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Raw-Material Authentication Using a Handheld Raman Spectrometer
Pharmaceutical manufacturing facilities are moving toward 100% inspection of incoming raw materials to confirm the content of each container is verifiable at the molecular level. Current practice requires incoming raw-material containers to be opened and samples to be extracted. The materials are then typically transported to a laboratory for chemical analysis, a process that may take several days or longer, during which time the material is unavailable for production. Technologies used for laboratory identification tests include high-pressure liquid chromatography (HPLC), near-infrared (NIR) and mid-infrared (mid-IR) spectroscopies, and other wet chemical methods (1–6). Among these methodologies, Raman spectroscopy has proven efficient and effective for a wide range of pharmaceutical applications, including identity testing of pharmaceutical raw materials, in-process analysis, and authentication of final dosage forms (7–9).
Operational attributes are important to take into account when selecting a measurement technology for a research or manufacturing environment. In the context of material identification, portable solutions (e.g., the handheld Raman instrumentation used in this study) can quickly verify material identity at the point of need. Furthermore, Raman spectra can be acquired through transparent packaging materials such as the plastic bags commonly used to line drums containing incoming raw materials (10, 11). This means of testing reduces the time between receipt and availability to the production line, minimizes handling and, for materials packaged in transparent media, eliminates the risk of contamination posed when the packaging seal is breached. NIR measurements also can be acquired through transparent packaging; however, special care must be taken when making NIR measurements through plastic containers because subtle variations from one container to another can manifest themselves quite markedly in the spectra. In contrast to Raman and NIR, mid-IR techniques require direct contact with the material and are not conducive to measurement through packaging.
Some challenges with using other types of spectroscopy, notably NIR, have been method development and method transfer between instruments. As Figure 2 shows, Raman spectra have distinct peaks that are characteristic of the material producing the spectrum. Therefore, like Fourier transform infrared (FTIR) spectroscopy, the intrinsic molecular selectivity means that spectral differences between materials are extremely pronounced relative to nuisance factors. This article describes how Raman methods generated from measurements on one unit can be easily transferred to another, simply by transferring the reference library files.
NIR spectra are less distinct, with broader peaks that result in poorer selectivity, and sometimes require computationally intensive methods to detect differences. As a result, the creation, transfer, and maintenance of NIR methods often require expert oversight and intervention. Variability among various NIR units' optics and other components is sometimes of the same order as the differences among samples tested. As a result, in transferring an NIR method to a different instrument, additional reference spectra and/or tuning of method parameters may be required.
To create methods, reference spectra first had to be acquired. The reference spectra for the materials were taken on three TruScan devices (hereafter referred to as "reference devices") using identical data collection software as the test devices described above. In the context of TruScan, methods are analytical tasks based on stored reference spectra that the instrument's software executes to determine whether a material's identity can be verified. For each material, a single reference spectrum was acquired by one of the three reference devices.
To collect each reference spectrum, a sample of the material was placed in a borosilicate glass vial (VWR, West Chester, PA) and the data were collected through the wall of the vial. The laser aperture of the instrument was placed at the appropriate distance from the vial using either the nose-cone or vial-holder attachment for correct spacing. For most materials, the acquisition process was initiated and simply allowed to continue until terminated automatically by the unit's library acquisition software. In contrast, cellulose, dextrin, trimagnesium phosphate, zinc sulfate, and calcium sulfate are slightly fluorescent, so special care had to be taken to avoid photobleaching the sample during reference measurement. This can be achieved by terminating the reference scan before the software automatically discontinues measurement or by periodically moving the sample throughout data collection. Finally, while reference spectra were collected, it was determined that colloidal silica, talc, sodium carboxy methyl cellulose, and hydroxy propyl methyl cellulose did not provide an adequate signal to be measured in a practical period of time for handheld deployment. In particular, colloidal silica had a very weak Raman signal, and the other three materials were too fluorescent to allow reliable determinations to be made.
Methods were created from the reference spectra by the associated web-based software utility and then loaded into the test instruments. To prepare test samples to challenge the methods, samples of approximately 2 g each of the 28 remaining materials were sealed in 2-m-thick polyethylene bags in an effort to emulate expected-use scenarios (measurement through plastic bags) for incoming material inspection. Three measurements of each sample were made, one measurement with each of the three test devices. Each test measurement was made using the automatic (or "auto") mode, where the unit's software controls data acquisition parameters to achieve the necessary spectral signal-to-noise ratio (SNR) in the shortest measurement time possible. The auto-mode measurements used when running a method compensate for differences in operator positioning, stray-light, lot-to-lot sample fluorescence, and so forth. Each "unknown" measured spectrum (three for each material) was evaluated against each method for the test materials using the probability based approach described previously, resulting in a p-value for each unknown-method pair.
