Coherent anti-Stokes Raman Scattering Spectra Research Articles

Log-Gaussian gamma processes for training Bayesian neural networks in Raman and CARS spectroscopies.

We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This addresses the challenge of limited real observations in various applications. We apply this methodology to both Raman and coherent anti-Stokes Raman scattering (CARS) spectra, using experimental spectra to estimate gamma process parameters. Parameter estimation is performed using Markov chain Monte Carlo methods, yielding a full Bayesian posterior distribution for the model which can be sampled for synthetic data generation. Additionally, we model the additive and multiplicative background functions for Raman and CARS with Gaussian processes. We train two Bayesian neural networks to estimate parameters of the gamma process which can then be used to estimate the underlying Raman spectrum and simultaneously provide uncertainty through the estimation of parameters of a probability distribution. We apply the trained Bayesian neural networks to experimental Raman spectra of phthalocyanine blue, aniline black, naphthol red, and red 264 pigments and also to experimental CARS spectra of adenosine phosphate, fructose, glucose, and sucrose. The results agree with deterministic point estimates for the underlying Raman and CARS spectral signatures.

Nitrogen thermometry in an inductively coupled plasma torch using broadband nanosecond coherent anti-Stokes Raman scattering.

The development of atmospheric hypersonic flight and re-entry capabilities requires the characterization of the thermo-chemical state of representative test environments. This study demonstrates the usage of multiplex nanosecond N 2 coherent anti-Stokes Raman scattering (CARS) to measure temperatures in an atmospheric, high-temperature (>6000K), air plasma plume, generated by an inductively coupled plasma torch. These are some of the highest temperatures ever accessed via gas-phase CARS, to our knowledge. Temperatures of N 2 in the equilibrium plasma plume are determined via theoretical fits to measured CARS spectra. We discuss the practical implementation of CARS at very high temperatures, including the scaling of the N 2 CARS signal strength from 300 to 6700K, where the expected peak signal from the high-temperature plasma torch gases is two orders of magnitude less than commonly encountered in combustion environments. An intensified CCD camera enables single-laser-shot detection at temperatures as high as 6200K, by increasing sensitivity and providing a time gate against intense background luminosity. We also discuss the impacts of unwanted two-beam CARS contributions from outside the nominal three-beam measurement volume. We present mean axial and radial temperature profiles, as well as time-series data derived from both single-laser-shot and accumulated CARS spectra. The single-laser-shot precision is 1.7%-2.6% at temperatures of 3500 to 6200K. The presented results pave the way for the use of CARS at very high temperatures and the measurement of spatially resolved interface processes in high-enthalpy flows.

Combination of deep learning and 2D CARS figures for identification of amyloid-β plaques.

In vivo imaging and accurate identification of amyloid-β (Aβ) plaque are crucial in Alzheimer's disease (AD) research. In this work, we propose to combine the coherent anti-Stokes Raman scattering (CARS) microscopy, a powerful detection technology for providing Raman spectra and label-free imaging, with deep learning to distinguish Aβ from non-Aβ regions in AD mice brains in vivo. The 1D CARS spectra is firstly converted to 2D CARS figures by using two different methods: spectral recurrence plot (SRP) and spectral Gramian angular field (SGAF). This can provide more learnable information to the network, improving the classification precision. We then devise a cross-stage attention network (CSAN) that automatically learns the features of Aβ plaques and non-Aβ regions by taking advantage of the computational advances in deep learning. Our algorithm yields higher accuracy, precision, sensitivity and specificity than the results of conventional multivariate statistical analysis method and 1D CARS spectra combined with deep learning, demonstrating its competence in identifying Aβ plaques. Last but not least, the CSAN framework requires no prior information on the imaging modality and may be applicable to other spectroscopy analytical fields.

