Drug Stability Analysis by Raman Spectroscopy

Abstract

Pharmaceutical drugs are available to astronauts to help them overcome the deleterious effects of weightlessness, sickness and injuries. Unfortunately, recent studies have shown that some of the drugs currently used may degrade more rapidly in space, losing their potency before their expiration dates. To complicate matters, the degradation products of some drugs can be toxic. Here, we present a preliminary investigation of the ability of Raman spectroscopy to quantify mixtures of four drugs; acetaminophen, azithromycin, epinephrine, and lidocaine, with their primary degradation products. The Raman spectra for the mixtures were replicated by adding the pure spectra of the drug and its degradant to determine the relative percent contributions using classical least squares. This multivariate approach allowed determining concentrations in ~10 min with a limit of detection of ~4% of the degradant. These results suggest that a Raman analyzer could be used to assess drug potency, nondestructively, at the time of use to ensure crewmember safety.

Full text

Pharmaceutics 2014, 6, 651-662; doi:10.3390/pharmaceutics6040651
pharmaceutics
ISSN 1999-4923
www.mdpi.com/journal/pharmaceutics
Article
Drug Stability Analysis by Raman Spectroscopy
Chetan Shende, Wayne Smith, Carl Brouillette and Stuart Farquharson *
Real-Time Analyzers, Inc., Middletown, CT 06457, USA; E-Mails: chetan@rta.biz (C.S.);
wayne@rta.biz (W.S.); carl@rta.biz (C.B.)
* Author to whom correspondence should be addressed; E-Mail: stu@rta.biz;
Tel.: +1-860-635-9800; Fax: +1-860-635-9804.
External Editor: Raymond Naxing Xu
Received: 2 October 2014; in revised form: 15 December 2014 / Accepted: 18 December 2014 /
Published: 22 December 2014
Abstract: Pharmaceutical drugs are available to astronauts to help them overcome the
deleterious effects of weightlessness, sickness and injuries. Unfortunately, recent studies
have shown that some of the drugs currently used may degrade more rapidly in space, losing
their potency before their expiration dates. To complicate matters, the degradation products
of some drugs can be toxic. Here, we present a preliminary investigation of the ability of
Raman spectroscopy to quantify mixtures of four drugs; acetaminophen, azithromycin,
epinephrine, and lidocaine, with their primary degradation products. The Raman spectra for
the mixtures were replicated by adding the pure spectra of the drug and its degradant to
determine the relative percent contributions using classical least squares. This multivariate
approach allowed determining concentrations in ~10 min with a limit of detection of ~4% of
the degradant. These results suggest that a Raman analyzer could be used to assess drug
potency, nondestructively, at the time of use to ensure crewmember safety.
Keywords: drug stability analysis; drug degradation; Raman spectroscopy; multivariate
analysis; astronaut health
1. Introduction
Astronauts suffer from a number of maladies caused by long-term weightlessness and radiation
exposure, such as space motion sickness (SMS), cephalad fluid shifts, sleep deprivation, reduced
immune response, and loss of bone and muscle mass [1–6]. To counter these effects, astronauts have
OPEN ACCESS

