Conventional Raman Spectroscopy Research Articles

High frequency of BCP, but less CPP crystal-mediated calcification in cartilage and synovial membrane of osteoarthritis patients

ObjectiveEctopic articular calcification is a common phenomenon of osteoarthritic joints, and closely related with disease progression. Identification of the involved calcium crystal types represents an important topic in research and clinical practice. Difficulties in accurate detection and crystal type identification have led to inconsistent data on the prevalence and spatial distribution of BCP and CPP deposition. MethodCombining multiple imaging methods including conventional radiography, histology and Raman spectroscopy, this study provides a comprehensive analysis of BCP and CPP based calcification, its frequency and distribution in cartilage and synovial membrane samples of 92 OA patients undergoing knee replacement surgery. ResultsConventional radiography showed calcifications in 35% of patients. Von Kossa staining detected calcified deposits in 88% and 57% of cartilage and synovial samples, respectively. BCP crystals presented as brittle deposits on top of the cartilage surface or embedded in synovial tissue. CPP deposits appeared as larger granular needle-shaped clusters or dense circular pockets below the cartilage surface or within synovial tissue. Spectroscopic analysis detected BCP crystals in 75% of cartilage and 43% of synovial samples. CPP deposition was only detected in 18% of cartilage and 15% of synovial samples, often coinciding with BCP deposits. ConclusionBCP is the predominant crystal type in calcified cartilage and synovium while CPP deposition is rare, often coinciding with BCP. Distinct and qualitative information on BCP and CPP deposits in joint tissues give rise to the speculation that different disease entities are involved that might need different treatment strategies.

Photon counting Raman spectroscopy: a benchmarking study vs surface plasmon enhancement

We demonstrate a single-photon counting Raman spectroscope and benchmark it against conventional and surface-enhanced Raman spectroscopy. For direct comparison without ambiguity, we use the same solutions of Rhodamine 6G and a common optical setup with either a spectrometer or an acousto-optic tunable filter, whereas the surface enhancement is realized with immobilized Ag nanoparticles. Our results find that the single photon counting significantly elevates the detection sensitivity by up to eight orders of magnitude, arriving at a comparable level of surface-enhanced Raman spectroscopy. Another significant advantage is with the time-resolving measurement, where we demonstrate time-gated and time-correlated single-photon counting with sub-nanosecond resolution. It offers insights into the samples’ transient responses and enables the isolation of Raman scattering from fluorescence signals.

Swept-source Raman spectroscopy of chemical and biological materials.

Raman spectroscopy has been used as a powerful tool for chemical analysis, enabling the noninvasive acquisition of molecular fingerprints from various samples. Raman spectroscopy has proven to be valuable in numerous fields, including pharmaceutical, materials science, and biomedicine. Active research and development efforts are currently underway to bring this analytical instrument into the field, enabling in situ Raman measurements for a wider range of applications. Dispersive Raman spectroscopy using a fixed, narrowband source is a common method for acquiring Raman spectra. However, dispersive Raman spectroscopy requires a bulky spectrometer, which limits its field applicability. Therefore, there has been a tremendous need to develop a portable and sensitive Raman system. We developed a compact swept-source Raman (SS-Raman) spectroscopy system and proposed a signal processing method to mitigate hardware limitations. We demonstrated the capabilities of the SS-Raman spectroscopy by acquiring Raman spectra from both chemical and biological samples. These spectra were then compared with Raman spectra obtained using a conventional dispersive Raman spectroscopy system. The SS-Raman spectroscopy system used a wavelength-swept source laser (822 to 842nm), a bandpass filter with a bandwidth of 1.5nm, and a low-noise silicon photoreceiver. Raman spectra were acquired from various chemical samples, including phenylalanine, hydroxyapatite, glucose, and acetaminophen. A comparative analysis with the conventional dispersive Raman spectroscopy was conducted by calculating the correlation coefficients between the spectra from the SS-Raman spectroscopy and those from the conventional system. Furthermore, Raman mapping was obtained from cross-sections of swine tissue, demonstrating the applicability of the SS-Raman spectroscopy in biological samples. We developed a compact SS-Raman system and validated its performance by acquiring Raman spectra from both chemical and biological materials. Our straightforward signal processing method enhanced the quality of the Raman spectra without incurring high costs. Raman spectra in the range of 900 to were observed for phenylalanine, hydroxyapatite, glucose, and acetaminophen. The results were validated with correlation coefficients of 0.88, 0.84, 0.87, and 0.73, respectively, compared with those obtained from dispersive Raman spectroscopy. Furthermore, we performed scans across the cross-section of swine tissue to generate a biological tissue mapping plot, providing information about the composition of swine tissue. We demonstrate the capabilities of the proposed compact SS-Raman spectroscopy system by obtaining Raman spectra of chemical and biological materials, utilizing straightforward signal processing. We anticipate that the SS-Raman spectroscopy will be utilized in various fields, including biomedical and chemical applications.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects.