Analysis of picosecond coherent anti-Stokes Raman spectra for gas-phase diagnostics

We present a hybrid frequency- and time-domain solution, applicable to the case of picosecond coherent anti-Stokes Raman scattering (CARS), for gas-phase diagnostics. A solution has been derived based on both physical arguments and four-wave mixing equations for picosecond CARS, with pulse durations that are comparable to the dephasing time scale for gas-phase Raman coherence—a regime where commonly employed solutions for impulsive (femtosecond) or cw (nanosecond) pump/Stokes forcing are not strictly valid. We present the ps-CARS spectrum in the form of incoherent sums of CARS intensity spectra, calculated from the fundamental solution for impulsive pump/Stokes Raman preparation. The solution was examined for temperatures from 1000–3000 K, for four plausible experimental configurations, with laser pulse durations of 50–150 ps, and probe pulse delays from −20 to 240 ps. Approximations based on cw and impulsive pump/Stokes preparation to fit picosecond CARS spectra at atmospheric pressure were examined and the relative thermometric accuracy and computational cost of these approximations were quantified for the case of a zero nonresonant CARS contribution, and a nonresonant susceptibility equal to 10% of the Raman-resonant value at the N2 bandhead. The nanosecond CARS approximation can result in large fitting errors when the probe pulse time delay is less than the probe pulse duration. Errors as large as 10–20% are observed in the fit temperatures for a zero picosecond probe pulse delay, when the nonresonant background is neglected, largely due to an inability of the time-independent cw model to capture transient frequency spread dephasing effects at the Q-branch bandhead. The inclusion of a nonresonant background results in 40–60% thermometry errors with a nanosecond model at a zero-probe delay. Time-dependent impulsive calculations used for femtosecond CARS better approximate the structure of the N2 bandhead, reducing temperature fitting errors to 5–10% at a short probe pulse delay. The impulsive approximation results in errors up to 10% at intermediate probe pulse delays, where the coherence of the pump and probe pulses leads to multiple terms in the picosecond CARS solution. Both approximations improve as the probe pulse delay exceeds the probe duration. The nanosecond approximation results in a 2–3% error, while the impulsive model results in differences of less than 1% in some cases. Fits to experimental data obtained using short, ∼60ps pulses at a zero probe time delay and longer 100 ps pulses at a substantial 200 ps delay are presented with accuracies of 1–3% in the fit temperature.

Discriminating Congested Vibrational Peaks of Condensed Organic Materials with Time- and Frequency-Resolved Coherent Anti-Stokes Raman Scattering Spectroscopy

The spectral congestion of highly overlapping vibrational peaks of molecules in condensed phases is a persistent challenge in conventional linear vibrational spectroscopy, making it difficult to accurately determine the spectroscopic parameters. This study demonstrates the utility of time- and frequency-resolved coherent anti-Stokes Raman scattering (CARS) spectroscopy with a time-delayed picosecond probe pulse in resolving congested C-H stretching vibrational peaks of condensed organic matters. The results show that the overlapping vibrational peaks of polymeric films and oily liquids, which are not easily distinguishable in spontaneous Raman spectroscopy, can be separated in the time-resolved CARS (tr-CARS) spectra. To understand the physical basis of the enhanced spectral resolution, we examine the time series of CARS spectra obtained by varying the delay time between the pump and probe pulses. The global fit analysis indicates that the effective suppression of faster Raman free-induction-decay components and instantaneous nonresonant background signals contributes to improved spectral resolution. Additionally, the present study reveals that the CARS spectra at a sufficient probe delay time are highly sensitive to the incident and detection polarizations, further improving vibrational peak distinguishability through polarization-controlled tr-CARS.

Background suppression for CARS thermometry in highly luminous flames using an electro-optical shutter.

