Applied Spectroscopy

Optical photothermal infrared (O-PTIR) spectroscopy is rapidly transforming molecular imaging by combining the chemical specificity of infrared absorption with the high spatial resolution enabled by visible-light excitation. This review provides a comprehensive and forward-looking overview of O-PTIR, with a particular focus on its expanding applications in biological and biomedical research. We begin by outlining the development of O-PTIR from earlier photoacoustic and photothermal infrared methodologies, placing it within the broader field of vibrational spectroscopy and emphasizing its unique advantages including sub-micrometer spatial resolution, label-free detection, and compatibility with heterogeneous biological samples. The foundational principles of O-PTIR, such as the photothermal effect, instrumental configurations, and the integration of simultaneous IR and Raman measurements, are discussed to establish the basis for its analytical capabilities. Leveraging these strengths, O-PTIR enables high-resolution and chemically specific imaging of key biomolecules including lipids, proteins, nucleic acids, and metabolites across cells, tissues, and microbial systems. Current applications span diverse areas such as cellular metabolism, microbial phenotyping, cancer diagnostics, biomarker identification, and pharmaceutical analysis. Alongside these advances, we critically evaluate existing limitations, including challenges associated with sample preparation, instrumental complexity, signal interpretation, and standardization. Finally, we highlight emerging directions such as live-cell O-PTIR measurements, artificial intelligence (AI)-driven spectral analysis, and the development of hybrid modalities including FISH-O-PTIR. Together, these innovations reinforce O-PTIR's potential as a transformative technology for biological and biomedical sciences, poised to bridge fundamental molecular studies with future clinical and translational applications.

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Exhaust gases generated by fossil fuel combustion significantly contribute to air pollution and climate change, emphasizing the need for efficient combustion diagnostics. OH radicals are key indicators of flame behavior; however, conventional laser-induced fluorescence (LIF) techniques are impractical for industrial monitoring due to their complexity. In this study, wavelength modulation spectroscopy (WMS) employing a near-infrared laser at 1.49 μm was used to measure OH radical concentrations, incorporating a correction for spectral interference from H₂O. A wavelength division multiplexer (WDM) was used to enable the simultaneous operation of two lasers at 1.49 μm (OH) and 1.39 μm (H₂O, temperature), allowing for correction of water interference. OH concentrations were determined using both WMS and direct absorption spectroscopy (DAS), and the results were evaluated through comparison with CHEMKIN simulations. The proposed dual-laser system demonstrated reliable and interference-corrected quantification of OH radicals in methane (CH₄)/air premixed flames over a range of equivalence ratios. Comparisons with thermocouple-based temperature measurements and CHEMKIN-predicted species concentrations confirmed the reliability of the proposed technique. This study highlights the robustness and applicability of WMS-based OH diagnostics for combustion monitoring and demonstrates the potential for future implementation in industrial burner systems.

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Partial least squares discriminant analysis (PLS-DA) is often used for data sets that consist of a large number of potential predictors but relatively few observations such that chance correlations between predictors and response can occur that lead to false conclusions. Hence, there is a need for data adequacy testing before model building but currently no such method exists. In this work we propose one where we used random permutations to destroy the correlation structure between predictor and response data. This produced normal distributions of chance correlation coefficients that were used to find correlation coefficients in the non-permuted data that differed significantly from chance occurrences. Based on these distributions, we defined two novel null hypotheses to control for when a true null hypothesis is incorrectly rejected and the other for when a false null hypothesis is not rejected. To counter false positive errors, the standard significance levels were adjusted with predictor-based Bonferroni corrections. To counter false negative errors, we compared the true and permuted correlation coefficients in distribution tails. The outcomes of the hypothesis tests then indicated whether or not PLS-DA models could be successfully built from these data sets. We also investigated how to determine the number of samples needed for a data set with a given number of predictors. Simulations showed that our method produced significantly fewer false positives than PLS-DA (P = 0.0018, our method error rate 12 × less than PLS-DA error rate) but significantly more false negatives (P = 0.0003, our method error rate 4.5 × more than PLS-DA error rate). Data from Raman spectroscopy showed that the method transferred to real data. By pre-screening such data, our method can aid in assessing whether to proceed with model building and, when there is a need to increase the sample size, we show by how much.

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This study presents a new approach to wood species identification using laser-induced breakdown spectroscopy (LIBS) combined with stacked machine learning techniques. The research analyzed 700 samples comprising nine Dalbergia species and nine additional tropical timber species, utilizing a handheld LIBS analyzer. A stacking methodology was developed by integrating three support vector machine (SVM) models with different kernel functions (linear, polynomial, and radial) in a one-versus-all (OVA) configuration. These SVM outputs were then combined using a partial least squares discriminant analysis (PLS-DA) meta-learner. Through PCA-based variable selection, the dimensionality was reduced from 23 401 to wavelengths while maintaining classification accuracy. The stacking approach achieved a Cohen's kappa value of 0.8671 in the validation set, significantly outperforming traditional flat classifiers. Variable importance analysis revealed calcium, magnesium, and barium as crucial elements for species differentiation, with their concentrations reflecting environmental conditions and geographical origins. This research demonstrates the potential of combining LIBS spectroscopy with advanced machine learning techniques for rapid, non-invasive timber identification, which can support efforts against illegal logging and enforcement of international trade regulations.

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Spectral similarity supports comparison of circular dichroism (CD) spectra by using all datapoints to improve alignment of wavelength, intensity and offset. CD is increasingly used to confirm the higher order structure and stability of biopharmaceutical proteins, which requires method validation and assessment of robustness in quality regulated analytical systems. Camphor-10-sulphonic acid (CSA), or its ammonium salt, is widely used to calibrate spectropolarimeters, with its use specified in the European Pharmacopoeia (EP), and, more broadly, as a system suitability standard. Spectral similarity comparison of 75 CSA reference spectra in the Protein Circular Dichroism Data Bank (PCDDB) showed the potential value of this approach to monitor instrument performance and support compliance within a quality system.