Pharmaceutics 2014, 6 652
available medicines to either treat the physiological changes or the symptoms. In the case of the
International Space Station (ISS), a medical kit is included to aid the health of the astronauts. The kit
contains pharmaceutical drugs that range from acetaminophen (Tylenol®) for headaches and pain to
promethazine for SMS to epinephrine for allergic reactions and cardiac arrest. However, the size
limitations imposed by the ISS (or future space craft) restrict the number of drug types, as well as their
quantity. Furthermore, the value of the drugs is limited to the period that they maintain 90% of their
potency, i.e., their shelf-lives, which are typically 1 to 2 years. The active pharmaceutical ingredient
(API) will degrade over time, most often due to heat and moisture, which promote hydrolysis of the API.
As expected, the ISS drugs are replaced according to their listed shelf-lives or more specifically, the
expiration dates. However, it was recently shown that the rate of degradation for some drugs may
accelerate in space [7,8], possibly due to radiation. It was reported that some of the drugs fell below 90%
potency before their expiration date. To complicate matters, the degradation products of some drugs can
be toxic, such as p-aminophenol formed from acetaminophen, which can cause liver damage [9].
Consequently, there is a need for an analyzer to measure both the API concentration and its
degradation products in spaceflights. Such an analyzer would have immediate value in its ability to
assess the potency of a drug at the time of use to ensure crewmember safety. It would also have
long-term value in its ability to perform degradation studies aboard the ISS to better define the
shelf-lives of drugs in that environment.
Drug manufacturers employ standard stress tests to establish shelf-life and identify degradation
products, as outlined by the International Conference on Harmonisation (ICH) [10]. These tests
accelerate environmental storage conditions, such as hydrolysis, photolysis, oxidation, and heat. Once
the degradation products are formed, they are identified by a number of analytical techniques, such as
infrared spectroscopy (IR), Raman spectroscopy, nuclear magnetic resonance spectroscopy (NMR),
and/or mass spectrometry (MS). Once identified, a method is developed to quantify the degradation
products. Most often the method is high performance liquid chromatography (HPLC) [11]. Numerous
HPLC methods for various pharmaceuticals, such as acetaminophen, have been published [12–18].
Unfortunately, HPLC is not suited for the proposed application, as it is relatively labor intensive, it is
time consuming (tens of minutes), and it requires consumables (solvents and columns). Samples must
be dissolved in a carrier solvent and filtered prior to injection into the column, and must often be
pretreated to remove interferents, such as the inactive ingredients (excipients). Furthermore, different
carrier solvents, columns and conditions are typically required for each drug class.
While IR, NMR, and MS can in principal be used to quantify API degradation, each has a serious
limitation with regard to this application. Just like HPLC, sample pretreatment, using consumables, is
required for NMR and MS. In addition, these analyzers are large and heavy. While mid-IR and near-IR
have been successfully used to confirm the identity of drugs and drug formulations using the reflection
mode [19–21], the transmission mode is required to perform quantitative analysis. The latter mode, in
general, requires diluting the sample in a non-absorbing solvent, which would preclude administering
the drug sample [22].
Raman spectroscopy measures the vibrational modes of a sample. A spectrum consists of a
wavelength distribution of peaks corresponding to molecular vibrations specific to the sample being
analyzed. Chemicals, such as drugs, can be identified by the frequency and quantified by the intensity
of the peaks. In the last 20 years, considerable technological advances, such as stable diode lasers, sharp

Pharmaceutics 2014, 6 653
wavelength transition optical filters, and high quantum efficiency detectors, have made Raman
spectroscopy standard equipment in analytical laboratories, and more recently, allowed the development
of lightweight, portable systems [23]. An attractive advantage to this technique is that samples do not
have to be extracted or prepared, and the analysis is non-destructive. A laser is simply focused into the
sample to generate the Raman radiation, which is collected by the spectrometer for analysis.
Recently, Raman spectroscopy has been successfully used to verify contents of drugs within their
packaging [22], measure the composition and uniformity of drug pills [24–29], identify street
drugs [30,31], and determine drug authenticity [32]. Based on these successes, we present a preliminary
investigation of the ability of Raman spectroscopy to quantify mixtures of four drugs; acetaminophen,
azithromycin, epinephrine, and lidocaine, with their primary degradation products. Here we limit the
analysis to each API and its primary degradant to demonstrate the basis of this approach.
2. Experimental Section
Acetaminophen (paracetamol, Tylenol®) and p-aminophenol were purchased from US Pharmacopeia
(Rockville, MD, USA) at a purity of >99%. Azithromycin (Zithromax®), azaerythromycin A, lidocaine
(xylocaine or lignocaine), 2,6-dimethylaniline, epinephrine (adrenaline, adrenalin), and nor-epinephrine
(noradrenaline) were purchased from Sigma Aldrich (Allentown, PA, USA) at a lower purity of >97.5%
(Analytical grade) due to the high cost. The APIs and degradants were ground by mortar and pestle into
fine powders to minimize particle size effects [33,34]. Samples were prepared by weighing (Metler
Toledo) and adding 1, 4, 9, 19, and 49 mg of the API to 1 mg of the degradant to produce 50%, 20%,
10%, 5%, and 2% mass percent mixtures, respectively. This concentration series was chosen to cover a
wide range with a focus on the low concentrations germane to the described application. After each
addition, the samples were vortex mixed (Scientific Industries, Inc., Bohemia, NY, USA) and again
ground by mortar and pestle to improve homogeneity.
All Raman measurements were performed using an FT-Raman spectrometer (RamanID-1064, RTA,
Middletown, CT, USA). The spectrometer employed a 1064 nm diode laser (Innovative Photonic
Solutions, Monmouth Junction, NJ, USA) that provided 500 mW at the sample, a proprietary
interferometer to separate the Raman signal into its component wavelengths, and a single element
InGaAs detector (Judson Tech, Montgomeryville, PA, USA). Measurements were performed using
8 cm−1 resolution. Samples were placed in a 2 mL vial, mounted on the XY stage above the source laser
and measured. Measurements of the pure samples consisted of five one-minute spectra averaged
together, each spectrum consisting of 100 scans. Measurements of mixtures consisted of ten one-minute
spectra averaged together, obtained at 10 spots, ~300 µm in diameter, spaced 1 mm apart along the
length of the vial to compensate for potential mixture inhomogeneities. The average spectrum of the ten
spots was used for calculations to better represent the prepared concentrations.
3. Results and Discussion
Four drugs representative of the medications used by NASA astronauts were selected for study:
acetaminophen, azithromycin, lidocaine, and epinephrine (Figure 1). Acetaminophen is used primarily
as a pain reliever and a fever reducer; azithromycin is used as an antibiotic used for treating middle ear
infections, strep throat, and pneumonia; lidocaine is used for relieving skin itching, burning and pain, as