An electro-optical shutter (EOS), comprising a Pockels cell located between crossed-axis polarizers, is integrated into a nanosecond coherent anti-Stokes Raman scattering (CARS) system. The use of the EOS enables thermometry measurements in high-luminosity flames through significant reduction of the background resulting from broadband flame emission. A temporal gating ≤100 ns along with an extinction ratio >10,000:1 are achieved using the EOS. Integration of the EOS enables the use of an unintensified CCD camera for signal detection, improving upon the signal-to-noise ratio achievable with inherently noisy microchannel plate intensification processes previously employed for short temporal gating. The reduction in background luminescence afforded by the EOS in these measurements allows the camera sensor to capture CARS spectra at a broad range of signal intensities and corresponding temperatures, without saturation of the sensor, thus enhancing the dynamic range of these measurements.

Coherent Anti-Stokes Raman Scattering Measurements of Time and Length Scales of Temperature Fluctuations in a Turbulent Flame.

The ability to derive temporal and spatial scales of "instantaneous" local temperature variations in a turbulent flame by means of coherent anti-Stokes Raman scattering (CARS) spectroscopy is demonstrated, for the first time to our knowledge. The measurements employed two CARS spectrometers with synchronized nanosecond pulse-repetitive lasers. The system was enabling to record, with a high temporal resolution of about 10 ns, series of single laser shot CARS spectra of N2 molecules from two spatially overlapped or displaced probe volumes as small as 0.03 × 0.03 × 2 mm3. The spectra were being recorded at a variable delay between two sequential shots, following each other in pairs at a repetition rate of 10 Hz. The series of 500 coupled measurements, at the delays in the range 1 μs-10 ms and the displacements up to 2.5 mm, have been performed in a few points of an open premixed methane-air flame of a laboratory burner with the time-averaged temperatures in the range 1200-1800 K. From the spectra, "instantaneous" temperatures, at the given delay and probe volume distance, have been derived. This allowed the auto-correlation coefficients of temperature fluctuations versus the delay and the displacement to be calculated. These dependences enabled to evaluate temperature correlation times and lengths under various mixture flow rates and equivalence ratios.

Label-Free Identification of Spore-Forming Bacteria Using Ultrabroadband Multiplex Coherent Anti-Stokes Raman Scattering Microspectroscopy.

Spore-forming bacteria accumulate dipicolinic acid (DPA) to form spores to survive in extreme environments. Vibrational spectroscopy is widely used to detect DPA and elucidate the existence of the bacteria, while vegetative cells, another form of spore-forming bacteria, have not been studied extensively. Herein, we applied coherent anti-Stokes Raman scattering (CARS) microscopy to spectroscopically identify both spores and vegetative cells without staining or molecular tagging. The spores were identified by the strong CARS signals due to DPA. Furthermore, we observed bright spots in the vegetative cells in the CARS image at 1735 cm-1. The vegetative cells contained molecular species with C=O bonds because this vibrational mode was associated with the carbonyl group. One of the candidate molecular species is diketopimelic acid (DKP), a DPA precursor. This hypothesis was verified by comparing the spectrum obtained by the vegetative cells with that of the DKP analogue (ketopimelic acid) and with the result obtained by DFT calculation. The results indicate that the observed vegetative cell is in the sporulation process. CARS spectra can be used to monitor the maturation and preformation of spores.

Temperature measurement utilizing the vibrational CARS signal from the ground level X2Σg+ of N2+ in high-temperature flames

We analyze the generation of the vibrational coherent anti-Stokes Raman scattering (CARS) signal from the ground level X2Σg+ of N2+ and demonstrate the feasibility for gas-phase thermometry utilizing the vibrational CARS signals of N2+ in high-temperature flames. The vibrational CARS signal of N2+ is obtained using the filamentation-based and two-beam hybrid femtosecond/picosecond CARS system and is employed to extract the fitting temperatures in high-temperature flames. The accuracy and precision of the fitting temperatures from the 1000 consecutive single-shot vibrational CARS spectra of N2+ at a target temperature of 1706 K are 1.23% and 5.76%, respectively, which are similar to the fitting results of N2 in previous filamentation-based CARS thermometry and could also demonstrate that the measured temperature in the filamentation is indeed not affected by the ionization. We further introduce the CARS signal of N2+ in multiple species measurement to obtain more details about the CARS process during filamentation. This work would provide an optional target molecule for CARS thermometry and help researchers further understand the molecular dynamics of N2+ ions during the filamentation.

Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra.

The nonresonant background (NRB) contribution to the coherent anti-Stokes Raman scattering (CARS) signal distorts the spectral line shapes and thus degrades the chemical information. Hence, finding an effective approach for removing NRB and extracting resonant vibrational signals is a challenging task. In this work, a bidirectional LSTM (Bi-LSTM) neural network is explored for the first time to remove the NRB in the CARS spectra automatically, and the results are compared with those of three DL models reported in the literature, namely, convolutional neural network (CNN), long short-term memory (LSTM) neural network, and very deep convolutional autoencoders (VECTOR). The results of the synthetic test data have shown that the Bi-LSTM model accurately extracts the spectral lines throughout the range. In contrast, the other three models' efficiency deteriorated while predicting the peaks on either end of the spectra, which resulted in a 60 times higher mean square error than that of the Bi-LSTM model. The Pearson correlation analysis demonstrated that Bi-LSTM model performance stands out from the rest, where 94% of the test spectra have correlation coefficients of more than 0.99. Finally, these four models were evaluated on four complex experimental CARS spectra, namely, protein, yeast, DMPC, and ADP, where the Bi-LSTM model has shown superior performance, followed by CNN, VECTOR, and LSTM. This comprehensive study provides a giant leap toward simplifying the analysis of complex CARS spectroscopy and microscopy.

Semi-synthetic data generation to fine-tune a convolutional neural network for retrieving Raman signals from CARS spectra

Coherent anti-Stokes Raman scattering (CARS) is a well-known third-order non-linear spectroscopic technique utilized to analyze the molecule structures. However, it has a coherent non-resonant background (NRB) alongside with Raman resonant, thus, it is difficult to extract the Raman part. In this work, the Raman signal is extracted from the CARS spectrum by using a convolutional neural network. The model architecture is adapted from the original SpecNet model. The model is pre-trained with synthetic data and fine-tuned with semi-synthetic data based on two sets of semi-synthetic spectra. The experimental results show that the model achieves 86% accuracy in predicting the Raman signal of semi-synthetic data. In addition, the sensitivity of the model performance to varying levels of noise is analysed. Based on standard metrics, the model performance decreases with the increasing level of noise in a non-linear manner. Finally, the prediction capability of the fine-tuned SpecNet model was evaluated on the four experimental CARS spectra and the results were found to be better compared to SpecNet.

Bessel Beam Coherent Anti-Stokes Raman Scattering Spectroscopy for Turbulent Flow Diagnosis.

Coherent anti-Stokes Raman scattering (CARS) spectroscopy plays an important role in chemical analysis for transient flow dynamics. Due to the turbulent ambient conditions, the CARS spectrum often suffers from a poor signal-to-noise ratio (SNR) and cannot provide a convincing measurement. Here, we report on a CARS spectroscopic method using a Bessel beam to enhance the spectral fidelity and SNR in a quasi-turbulent environment. Compared with traditional CARS, the measurement accuracy is significantly improved by taking advantage of the anti-scattering and self-healing characteristics of the Bessel beam. Our preliminary results indicate that Bessel beam CARS could be a promising method for high precision turbulent flow measurement fields.

Novel exploration of Raman microscopy and non-linear optical imaging in adenomyosis.

