Raman spectroscopy can provide valuable information about bone tissue composition in studies of bone development, biomechanics, and health. In order to study the Raman spectra of bone in vivo, instrumentation that enhances the recovery of subsurface spectra must be developed and validated. Five fiber-optic probe configurations were considered for transcutaneous bone Raman spectroscopy of small animals. Measurements were obtained from the tibia of sacrificed mice, and the bone Raman signal was recovered for each probe configuration. The configuration with the optimal combination of bone signal intensity, signal variance, and power distribution was then evaluated under in vivo conditions. Multiple in vivo transcutaneous measurements were obtained from the left tibia of 32 anesthetized mice. After collecting the transcutaneous Raman signal, exposed bone measurements were collected and used as a validation reference. Multivariate analysis was used to recover bone spectra from transcutaneous measurements. To assess the validity of the transcutaneous bone measurements cross-correlations were calculated between standardized spectra from the recovered bone signal and the exposed bone measurements. Additionally, the carbonate-to-phosphate height ratios of the recovered bone signals were compared to the reference exposed bone measurements. The mean cross-correlation coefficient between the recovered and exposed measurements was 0.96, and the carbonate-to-phosphate ratios did not differ significantly between the two sets of spectra (p > 0.05). During these first systematic in vivo Raman measurements, we discovered that probe alignment and animal coat color influenced the results and thus should be considered in future probe and study designs. Nevertheless, our noninvasive Raman spectroscopic probe accurately assessed bone tissue composition through the skin in live mice.

In this study, we report adaptation of Raman spectroscopy for arthroscopy of joint tissues using a custom-built fiber optic probe. Differentiation of healthy and damaged tissue or examination of subsurface tissue, such as subchondral bone, is a challenge in arthroscopy because visual inspection may not provide sufficient contrast. Discrimination of healthy versus damaged tissue may be improved by incorporating point spectroscopy or hyperspectral imaging into arthroscopy where contrast is based on molecular structure or chemical composition. Articular joint surfaces of knee cadaveric human tissue and tissue phantoms were examined using a custom-designed Raman fiber optic probe. Fiber-optic Raman spectra were compared against reference spectra of cartilage, subchondral bone and cancellous bone collected using Raman microspectroscopy. In fiber-optic Raman spectra of the articular surface, there was an effect of cartilage thickness on recovery of signal from subchondral bone. At sites with intact cartilage, the bone mineralization ratio decreased but there was a minimal effect in the bone mineral chemistry ratios. Tissue phantoms were prepared as experimental models of the osteochondral interface. Raman spectra of tissue phantoms suggested that optical scattering of cartilage has a large effect on the relative cartilage and bone signal. Finite element analysis modeling of light fluence in the osteochondral interface confirmed experimental findings in human cadaveric tissue and tissue phantoms. These first studies demonstrate proof of principle for Raman arthroscopic measurement of joint tissues and provide a basis for future clinical or animal model studies.

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing “concentration” or “weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Projective transformation is a mathematical correction (implemented in software) used in the remote imaging field to produce distortion-free images. We present the application of projective transformation to correct minor alignment and astigmatism distortions that are inherent in dispersive spectrographs. Patterned white-light images and neon emission spectra were used to produce registration points for the transformation. Raman transects collected on microscopy and fiber-optic systems were corrected using established methods and compared with the same transects corrected using the projective transformation. Even minor distortions have a significant effect on reproducibility and apparent fluorescence background complexity. Simulated Raman spectra were used to optimize the projective transformation algorithm. We demonstrate that the projective transformation reduced the apparent fluorescent background complexity and improved reproducibility of measured parameters of Raman spectra. Distortion correction using a projective transformation provides a major advantage in reducing the background fluorescence complexity even in instrumentation where slit-image distortions and camera rotation were minimized using manual or mechanical means. We expect these advantages should be readily applicable to other spectroscopic modalities using dispersive imaging spectrographs.