Pharmaceutics 2014, 6 654
well as for minor surgery; and epinephrine is used to treat allergic reactions and stimulate the heart
during cardiac arrest.
Figure 1. The chemical structures of the four drugs and their primary degradation products.
(a) acetaminophen to p-aminophenol; (b) azithromycin to azaerythromycin A; (c) lidocaine
to 2,6-dimethylaniline; and (d) epinephrine to nor-epinephrine.
(a) (b)
(c) (d)
The structures for acetaminophen and lidocaine and their primary degradation products are fairly
different and consequently produced Raman spectra with significant differences that could be used to
quantify mixtures (Figures 2 and 3). The Raman spectrum of acetaminophen is dominated by peaks at
797, 858, 1236, 1324, 1560, 1611, and 1649 cm
−1
, which are assigned to CNC ring stretching, ring breathing,
C–C ring stretching, amide III, amide II, ring stretching, and amide I modes, respectively [35–37]. Upon loss
of the amide functional group during degradation, the amide bands and the CNC stretching mode
disappear, while the increased molecular symmetry results in more intense ring modes for p-aminophenol.
The Raman spectrum of lidocaine is also dominated by the CNC ring, ring, and amide modes at 617,
706 and 1596, and 1666 cm
−1
, respectively. The same spectral changes are observed for the degradation
of lidocaine to 2,6-dimethylaniline, i.e., the 617 and 1666 cm
−1
peaks disappear, while the ring breathing
mode gains intensity and shifts to a lower frequency, viz. 706 to 675 cm
−1
.
Figure 2. Raman spectra of (a) acetaminophen and (b) p-aminophenol. Spectral conditions:
500 mW at 1064 nm, 8 cm
−1
resolution, 5 min acquisition (100 scans).

Pharmaceutics 2014, 6 655
Figure 3. (a) Raman spectra of (a) Lidocaine (intensity × 5); and (b) 2,6-dimethylaniline.
Spectral conditions as in Figure 2.
Epinephrine and azithromycin degradation involves replacing a methyl group by a hydrogen atom,
and consequently the spectral changes are less substantial. Virtually all of the peaks in the epinephrine
Raman spectrum are present in the nor-epinephrine spectrum, which include those at 599, 777, 1081,
1172, 1283 and 1599 cm
−1
that are assigned to CC=O stretching, ring breathing, CCH bending, CO
stretching, CO asymmetric stretching, and aromatic CC stretching modes (Figure 4) [35,38]. However,
the unassigned intense peak at 954 cm
−1
is replaced by a doublet with modest intensity at 947 and
966 cm
−1
, suggesting that this epinephrine peak contains some CNC character.
Figure 4. Raman spectra of (a) epinephrine and (b) nor-epinephrine. Spectral conditions as
in Figure 2.
Since azithromycin has numerous methyl groups, the replacement of one group at the nitrogen
position of the oxo-6-azacyclopentadecyl ring by a hydrogen atom, as expected, produced only minor
changes in the Raman spectrum (Figure 5). The Raman spectrum is dominated by a peak at 1454 cm
−1
due to the ether stretching modes, while a number of CC and CNC stretching, and CH bending modes
appear between 600 and 1200 cm
−1
[35]. Within this region minor changes do occur; in particular, the
peak at 814 cm
−1
loses intensity and shifts to 805 cm
−1
. While the spectral differences between
epinephrine and nor-epinephrine may be sufficient to quantify a mixture, it is clear that the differences
between azithromycin and azaerythromycin A may not.