Adenomyosis is a common gynecological disease in women. A relevant literature search found that approximately 82% of patients with adenomyosis chose to undergo hysterectomy. However, women of childbearing age are more likely to undergo surgery to preserve the uterus. Because it is difficult to determine the extent of adenomyosis, it is almost impossible to resect adenomyotic tissue and retain the uterus at the same time. Following ethics approval and patient consent, tissue samples were resected and prepared to create frozen slices for analysis. One slice was subjected to H&E staining while the remaining slices were photographed with Coherent Anti-Stokes Raman Scattering (CARS), Second-Harmonic Generation (SHG) microscopy, and Raman spectroscopy. Comparative observations and analyses at the same positions were carried out to explore the diagnostic ability of CARS, SHG, and Raman spectroscopy for adenomyosis. In adenomyotic tissue, we found two characteristic peaks at 1,155 and 1,519 cm-1 in the Raman spectrum, which were significantly different from normal tissue. The substances shown in the CARS spectrum were represented by peaks of 1,519 cm-1. SHG microscopy showed a distribution of collagen at the focus of the adenomyosis. This study represents a novel analysis of Raman microscopy, CARS, and SHG in the analysis of adenomyotic lesions. We found the diffraction spectrum useful in determining the focal boundary and the diagnosis of adenomyosis in the tested samples.

Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising.

We introduce a broadband coherent anti-Stokes Raman scattering (CARS) microscope based on a 2-MHz repetition rate ytterbium laser generating 1035-nm high-energy (≈µJ level) femtosecond pulses. These features of the driving laser allow producing broadband red-shifted Stokes pulses, covering the whole fingerprint region (400-1800 cm-1), employing supercontinuum generation in a bulk crystal. Our system reaches state-of-the-art acquisition speed (<1 ms/pixel) and unprecedented sensitivity of ≈14.1 mmol/L when detecting dimethyl sulfoxide in water. To further improve the performance of the system and to enhance the signal-to-noise ratio of the CARS spectra, we designed a convolutional neural network for spectral denoising, coupled with a post-processing pipeline to distinguish different chemical species of biological tissues.

Raman signal extraction from CARS spectra using a learned-matrix representation of the discrete Hilbert transform.

Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra due to interference with the nonresonant background (NRB) is vital for quantitative analysis. Popular computational approaches, the Kramers-Kronig relation and the maximum entropy method, have demonstrated success but may generate significant errors due to peaks that extend in any part beyond the recording window. In this work, we present a learned matrix approach to the discrete Hilbert transform that is easy to implement, fast, and dramatically improves accuracy of Raman retrieval using the Kramers-Kronig approach.

CARS spectra of polystyrene sheet at excitation of fs pulses and chirped pulses

Two-beam coherent anti-Stokes Raman scattering (CARS) spectroscopy setup is built. Measurements of CARS spectra generated from polystyrene (PS) sheet under excitation of femtosecond (fs) pulses and chirped pulses are demonstrated. The pump-Stokes time delay is adjusted at fixed step of 6 fs and transient CARS spectra is measured at every pump-Stokes time delay. Two-dimensional plots depicting intensity of fs CARS, chirped Stokes CARS, chirped pump/probe CARS and spectral focusing CARS as a function of the wavenumber and the pump-Stokes time delay are realized. Excitation situation of Raman resonances in fingerprint region of polystyrene molecule is discussed in conditions of un-chirped and chirped CARS spectra. Capacity of chirped Stokes CARS, chirped pump/probe CARS and spectral focusing CARS in improving spectral resolution over fs CARS is systematically analyzed.

Convolutional neural network-based retrieval of Raman signals from CARS spectra

We report the studies on the automatic extraction of the Raman signal from coherent anti-Stokes Raman scattering (CARS) spectra by using a convolutional neural network (CNN) model. The model architecture is adapted from literature and retrained with synthetic and semi-synthetic data. The synthesized CARS spectra better approximate the experimental CARS spectra. The retrained model accurately predicts spectral lines throughout the spectral range, even with minute intensities, which demonstrates the potential of the model. Further, the extracted Raman line-shapes are in good agreement with the original ones, with an RMS error of less than 7% on average and have shown correlation coefficients of more than 0.9. Finally, this approach has a strong potential in accurately estimating Raman signals from complex CARS data for various applications.

