Christopher D. Brown is a director of system analytics and applications, both at Ahura Scientific, Inc.

Christopher D. Brown

Pharmaceutical manufacturers are seeking ways to maintain and improve product quality while reducing manufacturing costs, including in the analytical testing of raw materials. Movement toward 100% inspection of raw materials as well as increased production volumes and the desire for lean manufacturing are additional motivations to find more cost-effective testing technologies.    

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). 

 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 

-values indicate greater discrepancy between test and reference spectra). If no significant discrepancies are found (i.e., 

-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.  

This study used five handheld material-identity verification systems (TruScan, Ahura Scientific, Wilmington, MA, see Figure 1). These instruments use Raman spectroscopy as the analytical technique and contain a 785-nm NIR external cavity-stabilized, thermoelectrically cooled laser as the light source. The laser typically operates at 300 mW output and automatically adjusts if necessary to avoid detector saturation. These instruments also include a single-pass dispersive spectrometer, a cooled CCD detector array, and dielectric edge filters for Rayleigh rejection. Each instrument  has an integrated barcode reader for selecting methods and capturing sample lot/batch/other identifiers. Table I shows specifications for the TruScan Raman material identity verification system.  

Figure 1: TruScan handheld material identity verification system based on Raman spectroscopic technology. 

Acquiring a spectrum is facilitated by using an auto-acquisition mode that monitors incoming data in real time and automatically adjusts the control settings (e.g., laser power, exposure time, number of exposures) such that spectra of comparable statistical resolving power are achieved from one measurement to the next. This adaptive approach to spectroscopy ensures consistent statistical performance of the instrument and embedded decision algorithms regardless of operating conditions. 

Four of the instruments (hereafter called reference devices) were used to acquire reference spectra for the 198 materials included in this study (see sidebar, "Materials tested"). To obtain the reference spectra, a reference sample of each material contained in a borosilicate glass vial (Kimble Glass, Vineland, NJ) was placed at the correct distance from the device's laser aperture using a vial-holder accessory. All data acquisition was performed in auto-mode. For test spectra, most analyses are complete in less than one minute. Reference-grade spectra used for method development are also generated using an auto-mode; however, these spectra have higher standards for signal-to-noise ratio than a standard test spectrum and therefore require a longer data acquisition time. Most auto-mode reference spectra are acquired in less than 30 min. The scans that did not auto-terminate within 45 min were manually stopped at 45 min to prevent photobleaching.  

Reference spectra can be transferred between TruScan devices using a compact flash card or a standard web browser that addresses the device via a TCP/IP connection. Using these approaches, the reference spectra were transferred from the four reference devices to the test device. Once all of the reference spectra had been transferred to the test device, the TCP/IP utility WebAdmin was used to generate a new method for each of the 198 materials using only one reference spectrum for each method. In the context of the TruScan device, 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.  

To challenge the methods, a test sample of each material was placed in a borosilicate glass vial, and the vial was inserted into the test device's vial holder. For each test sample, a spectrum was acquired using auto-mode. The onboard processor then evaluated the spectrum acquired for each test material against the method developed for that material, and also against all the methods created for the rest of the 198 materials, for a total of 39,204 evaluations of test spectra against methods. In addition to the pass and fail results normally presented by the test device, the 

-value was recorded for each test sample–method evaluation pair.  

The handheld Raman material-identity verification system shows a pass result whenever test of a method against a material produces a 

-value ≥0.05. In general, testing a method against the "wrong" material produces a 

-value far less than 0.05, resulting in a fail.  

Figure 2 provides a summary in histogram form of the 

-values generated in this study for all pairings of the 198 materials tested. The green bars represent results obtained when testing materials against methods corresponding to the same materials (true positive testing), and the red bars represent analyses achieved when testing materials against other methods (false-positive testing). To view both sets of results in the same graph, the 

-axis of the histogram represents the fraction of samples in each bin rather than the total frequency of samples in each bin. The results from true positive testing, in green, are all above the pass threshold with 

-values ranging from 0.052 to > 0.5. Though the false-positive testing did reveal a few negative samples with values as high as 0.15, well above the pass threshold and therefore false positives, the frequency with which this occurred is so low that negative samples with 

-values > 0.05 are not visible in the histogram. Conversely, negative samples presented in the histogram reveal that more than 99% of pairings where methods were tested against a different material, the 

 , considerably less than the pass threshold, and a definitive fail result. This demonstrates the extreme degree of selectivity achievable with handheld Raman spectroscopy.  