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis.

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Chemometric enhancement for blind signal resolution from non-invasive spatially offset Raman spectra

Spatially offset Raman spectroscopy (SORS) is a promising spectroscopic technique that enables the collection of Raman signals from deeper layers of materials compared to conventional Raman spectroscopy. SORS equipment allows acquiring Raman spectra of substances through packaging, making it a non-invasive analytical technique. Note that the acquired spectrum is always the result of mixing two contributions: (1) the spectrum of the container material, and (2) the spectrum of the substance inside the container. Nowadays, SORS equipment are supported by software capable of removing the surface contribution from the recorded spectral data. However, the optimal extraction of this contribution is not achieved in all cases. This study explored the potential of two chemometric methods, Multivariate Curve Resolution (MCR) and Independent Components Analysis (ICA), for resolving the mixed spectra acquired by Vaya Raman equipment (Agilent) of four standard substances (sucrose, anhydrous sodium sulphate, ethanol 96% and glycerol) analyzed through containers of two different plastic materials (polypropylene and polyethylene terephthalate). The two resulting resolved spectra (by MCR and by ICA) and the one resolved by the equipment software were compared by similarity analysis with the Raman spectra of the standard substances available in recognized databases intended as target spectra. Similarity analysis was performed by calculating four similarity indexes, namely: cosine, arccosine, coefficient of determination and nearness index. Both MCR and ICA methods successfully extracted pure Raman spectra of the test substances more similar to the standard substances than the spectra resolved by the equipment software. These results highlighted the potential of both methods for resolving complex mixed signals, such as those acquired through the SORS technique, thereby enhancing its utility.

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.

Time-resolved self-heating temperature measurements of AlInN/GaN HEMTs using CeO2 Raman micro-thermometers

The DC and RF electrical performance and reliability of GaN-based transistors depend on their thermal behavior. Therefore, measuring the self-heating temperature of these devices under real operating conditions with high accuracy is an important and challenging issue. For these reasons, we present a time-resolved thermometry technique to measure transient self-heating temperatures in semiconductor components by combining conventional Raman spectroscopy and CeO2 Raman micro-thermometers. Thus, the experimental GaN volumetric and surface self-heating temperatures measured for biased AlInN/GaN HEMTs in both DC and pulsed regime are reported with a submicrometer spatial resolution and a temperature resolution of about 5 °C. Likewise time-resolved self-heating temperature of drain contact surface has also been studied.

Evaluation of Anisotropic Biaxial Stress in Extremely-Thin Body (100) Silicon-Germanium-on-Insulator p-Type Metal-Oxide-Semiconductor Field-Effect-Transistor by Oil-Immersion Raman Spectroscopy

We evaluated the anisotropic biaxial strain in the channel region of extremely-thin body silicon-germanium-on-insulator p-MOSFETs by oil-immersion Raman spectroscopy. Oil-immersion Raman spectroscopy can measure the transverse optical (TO) phonon mode which cannot be detected by conventional Raman spectroscopy under the backscattering configuration for (001) substrate. Therefore, we can calculate the anisotropic biaxial stress in the SiGe channel region and investigate the channel width dependence of stress. From the Raman spectra obtained in the polarization configurations of longitudinal optical (LO)-active and TO-active, we confirmed that compressive stress is induced in the SiGe channel region. In addition, we observed that the strain state of the SiGe channel region becomes quasi-uniaxial strain by narrowing the channel width.