Delay-Spectral Focusing Dual-Comb Coherent Raman Spectroscopy for Rapid Detection in the High-Wavenumber Region

Rapid multiplex coherent anti-Stokes Raman scattering (CARS) spectroscopy in the high-wavenumber (HW) region shows great advantages in real-time dynamic process visualizations, clinical diagnosis, abundant microplastic assessment, etc. Fourier transform CARS (FT-CARS) improves the acquisition speed of multiplex CARS to the order of tens of kHz, yet typical approaches to utilize the intrinsic coherence of ultrabroadband pulses notably impede the attainable Raman vibrations in the HW region. Here, a novel delay-spectral focusing dual-comb (DC) CARS scheme is proposed for rapid HW Raman detection based on two fiber combs with 100 MHz repetition rates. By combining the particular advantages of DC asynchronous optical sampling and spectral focusing instantaneous single molecular vibration excitation, the Raman spectrum directly maps to DC relative delay, releasing the coherence constraint of excitation sources. Thus, an Er comb and a Yb comb, as pump and Stokes excitation pulses, respectively, flexibly match the Raman vibration in the HW region. Further, a rapid delay focusing method with intracavity electro-optic modulation is applied to actively control the DC relative delay scanning in the Raman shift region of interest. With these efforts, the spectral acquisition rate is improved more than 1000-fold up to 40 000 spectra/s, while keeping spectral resolution (∼10 cm–1) and the signal-to-noise ratio (∼260) stable along with active acquisition rate tuning. Combined with a microfluidic device, high-speed measurement in the HW region of 15 μm microbeads in flow is demonstrated by the system, reaching a high throughput of around 2150 events/s and more than 99% classification consistency. The prospect of rapid acquiring multiplex CARS spectra without the sacrifice of spectral resolution or the need for the coherence of the pulse sources offers huge potential in rapid monitoring circumstances, such as flow cytometry and microspectroscopic imaging in biomedicine.

Label-free enzymatic reaction monitoring in water-in-oil microdroplets using ultra-broadband multiplex coherent anti-Stokes Raman scattering spectroscopy.

We propose a system for monitoring an enzymatic reaction, i.e., dehydrogenation of ethanol catalyzed by alcohol dehydrogenase, in microdroplets using ultra-broadband multiplex coherent anti-Stokes Raman scattering (CARS) spectroscopy. The reaction solution was encapsulated in water-in-oil microdroplets with diameters of 50 µm. The reaction was monitored by measuring the concentration of coenzymes from the CARS spectrum obtained in one-second exposure time. The results obtained using our system was consistent with those of the conventional fluorescence measurement system and indicate the potential of CARS spectroscopy for droplet-based high-throughput screening of enzymes.

Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks.

We report the retrieval of the Raman signal from coherent anti-Stokes Raman scattering (CARS) spectra using a convolutional neural network (CNN) model. Three different types of non-resonant backgrounds (NRBs) were explored to simulate the CARS spectra viz (1) product of two sigmoids following the original SpecNet model, (2) Single Sigmoid, and (3) fourth-order polynomial function. Later, 50 000 CARS spectra were separately synthesized using each NRB type to train the CNN model and, after training, we tested its performance on 300 simulated test spectra. The results have shown that imaginary part extraction capability is superior for the model trained with Polynomial NRB, and the extracted line shapes are in good agreement with the ground truth. Moreover, correlation analysis was carried out to compare the retrieved Raman signals to real ones, and a higher correlation coefficient was obtained for the model trained with the Polynomial NRB (on average, ∼0.95 for 300 test spectra), whereas it was ∼0.89 for the other NRBs. Finally, the predictive capability is evaluated on three complex experimental CARS spectra (DMPC, ADP, and yeast), where the Polynomial NRB model performance is found to stand out from the rest. This approach has a strong potential to simplify the analysis of complex CARS spectroscopy and can be helpful in real-time microscopy imaging applications.