The online biomedical Raman spectroscopic framework developed has been implemented as a graphical user interface (GUI) under the Matlab 2011a (Mathworks Inc., Natick, MA) scripting environment in a fast computing workstation (64 bit I7 quad-core 4GB memory). This framework has been thoroughly optimized for rapid data processing for real-time tissue diagnostics. Hardware components of the rapid Raman system (e.g., laser power control, spectrometer, CCD shutter and camera readout synchronization) have been interfaced to the Matlab software through libraries for different spectrometers/cameras [e.g., PVCAM library (Princeton Instruments, Roper Scientific, Inc., Trenton, NJ) and Omni Driver (Ocean Optics Inc., Dunedin, FL), etc.]. A schematic of the spectral acquisition and processing flow of online diagnostic framework is depicted in Fig. 2. The laser was electronically synchronized with the CCD shutter. The automatic adjustment of laser power, exposure time and accumulation of spectra were realized by scaling to within 85% of the total photon counts (e.g., 55,250 of 65,000 photons) based on preceding tissue Raman measurements, whereas an upper limit of 0.5 s was set to realize clinically acceptable conditions. The accumulation of multiple spectra and automatic adjustment of exposure time provides a rapid and straightforward methodology to prevent CCD saturation and to obtain high signal-to-noise ratios (SNR) for endoscopic applications. The Raman-shift axis (wavelength) was calibrated using a mercury/argon calibration lamp (Ocean Optics Inc., Dunedin, FL). The spectral response correction for the wavelength-dependence of the system was conducted using a standard lamp (RS-10, EG&G Gamma Scientific, San Diego, CA). The reproducibility of the platform can be continuously monitored with the laser frequency and Raman spectra of cyclohexane and acetaminophen as wavenumber standards. All the system performance measures including CCD temperature, integration time, laser power, CCD alignment are accordingly logged into a central database via SQL server. Due to the inter-anatomical and inter-organ spectral variances as we observed earlier,9,29 the online framework we designed implements organ-specific diagnostic models and can instantly switch among the spectral databases of different organs [e.g., esophagus, gastric, colon, cervix, bladder, lung, nasopharynx, larynx, and the oral cavity (hard palate, soft palate, buccal, inner lip, ventral and the tongue)], making this Raman platform a universal diagnostic tool for cancer detection at endoscopy.