Raman spectroscopy is an important and powerful technique for analyzing the chemical composition of biological or nonbiological samples in many fields. A serious challenge frequently encountered in Raman measurements arises from the existence of the concurrent fluorescence background. The fluorescence intensity is normally several orders of magnitude larger than the Raman scattering signal, especially in biological samples. Such fluorescence background must be suppressed in order to obtain accurate Raman spectra. Several different techniques have been explored for this purpose. These techniques could be generally grouped into time-domain, frequency-domain, wavelength-domain, and computational methods in addition to various Raman enhancement techniques and other unconventional methods. This review briefly describes the fundamental principles of each group of methods, reports the most recent advances, and makes comparison across those major categories of techniques in terms of cost and performance in a hope to guide interested readers to select proper methods for specific applications.

A general survey is provided on the capability of Monte Carlo (MC) modeling in tissue optics while paying special attention to the recent progress in the development of methods for speeding up MC simulations. The principles of MC modeling for the simulation of light transport in tissues, which includes the general procedure of tracking an individual photon packet, common light–tissue interactions that can be simulated, frequently used tissue models, common contact/noncontact illumination and detection setups, and the treatment of time-resolved and frequency-domain optical measurements, are briefly described to help interested readers achieve a quick start. Following that, a variety of methods for speeding up MC simulations, which includes scaling methods, perturbation methods, hybrid methods, variance reduction techniques, parallel computation, and special methods for fluorescence simulations, as well as their respective advantages and disadvantages are discussed. Then the applications of MC methods in tissue optics, laser Doppler flowmetry, photodynamic therapy, optical coherence tomography, and diffuse optical tomography are briefly surveyed. Finally, the potential directions for the future development of the MC method in tissue optics are discussed.

Raman spectroscopy has been explored as a promising label-free technique in discriminating apoptosis and necrosis induced cell death in leukemia cells. In addition to Principal component analysis (PCA) as commonly employed in Raman data analysis, another less commonly used but powerful method is Biochemical Component Analysis (BCA). In BCA, a Raman spectrum is decomposed into the contributions from several known basic biochemical components, such as proteins, lipid, nucleic acids and glycogen groups etc. The differences in terms of classification accuracy and interpretability of resulting data between these two methods in Raman spectroscopy have not been systematically investigated to our knowledge. In this study, we utilized both methods to analyze the Raman spectra measured from live cells, apoptotic and necrotic leukemia cells. The comparison indicates that two methods yield comparable accuracy in sample classification when the numbers of basic components are equal. The changes in the contributions of biochemical components in BCA can be interpreted by cell biology principles in apoptosis and necrosis. In contrast, the contributions of most principle components in PCA are difficult to interpret except the first one. The capability of BCA to unveil fine biochemical changes in cell spectra and excellent accuracy in classification can impel the broad application of Raman spectroscopy in biological research.

Raman spectroscopy is a powerful non-destructive technique for qualitatively and quantitatively characterizing materials. However, noise often obscures interesting Raman peaks due to the inherently weak Raman signal, especially in biological samples. In this study, we develop a method based on spectral reconstruction to recover Raman spectra with low signal-to-noise ratio (SNR). The synthesis of narrow-band measurements from low-SNR Raman spectra eliminates the effect of noise by integrating the Raman signal along the wavenumber dimension, which is followed by spectral reconstruction based on Wiener estimation to recover the Raman spectrum with high spectral resolution. Non-negative principal components based filters are used in the synthesis to ensure that most variance contained in the original Raman measurements are retained. A total of 25 agar phantoms and 20 bacteria samples were measured and data were used to validate our method. Four commonly used de-noising methods in Raman spectroscopy, i.e. Savitzky-Golay (SG) algorithm, finite impulse response (FIR) filtration, wavelet transform and factor analysis, were also evaluated on the same set of data in addition to the proposed method for comparison. The proposed method showed the superior accuracy in the recovery of Raman spectra from measurements with extremely low SNR, compared with the four commonly used de-noising methods.

The goal of the work is to experimentally verify Monte Carlo modeling of fluorescence and diffuse reflectance measurements in turbid, tissue phantom models. In particular, two series of simulations and experiments, in which one optical parameter (absorption or scattering coefficient) is varied while the other is fixed, are carried out to assess the effect of the absorption coefficient $(μa)$ and scattering coefficient $(μs)$ on the fluorescence and diffuse reflectance measured from a turbid medium. Moreover, simulations and experiments are carried out for several fiber optic probe geometries that are designed to sample small tissue volumes. Additionally, a group of conversion expressions are derived to convert the optical properties and fluorescence quantum yield measured from tissue phantoms for use in Monte Carlo simulations. The conversions account for the differences between the definitions of the absorption coefficient and fluorescence quantum yield of fluorophores in a tissue phantom model and those in a Monte Carlo simulation. The results indicate that there is good agreement between the simulated and experimentally measured results in most cases. This dataset can serve as a systematic validation of Monte Carlo modeling of fluorescent light propagation in tissues. The simulations are carried out for a wide range of absorption and scattering coefficients as well as ratios of scattering coefficient to absorption coefficient, and thus would be applicable to tissue optical properties over a wide wavelength range (UV-visible/near infrared). The fiber optic probe geometries that are modeled in this study include those commonly used for measuring fluorescence from tissues in practice. © 2003 Society of Photo-Optical Instrumentation Engineers.