Pharmaceutics 2014, 6 656
Figure 5. Raman spectra of (a) azithromycin and (b) azaerythromycin A. Spectral conditions
as in Figure 2.
The initial quantification of the mixtures employed the traditional method of measuring Raman peak
intensities (height or area). It is worth noting that the absolute concentration of the degradant is not
necessary, but only its concentration relative to the API. As shown in Figure 2, the best peak for this
purpose is the intense ring breathing mode at 846 cm
−1
for p-aminophenol. Since this peak overlaps with
the same mode in the acetaminophen spectrum (Figure 6), the peak height was used. The peak height at
this frequency for the measured mixtures was scaled to 100% for pure p-aminophenol and 0% for pure
acetaminophen (Table 1). While the correlation coefficient (R
2
) for a plot of the prepared versus
calculated percentages was close to 1.0, all of the predicted concentrations were high with an intercept
of 2.9% (Figure 7). The latter can be taken as the background level, which multiplied by 3 represents a
limit of detection (LOD) of 8.7% [39]. The lack of accuracy in the data, represented by the root mean
squared error (RMSE), can be attributed to the overlapping of the peaks. It is clear from these data, that
traditional peak height analysis will not allow accurate determination of acetaminophen in a medication
that has degraded by 10% or less.
Since the univariate approach to quantitation clearly has limitations, a simple multivariate approach
was investigated. The approach fit the entire spectrum with classical least squares weighted contributions
from the two pure spectra of the drug and its degradant at each wavelength [40]. A software program
was written that allows selecting the spectral region to fit, smoothing the spectra, and/or taking the first
derivative. Judicious selection of the spectral region allows limiting the calculation to the spectral
features that best represent each component in the mixture. Smoothing the spectra minimizes errors
associated with fitting the noise instead of the signal. The first derivative minimizes the effects of
baseline offset, slope and fluorescence contributions. For the acetaminophen/p-aminophenol mixtures,
the 550 to 1800 cm
−1
spectral region of the raw spectra and their first derivatives were used to calculate
each sample concentration (Table 1). Both spectral types yielded more accurate concentrations than the
simple peak height calculation. More importantly, the spectra-based calculated percentages proved very
accurate at the low concentrations, e.g., 4.9% and 2.2% for 5.0% and 2.0%, respectively. The straight
line fit to the spectra also yielded a y-intercept of 0.96, representing an improvement of a factor of three
in the LOD to 2.88%. Smoothing was not used in these calculations, since the spectra contained little
noise. In addition, using the first derivative of the spectra was probably not necessary since the spectral
baseline was flat. In fact, the first derivative yielded slightly inferior results compared with the raw spectra.

Pharmaceutics 2014, 6 657
Figure 6. Raman spectra of p-aminophenol mixed with acetaminophen at 100, 50, 20, 10, 5,
and 2 mass %, all normalized to the 1170 cm
−1
peak height set equal to 1. Inset: graph and
values of prepared versus calculated mass % based on peak height at 846 cm
−1
. Spectral
conditions as in Figure 2, except ten one-minute spectra were averaged for each concentration.
Table 1. Prepared and Calculated percentages of p-aminophenol mixed with acetaminophen
determined using Raman peak height at 846 cm
−1
and spectral weighting of the spectra and
their first derivatives for the 550 to 1800 cm
−1
region.
% p-Aminophenol
Prepared Calculated Calculated (550–1800)
% Peak Hit Spectra 1st der
50 56.8 48.7 48.0
20 21.8 18.4 17.3
10 16.0 13.4 13.5
5 7.3 4.9 4.2
2 5.3 2.2 4.2
R2 0.993 0.991 0.985
RMSE 4.52 1.78 2.41
Intercept 2.90 0.96 1.44
LOD 8.70 2.88 4.32
Figure 7. (a) Plots of Prepared versus Calculated percentages of p-aminophenol mixed with
acetaminophen determined using (a) the Raman peak height at 846 cm
−1
; and the 550–1800 cm
−1
Raman spectral region using (b) raw spectra (circles), and (c) first derivatives of the spectra
(squares). Ideally, the slope should equal 1.0 and the intercepts 0.0.