Results and discussion
The diagonal elements of Figure 6 represent cases in which the material was tested against the corresponding method, whereas the off-diagonal entries represent materials being tested by the "wrong" method. The dark-green squares on the diagonal show where the measured spectrum matched the corresponding reference spectrum with a p-value >0.1, which indicate that these materials easily pass as being consistent with the method reference spectrum. Two materials, ethyl cellulose and hydroxypropyl cellulose, resulted in average p-values in the range of 0.01 to 0.1 when tested against themselves. As also shown in Figure 6, examination of individual results from each of the three instruments revealed that these two samples, numbers 10 and 11 (indicated by daggers), were the only ones that resulted in differences in pass–fail decisions among the three test instruments. For ethyl cellulose, the p-values were 0.03, 0.06, and 0.04, and for hydroxypropyl cellulose, the p-values were 0.02, 0.05, and 0.11 across the three test units, respectively. Thus, both materials were very close to the borderline threshold of 0.05 and either just passed or just failed.
As shown in Table II, ethyl cellulose and hydroxypropyl cellulose also correspond to the longest required measurement times, 284 s and 390 s, respectively. Further inspection of the spectral data (not shown) revealed subtle features in the unknown measurements that were not present in the reference measurements. To determine whether the polyethylene containers could be responsible, an auto-mode spectrum for each of these two materials was acquired through borosilicate glass vials. These spectra were consistent with the reference measurements and did not contain any extra features. In addition, a spectrum of polyethylene was acquired by folding an empty polyethylene bag over on itself several times to produce a sample of sufficient thickness for measurement. The resulting spectrum (not shown) contained bands in the spectral regions where the extra peaks in the unknown were found. Thus, despite the ability of Raman to sample through packaging, the extremely weak signal and long measurement times for these two cellulose materials resulted in conditions favorable to allow the plastic to subtly interfere with the measurement. It is likely that measurements could be successfully made through glass, but this was not attempted because the required measurement time may not be considered practical for routine field use.
Examination of the off-diagonal elements in Figure 6 confirms the excellent selectivity of the technology as evidenced by the overwhelming majority of off-diagonal elements with p < 10–15. The only area in the table where there is a lack of acceptable selectivity is for the alkali metal stearate materials. Stearic acid is differentiable from both calcium and magnesium stearate (p < 0.01); however, calcium stearate and magnesium stearate cannot be readily differentiated from one another. Whereas many materials differing only in their cation can be readily differentiated with the handheld Raman system (e.g., in the study, sodium and potassium bicarbonate, calcium, and zinc sulfate), calcium stearate and magnesium stearate are simply too similar from a spectroscopic standpoint. This is likely a result of the change in cation not having an appreciable long-range influence on the relatively large anion (stearate) from which the Raman signal is actually generated.
Handheld Raman spectroscopy is an excellent alternative to traditional incoming raw-material inspection by high-pressure liquid chromatography, wet chemical methods, and NIR and mid-IR spectroscopy. The technology has excellent specificity, which, coupled with intelligent on-board algorithms, reduce the time and effort required to develop and validate methods. Furthermore, methods can be successfully loaded onto different handheld Raman instruments to produce consistent data and material identification on the multiple instruments, without loading additional spectra or performing other customization.
In addition to its analytical characteristics, today's handheld Raman solutions are environmentally robust and can be used by expert spectroscopists as well as operations-based personnel. This is in contrast to Raman instruments of the past, which were bulky, slow, expensive, and delicate. Based on the study presented in this article of common pharmaceutical materials, the handheld Raman spectrometer offers an attractive option for 100% inspection of most incoming raw material in pharmaceutical manufacturing facilities.
Dr. Wayne Jalenak is gratefully acknowledged for useful discussions and for preparation of the glycerin samples presented in the introduction.
Robert L. Green* is a research scientist and Christopher D. Brown is a director of system analytics and applications, both at Ahura Scientific, Inc., 46 Jonspin Road, Wilmington, MA 01887,
tel. 978.657.5555, fax 978.657.5921, email@example.com
*To whom all correspondence should be addressed.
Submitted: July 26, 2007. Accepted: Sept. 13, 2007.
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