Figure 2: Histogram of p-values for true positive testing and false positive testing of 198 materials. 

The data in the histogram in Figure 2 were also encoded as a confusion matrix (see Figure 3), thus providing a different view of the information. Figure 3 contains 198 rows and 198 columns (39,204 method–test material pairings), color coded red for 

-values <0.05 (corresponding to "fail" results) and green for 

-values ≥ 0.05 (corresponding to "pass" results). The green along the diagonal in Figure 3 shows where the test device declared pass results when a method was tested against its corresponding material. The red shown on the vast majority of off-diagonal positions indicates where the device declared a fail result when testing a method against the wrong material. As can be seen in Figure 3, for all but six pairings, the test device indicated pass when the material's method was tested against the same material, and fail when tested against all others.  

Figure 3: Confusion matrix summary of p-values for device challenges on 198 materials. 

The false-positive results for the six pairings shown in Figure 3 are results of the extremely high similarity in composition and/or molecular structure of the materials in each pair. One example is safflower oil, which has a very high concentration of linoleic aicd. Other examples are povidone and crospovidone which have the same structure except for a cross linkage in crospovidone. The slight differences between the materials in each pair do not result in significant differences in their Raman spectra.  

-value results, the range was more finely divided in the following compound class studies, allowing a more detailed evaluation of the differentiating capability of the device's embedded decision algorithms. 

 Of the 198 test materials, 12 were amino acids. Figure 4 shows 

-value ranges for the pairings of amino acids. The green indicates 

-values ≥0.1, a more stringent qualification than the test device's normal threshold of 0.05 for a pass result. For all amino acids, the test device correctly verified each material. The test device also correctly rejected nonmatching materials, with 

Figure 4: Confusion matrix for amino acid subset. 

Test materials also included 15 organic solvents. Figure 5 shows 

-value ranges for the test pairings of these solvents. The green area indicates 

-values ≥0.1, again, a more stringent qualification than the test device's normal "pass" threshold of 0.05. For this category, the test device correctly verified each material. It also correctly rejected nonmatching materials, with 

 , as indicated by red squares in Figure 5.  

Figure 5: Confusion matrix for organic solvent subset. 

-values for 13 long-chain hydrocarbons are shown in Figure 6. Unlike the other categories of test substances discussed previously, some of these pairings resulted in higher 

-values when a method was tested against a similar material in this class of compounds. For larger molecules such as these, the Raman signal corresponding to the long hydrocarbon chain portion of the molecule can dominate the signal making spectral features arising from other functional groups difficult to detect. This is in contrast to small molecules where single atomic or functional group substitutions are easily detected (e.g. acetone/methyl ethyl ketone and calcium sulfate/magnesium sulfate).  

Figure 6: Confusion matrix for long-chain hydrocarbon subset. 

In almost all cases, the long-chain hydrocarbon pairings produced exclusive (completely selective), pass verdicts with 

-values >0.1, indicated by green in Figure 6. The one exception was the magnesium stearate–sorbitan monopalmitate pairing. In this case, the Raman signal from the long-chain hydrocarbon (differing between the two materials by only one carbon) overwhelms differences present in the rest of the molecule. About half of the negative pairings produced very low 

 , indicated by red in Figure 6. Nineteen pairings of long-chain hydrocarbons resulted in p-values in the range of 0.01 to 0.1 (yellow in Figure 6). Off-diagonal 

-values in this range represent cases in which clear gaps in selectivity between methods exist (

-value >0.05) or where the pairings are not so far removed, lying in the 0.01–0.05 range, that there is risk for inadequate selectivity. It is important to identify materials like these during method validation to identify potential false positives and assess their degree of risk. Another group, the 54 pairings indicated by orange in Figure 6, produced 

  to 0.01 which are of significantly less concern during method validation and robustness testing.  

This study demonstrates the general applicability of handheld Raman spectroscopy for pharmaceutical raw material identification through extensive characterization of method performance for 198 raw materials commonly used in the pharmaceutical and related industries. Method development required only one sample of each material. In nearly 40,000 method challenges, a 100% correct positive identification rate and 99.9% correct rejection rate was achieved.  