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This study employs an integrated, multi-technique, minimally microinvasive investigation of the material composition, stratigraphy, and technique of the Portrait of Farrokh Gaffari, which was created in 1959 by Qollar-Aqasi, an artist of the Iranian Coffeehouse Painting workshop, thereby closing the element → phase → molecule evidence chain by combining whole-field macroscopic X-ray fluorescence analysis (MA-XRF) with X-ray diffraction (XRD) and micro-Raman, complemented by cross-section microscopy and fiber analysis. MA-XRF resolves two subsurface narratives: Continuous Sr and Ba-K line patterns that expose hidden Persian lettering on a reused shop-sign support (blue ground with white lettering), and an Fe-mapped pentimento masked by Fe-poor overpaint, documenting a localized compositional revision. Mapping and section analysis reveal a zinc-rich gray priming beneath the paint and multi-campaign varnishes. Using Bruker Gamma Filter technology, effective separation of substrate-derived and surface zinc signals was achieved, thereby improving the accuracy of spectral targeting. Raman identifies lithol red (PR49, likely PR49:2) as the principal red and Prussian blue (PB) as the main blue. Ultramarine occurs as deep-blue accents. XRD identifies lithopone (BaSO₄+ZnS), zinc oxide (ZnO), and lead chromate (PbCrO₄), rationalizing why the red is XRD-silent while whites/yellows are phase-visible. Together, these data resolve a compact, high-saturation palette logic: mixture-greens from PbCrO₄ + PB, lithopone-moderated whites and reds, and area-specific combinations (e.g., canopy: PbCrO₄ + PB + iron oxide + lithopone; fountains and flowing water: PB + lithopone, flowerbeds/red blossoms: lithol Red (PR49) + carbon black). Fiber microscopy identifies a cotton support assembled from two panels with orthogonal warp orientations. Furthermore, it is worth noting that this painting was executed on a repurposed blue-background-with-white-lettering advertising sign, which is a substrate choice that not only aligns closely with the pragmatic “local material adoption” principle of the Coffeehouse Painting workshop but also echoes their typical painting-on-a-sign practice, with the original sign’s material composition specified as follows: the blue ground comprises CaCO₃, BaSO₄, and PB, while the white lettering is formulated with BaSO₄.

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Reliable monitoring and analysis of volatile organic compounds (VOCs) are crucial for environmental monitoring and human health. In this work, we report a system for fast, sensitive, and multispecies detection of VOCs by combining a fiber-based broadband mid-infrared (MIR) supercontinuum (SC) source with an upconversion spectrometer. The system’s performance in terms of species identification accuracy, robustness against inter-species interference and real-time multi-species detection capability was evaluated by monitoring and tracing the evaporation dynamics of three VOCs, i.e., acetone, ethanol, and α-pinene. We achieved detection limits of 30 parts per million per meter (ppm·m), 5 ppm·m, and 0.6 ppm·ּּm in 1 second for these three compounds, respectively. Furthermore, the evaporation behavior of individual compounds from a mixture was investigated to demonstrate the system’s capability for dynamic multicomponent analysis.

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Flufenamic acid (FFA), a widely used nonsteroidal anti-inflammatory drug, is primarily recognized for its therapeutic role in pain and inflammation management. Beyond its pharmaceutical relevance, FFA exhibits pronounced pH-dependent variations in its ultraviolet–visible (UV–Vis) absorption and fluorescence spectra, which remain largely unexplored for functional material applications. In this study, the optical behavior of FFA was systematically investigated over a broad pH range (1–14), revealing distinct and reversible spectral changes driven by protonation and deprotonation processes. These pH-responsive optical features were employed to construct molecular logic gates, including implication and an improved “INHIBIT” (IP-INHIBIT) gate, highlighting the potential of FFA as an on–off pH sensor and a promising candidate for organic electronic applications.

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In milk-based formulations, fat globule size is a fundamental quality parameter. Accurate measurement of globule size is critical for ensuring consistent product performance, preferably through in-line monitoring. However, most conventional techniques are off-line and unsuitable for integration into manufacturing processes. The objective of this study was to demonstrate the feasibility of a recently developed optical sensor based on Multi-Reflectance Spectroscopy (MRS) for in-line estimation of the fat globule Sauter diameter. The MRS sensor acquires multidimensional reflectance data across multiple wavelengths and defined illumination–detection geometries. Industrial validation was performed in a milk standardization plant using low-fat (1.5 wt%) and whole-fat (3.5 wt%) milk. Fat globule sizes were systematically varied by stepwise adjustment of homogenization pressure. Reference globule size distributions were obtained via analytical centrifugation and expressed as the Sauter diameter. Analytical reflectances derived from the reference size distributions were compared with experimental spectra to assess globule size dependency of the optical responses. Principal component analysis revealed a clear separation between fat concentration levels, motivating the development of concentration-specific partial least squares regression models. Two PLS models were trained and validated using independent datasets. Across the combined concentration range and full Sauter diameter span (0.7–3.5  μ m), resultet in a test RMSE of 271 nm. For the separate 1.5 and 3.5 wt% fat lines, RMSE values of approximately 80 and 40 nm were obtained, respectively. These results demonstrate that the MRS-based optical sensor enables accurate, real-time in-line monitoring of fat globule size in complex dairy emulsions and represents a promising process-analytical tool for industrial process control.