Coating Silver Tree‐like Fractal Structure with Silica Layer for Inhibiting Chemical Reactions of Analytes in Surface‐Enhanced Raman Scattering

Silica coating is performed onto silver tree‐like fractal structures, which are self‐grown in a solution, through a wet process using tetraethyl orthosilicate. Surface‐enhanced Raman scattering (SERS) of para‐aminothiophenol (p‐ATP) is measured on the silver tree‐like fractal structures with/without silica layer at the excitation wavelength of 532 nm. p‐ATP is chemically transformed into dimercaptoazobenzene (DMAB) on the non‐coated silver tree‐like fractal structures, where DMAB peaks are clearly observed, during the SERS measurements. The DMAB peaks decrease/disappear on the silica‐coated ones although the p‐ATP peaks are observed. In the results, it is indicated that the chemical transformation is inhibited on the silica‐coated ones. The sensitivity is decreased by half compared to the non‐coated silver tree‐like fractal structures, where the lower detection limit is estimated to be ≈2 × 10−5 mol L−1 for p‐ATP. The silica coating is advantageous for inhibiting chemical transformations of analytes, enabling identification/estimation of chemicals in unknown sample with SERS similarly to conventional Raman spectroscopy.

G phonon linewidth and phonon-phonon interaction in p-type doped CVD graphene crystals

We employed conventional and time-resolved Raman spectroscopy to study the effects of electron-phonon and phonon-phonon interactions on the linewidth of the G phonon in both unintentionally and intentionally doped polycrystalline chemical vapor deposited (CVD) graphene samples. We directly measured the G phonon lifetime, observing a ≈14% decrease with doping up to EF = 270 meV. The anticipated reduction of G phonon linewidth due to a decrement in electron-phonon contribution deviates from first principles calculations. CVD samples exhibit a ≈30% decrease in the electron-phonon coupling constant, λΓ(EF = 0), compared to exfoliated graphene samples. Additionally, phonon-defect scattering makes a significant contribution to the G band linewidth in CVD graphene samples, owing to their lower crystal quality compared to exfoliated graphene.

Development of a Low-Cost Paper-Based Platform for Coffee Ring-Assisted SERS.

The need for highly sensitive, low-cost, and timely diagnostic technologies at the point of care is increasing. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is an advantageous technique to address this need, as it can rapidly detect analytes in small or dilute samples with improved sensitivity compared to conventional Raman spectroscopy. Despite the many advantages of SERS, one drawback of the technique is poor reproducibility due to variable interactions between nanoparticles and target analytes. To overcome this limitation, coupling SERS with the coffee ring effect has been implemented to concentrate and localize analyte-nanoparticle conjugates for improved signal reproducibility. However, current coffee ring platforms require laborious fabrication steps. Herein, we present a low-cost, two-step fabrication process for coffee ring-assisted SERS, utilizing wax-printed nitrocellulose paper. The platform was designed to produce a highly hydrophobic paper substrate that supports the coffee ring effect and tested using gold nanoparticles for SERS sensing. The nanoparticle concentration and solvent were varied to determine the effect of solution composition on ring formation and center clearance. The SERS signal was validated using 4-mercaptobenzoic acid (MBA) and tested with Moraxella catarrhalis bacteria to ensure functionality for chemical and biological applications. The limit of detection using MBA is 41.56 nM, and the biochemical components of the bacterial cell wall were enhanced with low spectral variability. The developed platform is advantageous due to ease of fabrication and use, representing the next step toward implementing low-cost coffee ring-assisted SERS for point-of-care sensing.

What is cooking in your kitchen: seeing "invisible" with time-resolved coherent anti-Stokes Raman spectroscopy.