Handheld Raman spectroscopy offers an attractive alternative to other analytical techniques for identity verification of raw materials. The technology's specificity for a wide range of materials, its ease of method development and validation, and the ability to directly transfer methods between instruments, enable handheld Raman spectroscopy to both improve the quality of raw-materials testing while reducing cost.  

What would you do differently? Submit your comments about this paper in the space below.

 1.  T. Vankeirsbilck et al., "Applications of Raman Spectroscopy in Pharmaceutical Analysis," 

 2.  R.L. McCreery et al., "Noninvasive Identification of Materials Inside USP Vials with Raman Spectroscopy and a Raman Spectral Library," 

 3.  R. Cantu et al., "A Simple Approach for Developing, Validating, and Transferring an Identification Method for Multidose Pharmaceutical Products using FT-Raman Spectroscopy," 

 4.  R.L. Green and C.D. Brown, "Raw-Material Authentication Using a Handheld Raman Spectrometer," 

 27  ‹1120› "Raman spectrometry," p. 1120. 

 6.  ICH Q2R1 Harmonised Tripartite Guideline, 

Validation of Analytical Procedures: Text and Methodolgy

 7.  S.R. Lowry, "Automated Spectral Searching in Infrared, Raman, and Near-Infrared Spectroscopy," in 

 Vol.3 (Wiley, New York, 2001), pp. 1948-1961.  

What would you do differently? Email your thoughts about this paper to 

Robert L. Green* is a research scientist, Robert Brush is an application scientist, Wayne Jalenak is a senior application scientist, and Christopher D. Brown is director of system analytics and applications, all at Ahura Scientific, 46 Jonspin Rd., Wilmington, MA 01887, tel. 978.657.5555, fax 978.657.5921, 

*To whom all correspondence should be addressed. 

Articles

Using handheld Raman spectroscopy, methods were developed and evaluated for 198 substances widely used as raw materials.

Verification Methods for 198 Common Raw Materials 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).  

Renewed enthusiasm for Raman technology has been spurred on by numerous technological advances. Raman scattering was historically very difficult to detect because the scattering phenomenon is very weak. NIR lasers, charge-coupled device (CCD) detectors, and Rayleigh rejection filters have made Raman spectroscopy a practical laboratory analysis technique by increasing the sensitivity and decreasing nuisance signal contributions from fluorescence. Furthermore, modern Raman instrumentation is faster, more robust, and less expensive than previous systems. In addition, advances in component miniaturization have led to the availability of portable, handheld Raman systems (see Figure 1).  

Figure 1 (ALL IMAGES ARE COURTESY OF THE AUTHORS) 

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.  

In terms of analytical characteristics, Raman spectroscopy is particularly effective for identity testing because of its high degree of selectivity (12). Every chemical compound with covalent bonds produces its own characteristic pattern of Raman shifts, which can be used to chemically fingerprint and therefore identify the compound. Raman spectra for acetylsalicylic acid and acetaminophen are shown in Figure 2, illustrating the excellent selectivity provided by distinct, well-defined peaks. 

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.  

When making an identity assessment based on spectral data, the unknown measurement is examined in relation to one or more reference spectra. A common approach for spectral comparison is to calculate the wavelength correlation, which is equivalent to measuring the cosine of the angle between the two spectra. The resulting correlation coefficient, 

 is 1 when the two spectra are in perfect correspondence and 0 when they are orthogonal. Although useful for quick similarity assessments, the correlation coefficient is not particularly sensitive to discrepancies between the two spectra of interest. More problematic, a correlation coefficient other than 0 or 1 has no direct interpretation in the context of spectral identity testing because a transparent interpretation as a test statistic only holds when dealing with random normal variates, which is clearly not the case for FTIR, Raman, or NIR spectra. Despite these difficulties, there is regulatory guidance on selecting a correlation threshold that states, "Unless otherwise justified, a [correlation] threshold below 0.95 is not acceptable..."(13). Arbitrary designation of a correlation threshold in this manner can be perilous because it is unsupported by either basic statistics or demonstration, a point that has also been made by other researchers (14). 

