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.

In recognition of the misuse risks of fentanyl, there is an urgent need to develop a useful and rapid analytical method to detect and monitor the opioid drug. The surface-enhanced shifted excitation Raman difference spectroscopy (SE-SERDS) method has been demonstrated to suppress background interference and enhance Raman signals. In this study, the SE-SERDS method was used for trace detection of fentanyl in beverages. To prepare the simulated illegal drug–beverages, fentanyls were dissolved into distilled water or Mizone as a series of test samples. Based on our previous work, the surface-enhanced Raman spectroscopy detection was performed on the beverages containing fentanyl by the prepared AgNPs and the SE-SERDS spectra of test samples were collected by the dual-wavelength rapid excitation Raman difference spectroscopy system. In addition, the quantitative relationship between fentanyl concentrations and the Raman peaks was constructed by the Langmuir equation. The experimental results show that the limits of quantitation for fentanyl in distilled water and Mizone were 10 ng/mL and 200 ng/mL, respectively; the correlation coefficients for the nonlinear regression were as high as 0.9802 and 0.9794, respectively; and the relative standard deviation was less than 15%. Hence, the SE-SERDS method will be a promising method for the trace analyses of food safety and forensics.

Surface-enhanced coherent anti-Stokes Raman scattering (SECARS) technique has triggered huge interests due to the significant signal enhancement for high-sensitivity detection. Previous SECARS work has tended to focus only on the enhancement effect at a certain combination of frequencies, more suitable for single-frequency CARS. In this work, based on the enhancement factor for broadband SECARS excitation process, a novel Fano resonance plasmonic nanostructure for SECARS is studied. In addition to the 12 orders of magnitude enhancement effect that can be realized under single-frequency CARS, this structure also shows huge enhancement under broadband CARS in a wide wavenumber region, covering most of the fingerprint region. This geometrically-tunable Fano plasmonic nanostructure provides a way to realize broadband-enhanced CARS, with potentials in single-molecular monitoring and high-selectivity biochemical detection.

Impulsive stimulated Brillouin spectroscopy (ISBS) plays a critical role in investigating mechanical properties thanks to its fast measurement rate. However, traditional Fourier transform-based data processing cannot decipher measured data sensitively because of its incompetence in dealing with low signal-to-noise ratio (SNR) signals caused by a short exposure time and weak signals in a multi-peak spectrum. Here, we propose an adaptive noise-suppression Matrix Pencil method for heterodyne ISBS as an alternative spectral analysis technique, speeding up the measurement regardless of the low SNR and enhancing the sensitivity of multi-component viscoelastic identification. The algorithm maintains accuracy of 0.005% for methanol sound speed even when the SNR drops 33 dB and the exposure time is reduced to 0.4 ms. Moreover, it proves to extract a weak component that accounts for 6% from a polymer mixture, which is inaccessible for the traditional method. With its outstanding ability to sensitively decipher weak signals without spectral a priori information and regardless of low SNRs or concentrations, this method offers a fresh perspective for ISBS on fast viscoelasticity measurements and multi-component identifications.

Rapid coherent Raman hyperspectral imaging shows great promise for applications in sensing, medical diagnostics, and dynamic metabolism monitoring. However, the spectral acquisition speed of current multiplex coherent anti-Stokes Raman scattering (CARS) microscopy is generally limited by the spectrometer integration time, and as the detection speed increases, the signal-to-noise ratio (SNR) of single spectrum will decrease, leading to a terrible imaging quality. In this Letter, we report a dual-comb coherent Raman hyperspectral microscopy imaging system developed by integrating two approaches, a rapid delay-spectral focusing method and deep learning. The spectral refresh rate is exploited by focusing the relative delay scanning in the effective Raman excitation region, enabling a spectral acquisition speed of 36 kHz, ≈4 frames/s, for a pixel resolution of 95 × 95 pixels and a spectral bandwidth no less than 200 cm−1. To improve the spectral SNR and imaging quality, the deep learning models are designed for spectral preprocessing and automatic unsupervised feature extraction. In addition, by changing the relative delay focusing region of the comb pairs, the detected spectral wavenumber region can be flexibly tuned to the high SNR region of the spectrum.