complex and dynamic molecular compositions, with scalable improvement to both finesse and spectral coverage. A new optical technique, modulated ringdown comb interferometry, is introduced for measuring the concentration of gas species in a complex sample and its efficacy demonstrated using exhaled human breath and ambient air in the mid-infrared.

Understanding the spatial and depth sensitivity of non-invasive near-infrared spectroscopy (NIRS) measurements to brain tissue-i.e., near-infrared neuromonitoring (NIN) - is essential for designing experiments as well as interpreting research findings. However, a thorough characterization of such sensitivity in realistic head models has remained unavailable. In this study, we conducted 3,555 Monte Carlo (MC) simulations to densely cover the scalp of a well-characterized, adult male template brain (Colin27). We sought to evaluate: (i) the spatial sensitivity profile of NIRS to brain tissue as a function of source-detector separation, (ii) the NIRS sensitivity to brain tissue as a function of depth in this realistic and complex head model, and (iii) the effect of NIRS instrument sensitivity on detecting brain activation. We found that increasing the source-detector (SD) separation from 20 to 65 mm provides monotonic increases in sensitivity to brain tissue. For every 10 mm increase in SD separation (up to ~45 mm), sensitivity to gray matter increased an additional 4%. Our analyses also demonstrate that sensitivity in depth (S) decreases exponentially, with a \"rule-of-thumb\" formula S=0.75*0.85(depth). Thus, while the depth sensitivity of NIRS is not strictly limited, NIN signals in adult humans are strongly biased towards the outermost 10-15 mm of intracranial space. These general results, along with the detailed quantitation of sensitivity estimates around the head, can provide detailed guidance for interpreting the likely sources of NIRS signals, as well as help NIRS investigators design and plan better NIRS experiments, head probes and instruments.

Microplastics are a widespread contaminant found not only in various natural habitats but also in drinking waters. With spectroscopic methods, the polymer type, number, size, and size distribution as well as the shape of microplastic particles in waters can be determined, which is of great relevance to toxicological studies. Methods used in studies so far show a huge diversity regarding experimental setups and often a lack of certain quality assurance aspects. To overcome these problems, this critical review and consensus paper of 12 European analytical laboratories and institutions, dealing with microplastic particle identification and quantification with spectroscopic methods, gives guidance toward harmonized microplastic particle analysis in clean waters. The aims of this paper are to (i) improve the reliability of microplastic analysis, (ii) facilitate and improve the planning of sample preparation and microplastic detection, and (iii) provide a better understanding regarding the evaluation of already existing studies. With these aims, we hope to make an important step toward harmonization of microplastic particle analysis in clean water samples and, thus, allow the comparability of results obtained in different studies by using similar or harmonized methods. Clean water samples, for the purpose of this paper, are considered to comprise all water samples with low matrix content, in particular drinking, tap, and bottled water, but also other water types such as clean freshwater.

The review briefly describes various types of infrared (IR) and Raman spectroscopy methods. At the beginning of the review, the basic concepts of biological methods of environmental monitoring, namely bioanalytical and biomonitoring methods, are briefly considered. The main part of the review describes the basic principles and concepts of vibration spectroscopy and microspectrophotometry, in particular IR spectroscopy, mid- and near-IR spectroscopy, IR microspectroscopy, Raman spectroscopy, resonance Raman spectroscopy, Surface-enhanced Raman spectroscopy, and Raman microscopy. Examples of the use of various methods of vibration spectroscopy for the study of biological samples, especially in the context of environmental monitoring, are given. Based on the described results, the authors conclude that the near-IR spectroscopy-based methods are the most convenient for environmental studies, and the relevance of the use of IR and Raman spectroscopy in environmental monitoring will increase with time.

The chemical and structural properties of biomolecules determine their interactions, and thus their functions, in a wide variety of biochemical processes. Innovative imaging methods have been developed to characterise biomolecular structures down to the angstrom level. However, acquiring vibrational absorption spectra at the single molecule level, a benchmark for bulk sample characterization, has remained elusive. Here, we introduce off-resonance, low power and short pulse infrared nanospectroscopy (ORS-nanoIR) to allow the acquisition of infrared absorption spectra and chemical maps at the single molecule level, at high throughput on a second timescale and with a high signal-to-noise ratio (~10–20). This high sensitivity enables the accurate determination of the secondary structure of single protein molecules with over a million-fold lower mass than conventional bulk vibrational spectroscopy. These results pave the way to probe directly the chemical and structural properties of individual biomolecules, as well as their interactions, in a broad range of chemical and biological systems. While infrared nanospectroscopy methods based on thermomechanical detection (AFM-IR) enables the acquisition of absorption spectra at the nanoscale, single molecule detection has not been possible so far. Here, the authors present off-resonance, low power and short pulse infrared nanospectroscopy (ORS-nanoIR), which allows measuring infrared absorption spectra at the single molecule level in a time scale of seconds with high throughput and demonstrate that the secondary structure of single protein molecules can be determined with this method.

The analysis of human facial expressions from the thermal images captured by the Infrared Thermal Imaging (IRTI) cameras has recently gained importance compared to images captured by the standard cameras using light having a wavelength in the visible spectrum. It is because infrared cameras work well in low-light conditions and also infrared spectrum captures thermal distribution that is very useful for building systems like Robot interaction systems, quantifying the cognitive responses from facial expressions, disease control, etc. In this paper, a deep learning model called IRFacExNet ( I nfra R ed Fac ial Ex pression Net work) has been proposed for facial expression recognition (FER) from infrared images. It utilizes two building blocks namely Residual unit and Transformation unit which extract dominant features from the input images specific to the expressions. The extracted features help to detect the emotion of the subjects in consideration accurately. The Snapshot ensemble technique is adopted with a Cosine annealing learning rate scheduler to improve the overall performance. The performance of the proposed model has been evaluated on a publicly available dataset, namely IRDatabase developed by RWTH Aachen University. The facial expressions present in the dataset are Fear, Anger, Contempt, Disgust, Happy, Neutral, Sad, and Surprise. The proposed model produces 88.43% recognition accuracy, better than some state-of-the-art methods considered here for comparison. Our model provides a robust framework for the detection of accurate expression in the absence of visible light.

We used two-dimensional infrared spectroscopy to disentangle the broad infrared band in the amide II vibrational regions of Bombyx mori native silk films, identifying the single amide II modes and correlating them to specific secondary structure. Amide I and amide II modes have a strong vibrational coupling, which manifests as cross-peaks in 2D infrared spectra with frequencies determined by both the amide I and amide II frequencies of the same secondary structure. By cross referencing with well-known amide I assignments, we determined that the amide II (N-H) absorbs at around 1552 and at 1530 cm–1 for helical and β-sheet structures, respectively. We also observed a peak at 1517 cm−1 that could not be easily assigned to an amide II mode, and instead we tentatively assigned it to a Tyrosine sidechain. These results stand in contrast with previous findings from linear infrared spectroscopy, highlighting the ability of multidimensional spectroscopy for untangling convoluted spectra, and suggesting the need for caution when assigning silk amide II spectra.