Figure 3 shows a Raman reference spectrum for pure glycerin and a Raman spectrum measured for an "unknown" substance, in this case glycerin contaminated with 20% diethylene glycol by volume. Contamination of glycerin with diethylene glycol is a problem of current interest, as evidenced by several news reports and a recent FDA guidance (15). In spite of clear differences in the highlighted regions of the spectra in Figure 5, there is a very favorable correlation coefficient (0.96), which indicates that this material would pass as acceptable unless a higher than typical correlation threshold were applied. 

An alternate approach to wavelength correlation used by the handheld units for this study is to evaluate whether the measured spectrum lies within the multivariate domain of the reference spectrum (or spectra), which is defined by the uncertainty characteristics of each measurement, including exposure settings, instrument and environment properties (e.g., temperature, dark current, ambient lighting), and the optical properties of the sample itself. When comparing spectra in this manner, the analyst looks for spectral features that contradict the reference spectrum given the uncertainty of the measurement, rather than how well the bulk spectrum matches. For identity testing, the critical question is whether the measured spectrum can be considered consistent with the reference spectrum given the multivariate uncertainty of the measurement conditions. Like most statistical tests, the analysis is distilled to a 

-value, in this case the probability that the observed differences between the test and reference spectra simply arose by chance given the uncertainty of the measurement. Higher 

-values indicate that any differences are not large relative to the uncertainty of the measurement. In such cases, the measured spectrum is deemed consistent with the reference spectrum, and the instrument declares "pass." If the 

-value is too low (below 0.05 as the device default), then it suggests that discrepancies between the measured and reference spectrum were unlikely to arise from the uncertainty in the measurement alone, and the device declares "fail." The system logic just described is illustrated in Figure 4. The earlier example for the spectra in Figure 3 resulted in a 

, which indicates that there is a discrepancy. 

Handheld Raman material identity verification systems were used in this work (TruScan, Ahura Scientific, Wilmington, MA). Six units manufactured at different times from different runs of parts were used in this study (details provided in subsequent paragraphs in this article). This instrumentation uses a 785-nm NIR external cavity-stabilized, cooled laser as a light source. The laser has a maximum power consumption of 1 W, a maximum output of 400 mW, and typically operates at 300 mW output. A single-dispersive spectrometer, cooled CCD, and dielectric notch filters for Rayleigh rejection make up the rest of the instrument hardware. The device also has an integrated barcode reader for capturing sample lot–batch–other identifiers and selecting methods. Detailed specifications are listed in Table I. 

Table I:  TruScan instrument specifications. 

Three TruScan devices (hereafter referred to as the "test devices") were tested with each of the 32 common pharmaceutical materials listed in the sidebar "Materials tested" (Sigma-Aldrich, St. Louis, MO) to evaluate the applicability of the systems for incoming inspection. 

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 

When authenticating material, the user selects the appropriate method from a menu on the instrument's display and enters the material identification number with the barcode reader or keypad. The operator directs the unit's laser aperture toward the sample and initiates the measurement. Once data collection begins, the instrument optimizes settings including exposure time, number of accumulations, and laser power to achieve acceptable SNR as quickly as possible. Table II shows the average measurement times required for each material. As demonstrated by the information in Table II, the average measurement times vary considerably depending on the characteristics of the material (Raman cross section, etc.). Specifically, the times range from 1 s to more than 6 min; however, the majority of materials were measured in less than 1 min. The individual measurement times (not shown) for the three units differ to some degree, as well, primarily because of environmental conditions during the measurement (sample positioning, ambient light, etc.). 

Table II:  Measurement time averages (rounded to nearest second). 

As indicated in the experimental section, a 

-value for each unknown-method pair was generated for measurements across all three test devices. The 

-values, which indicate whether the measured spectrum is statistically different from the reference spectrum, were examined individually and were averaged for presentation purposes. Figure 6 shows data for each material– method pair. Note that the values shown range from 

 >0.1. In normal application of the instrument, the result is a simple pass–fail determination. The unit's default threshold for "pass" is 

 ≥0.05. This indicates that the measured spectrum is statistically consistent with the reference spectrum given the uncertainty of the measurement. 

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 

-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 

-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 

-values were 0.03, 0.06, and 0.04, and for hydroxypropyl cellulose, the 

-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 

. 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 (

 < 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. 

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,  

Submitted: July 26, 2007. Accepted: Sept. 13, 2007.  

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Using a handheld Raman spectrometer, the authors developed methods for 28 commonly used excipients and active ingredients.