Cooking oil is a critical component of human food and its main component, lipid, is influential to health, but assessing its authenticity and quality can be challenging due to its complex chemical composition. In this study, we introduce a novel application of time-resolved coherent anti-Stokes Raman scattering (T-CARS) spectroscopy for detecting adulteration and understanding the mechanisms of lipid oxidation in various cooking oils. Our research surpasses the limitations of conventional spontaneous Raman spectroscopy, demonstrating that intra-molecular interactions from unsaturated bonds in triglycerides significantly influence vibrational dephasing time. We observed that these dephasing times, although diverse initially, converge to a similar value after heating cycles. Notably, a longer vibrational dephasing of the CH2 symmetric stretching mode was found to correlate with a higher lipid oxidation rate. These findings underscore the potential of T-CARS in identifying and characterizing subtle molecular interactions, offering a transformative approach to understanding molecular dynamics. This research paves the way for broader applications of T-CARS across fields such as chemistry, biomedicine, and material science, marking a significant advancement in the development of innovative analytical techniques.

An in situ real-time monitoring of the hydration and dehydration of building gypsum: Conventional and standoff Raman spectroscopy coupled with chemometrics analysis

The application of conventional and standoff Raman spectroscopy has been a widely explored technique for materials characterization. This study uses Raman spectroscopy coupled with chemometrics analysis to monitor gypsum's hydration and dehydration process. The Raman spectra of the specimens are captured periodically, and the intensity ratios of the selected characteristic Raman bands and the intensity ratios of the characteristics bands are used for chemometric analysis. To determine the statistical significance of the variations of the Raman band intensities with the setting time, a statistical analysis of the intensity ratios was conducted using analysis of variances (ANOVA) followed by Games-Howell post-hoc statistics and pairwise comparison of the groups. As the dehydration progresses and the sample dries, the Raman bands of the water molecule gradually diminish, indicating the loss of water molecules from the specimens. The results demonstrate the capability of the Raman spectroscopy, the chemometric techniques used, and the analysis of variances for distinguishing and monitoring gypsum's hydration and dehydration process. The gradual variation in Raman band intensity ratios and the diminishing bands of water molecules corresponds to dehydration, the loss of water, and the phase transformation from a fluid-like gypsum paste to solid phases.

Investigation of Equivalence Between Non-Resonant Raman Excitation Spectroscopy and Conventional Raman Spectroscopy

As spontaneous Raman spectroscopy is demonstrated to be viable for many applications, it is still held back by its inherently low signal and typically extended acquisition time. While several techniques can overcome this by enhancing the scattered signal to quicken acquisition, a parallel field that quickens spectral acquisition using instrumentation has received less focus. In principle, Raman spectra could be obtained by tuning the excitation wavelength and measuring the Raman signal at a fixed wavelength using a single-pixel detector. Before exploring the potential of quicker acquisition, it is important to first study the equivalence of Raman Excitation Spectroscopy and conventional Raman Spectroscopy experimentally. We demonstrate the equivalence between the two techniques for biological and non-biological samples by measuring Raman Excitation Maps. Additionally, we explore the interpolation of low-resolution excitation spectra using excess information in the maps for higher resolution excitation spectra. This work, together with current progression in fast tunable lasers and highly sensitive single-pixel detectors, provides a promising prospect for dedicated Raman Excitation Spectroscopy instruments to be developed.

Separating Vibrational Coherences in Ground/Excited Electronic States of Solvent/Solute Following Non-Resonant/Resonant Impulsive Excitation.

In a quest to track down the origin of coherent vibrational motions observed in femtosecond pump-probe transients, whether they arise from ground/excited electronic state of solute or are contributed by the solvent, we demonstrate a method for extricating vibrations under resonant and non-resonant impulsive excitations using a diatomic solute in condensed phase (iodine in carbon tetrachloride) with aid of spectral dispersion of the chirped broadband probe. Most importantly, we show how a sum over intensities for a select region of detection wavelengths and Fourier transform of data over select temporal window untwine contributions from vibrational modes of different origins. Thus, in a single pump-probe experiment, vibrational features specific to solute as well as solvent are disentangled that are otherwise spectrally overlapping and are non-separable in conventional (spontaneous/stimulated) Raman spectroscopy employing narrowband excitation. We envision wide-ranging applications of this method to unveil vibrational features in complex molecular systems.

Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy.

We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Mineral Assemblage of Olivine-Hosted Melt Inclusions in a Mantle Xenolith from the V. Grib Kimberlite Pipe: Direct Evidence for the Presence of an Alkali-Rich Carbonate Melt in the Mantle Beneath the Baltic Super-Craton

This report deals with the first mineralogical examination of secondary crystallized melt inclusions (CMIs) in healed cracks within olivine in a mantle peridotite xenolith from the V. Grib kimberlite pipe (Arkhangelsk diamondiferous province). In contrast to micro/nano-inclusions in diamonds, the studied CMIs are quite large (up to 50 µm), so that the mineral composition of the CMIs can be determined via conventional analytical approaches, e.g., Raman spectroscopy and scanning electron microscopy. Garnet peridotite is a coarse-grained mantle rock that equilibrates at 3.3 GPa and 750 °C (corresponding to a depth of ~100 km). The CMIs are therefore tiny snapshots of melt that existed in the shallow lithospheric mantle and were entrapped in olivine. In total, nineteen mineral species were identified among the daughter magmatic minerals of the CMIs. Various Na-K-Ca-, Na-Ca-, Na-Mg-, Ca-Mg-, Mg- and Ca-carbonates; Na-Mg-carbonates with the additional anions Cl−, SO42− and PO43−; alkali sulfates; chlorides; phosphates; sulfides; oxides; and silicates were established. Within the mineral assemblage, carbonates were predominant, with their abundance being more than 62 vol.%. The CMIs contained twelve alkali-rich minerals; nine of them were Na-bearing and showed bulk molar (Na + K)/Ca ≥ 1. The CMIs’ parental melt was an alkali-rich carbonate liquid that contained low amounts of SiO2 (≤9.6 wt%) and H2O (≤2.6 wt%). According to our estimates, the time of complete equilibration between olivine within the healed cracks and host olivine in the mantle at the calculated P-T parameters for the studied xenolith should be no more than several years. Based on this geologically short time span, a genetic link between the studied CMIs and the magmatism that formed the V. Grib kimberlite pipe is suggested.

Fast-Training Deep Learning Algorithm for Multiplex Quantification of Mammalian Bioproduction Metabolites via Contactless Short-Wave Infrared Hyperspectral Sensing.

Within the biopharmaceutical sector, there exists the need for a contactless multiplex sensor, which can accurately detect metabolite levels in real time for precise feedback control of a bioreactor environment. Reported spectral sensors in the literature only work when fully submerged in the bioreactor and are subject to probe fouling due to a cell debris buildup. The use of a short-wave infrared (SWIR) hyperspectral (HS) cam era allows for efficient, fully contactless collection of large spectral datasets for metabolite quantification. Here, we report the development of an interpretable deep learning system, a convolution metabolite regression (CMR) approach that detects glucose and lactate concentrations using label-free contactless HS images of cell-free spent media samples from Chinese hamster ovary (CHO) cell growth flasks. Using a dataset of <500 HS images, these CMR algorithms achieved a competitive test root-mean-square error (RMSE) performance of glucose quantification within 27 mg/dL and lactate quantification within 20 mg/dL. Conventional Raman spectroscopy probes report a validation performance of 26 and 18 mg/dL for glucose and lactate, respectively. The CMR system trains within 10 epochs and uses a convolution encoder with a sparse bottleneck regression layer to pick the best-performing filters learned by CMR. Each of these filters is combined with existing interpretable models to produce a metabolite sensing system that automatically removes spurious predictions. Collectively, this work will advance the safe and efficient adoption of contactless deep learning sensing systems for fine control of a variety of bioreactor environments.

Coherent Anti-Stokes Raman Scattering Cell Imaging and Segmentation with Unsupervised Data Analysis

Offering the high spectral resolution of conventional Raman spectroscopy combined with reduced acquisition time, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses has been accepted as a mature and straightforward technology for label-free bioimaging. In a recently published paper, Philippe Leproux and associates have introduced the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). Leproux spoke to Spectroscopy about combining the techniques, as well as discussing the potential of the results.