Raman spectroscopy's inherent selectivity makes it an effective option for qualitative verification of pharmaceutical raw materials (1–3). Until recently, Raman spectroscopy was difficult to implement because of the technical challenge of detecting the relatively weak but highly selective Raman scattering effect. During the past 10 years, improvements in the sensitivity of charge-coupled device (CCD) detectors, the increased availability of longer-wavelength diode lasers, and developments in Rayleigh rejection filters have made Raman spectroscopy suitable for routine applications. Moreover, miniaturization of optical components has enabled the development of portable, handheld Raman spectrometers capable of robust material verification through transparent packaging materials such as glass and plastic (4).
US Pharmacopoeia General Chapter ‹1120› recognizes Raman spectroscopy as one of the accepted technologies for material identity verification, asserting that "Because the Raman spectrum is specific for a given compound, qualitative Raman measurements may be used as a compendial ID test, as well as for structural elucidation" (5). As this statement suggests, the inherent characteristics of Raman spectra, namely discrete peaks corresponding to specific molecular-bond vibrations, enable verification of a wide range of raw materials.In addition to its high degree of molecular selectivity, Raman spectroscopy is relatively insensitive to physical and environmental factors such as particle size, humidity, and contributions from packaging material. This insensitivity combined with the high molecular selectivity allows for very streamlined method development when using Raman. In fact, a method can be typically based on a single reference spectrum (4).
The high molecular selectivity and external factor insensitivity of Raman spectroscopy help eliminate problems associated with transferring methods between multiple instruments. Careful instrument engineering to provide a consistent spectroscopic platform to acquire and share data, with thorough system response characterization, allow a method developed on one device to be shared seamlessly with any other device, thereby saving time and cost (4).
Portable, field-based alternatives offer many operational advantages over laboratory testing, but it is important to consider challenges arising when such a technology is in use such as the inability to control the testing environment (temperature, humidity, ambient lighting, etc.). Instruments used in the field should be expressly designed for field use and be able to automatically compensate for external variables such as ambient lighting and sampling geometry, and spectroscopic differences in analyzed materials.
An important final consideration when evaluating any analytical methodology is the method performance that can be achieved. In the context of identity testing, specificity (i.e., selectivity) is the only figure of merit that must be demonstrated through comprehensive validation (6). When making an identity assessment on the basis of spectral data, the unknown measurement is examined in relation to one or more reference spectra. The final assessment can be performed manually by comparing peak locations, but usually a full-spectrum automated analysis is performed. Clearly, the algorithmic approach used to render a final decision plays a role in determining the level of specificity that can be achieved when performing an analysis. Common approaches for spectral comparison include hit quality index (HQI) and spectral correlation (7). These methods are useful for quick similarity assessments; unfortunately, they generally do not provide a direct interpretation in the context of spectral identity testing. Furthermore, even when these techniques produce a favorable correlation coefficient, misidentification may occur because these metrics are not particularly sensitive to discrepancies between spectra of interest (4).
An alternative to HQI and spectral correlation is to use a fully multivariate probabilistic approach that looks for spectroscopic discrepancies between the test and reference spectrum in question. Under a null hypothesis that the test and reference spectra are the same within the measurement uncertainty, the comparison is distilled into a traditional p-value (small p-values indicate greater discrepancy between test and reference spectra). If no significant discrepancies are found (i.e., p-value >0.05), then the device deems the test spectrum consistent with the reference spectrum, and a pass result is declared. If discrepancies are found—other than those expected to arise from the uncertainty in the measurement alone—then the test material cannot be authenticated against the reference and a fail result is declared.
In the present study, the broad applicability of portable Raman spectroscopy for raw material identity verification is evaluated by examining a range of 198 Raman-active substances widely used as raw materials in the manufacture of pharmaceutical, consumer health, and personal care products. Each verification method was developed using one reference spectrum in a fraction of the time typically required for near-infrared (NIR) method development. Many diverse categories of materials were tested (see sidebar, "Categories of materials"). A thorough assessment of selectivity was achieved by evaluating each material against all methods to fully assess the performance of each method.