Pharmaceutics 2014, 6 658
The spectral fitting approach was next applied to the remaining drugs and their degradants to
determine how well it could calculate low concentrations. The data are summarized in Table 2.
Table 2. Prepared and calculated percentages of degradants mixed with their corresponding
drug determined using Raman spectra. Spectral regions are indicated.
Azaerythromycin A Norepinephrine 2,6-Dimethylaniline
Prepared Calculated Calculated Calculated
% 565–1220 575–1560 450–1050
50 41.3 43.6 48.1
20 22.1 24.6 20.6
10 11.3 8.4 10.4
5 4.3 2.9 9.7 *
2 0.8 0.2 7.3 *
R2 0.97 0.96 0.99
RMSE 4.09 3.80 1.17
Intercept 1.55 0.045 1.40
LOD 4.65 0.135 4.20
* Not included in the R2, RMSE, Intercept and LOD calculations.
The azithromycin/azaerythromycin A mixtures presented the greatest challenge of the four drugs to
the ability of Raman spectroscopy to calculate percentages using spectral weighting, since the spectral
differences are very minor (see Figure 5) and the Raman spectra are relatively weak (the y-axes of all
spectra in Figures 2–5 use the same relative scale). Nevertheless, very good results were obtained, as
long as the spectral region used to calculate the concentrations was confined to the region where these
spectral differences are most apparent. A very good fit to the 5%/95% azaerythromycin A/azithromycin
mixture was obtained using 4.3%/95.7% and the 565 to 1220 cm
−1
region (Figure 8).
Figure 8. Raman spectrum of (a) the 5%/95% azaerythromycin A/azithromycin mixture
(red) fit with (b) a spectrum created using 4.3% azaerythromycin A and 95.7% azithromycin
of their pure spectra; (c) The residual of the fit (a–b). Spectral conditions as in Figure 6,
except 565 to 1220 cm
−1
region used.
While the norepinephrine/epinephrine pair had greater spectral differences than the azaerythromycin A/
azithromycin pair, the accuracies of the calculated values were not quite as good at the low

Pharmaceutics 2014, 6 659
concentrations (Table 2). This may be attributed to the inhomogeneities in the samples and the limited
number of spectral measurement points, which may not have adequately represented the concentration
for this sample. The lidocaine/2,6-dimethylaniline mixtures presented a different challenge, primarily in
terms of sample preparation. Lidocaine is a powder, while 2,6-dimethylaniline is a liquid. The samples
were prepared as weight/weight percentages, but at 2,6-dimethylaniline concentrations below 10%,
lidocaine did not dissolve, but instead separated. Consequently, Raman spectral measurements were of
heterogeneous samples. This created greater error in 2% and 5% 2,6-dimethylaniline concentrations,
which were consequently not used in the R2 and LOD calculations.
4. Conclusions
This preliminary investigation demonstrates that Raman spectroscopy has great potential to determine
the extent of degradation of active pharmaceutical ingredients nondestructively and without sample
preparation in 10 min or less. The average limit of detection of 3.9%, based on y-intercepts for the four
drugs measured, suggests that 10% degradation can be determined with this ~4% accuracy (RMSE),
provided that the tablets, powders, gels or pastes contain a significant percentage of API by mass.
Fortunately, many formulations, such as Tylenol® and PLIVA® (azithromycin) are composed of greater
than 50% API, viz.: 325 and 250 mg, as part of 400 and 450 mg tablets, respectively [41]. It is also worth
noting that, with the exception of titanium dioxide, most excipients generate much weaker Raman signals
than APIs [42]. Furthermore, the maximum mass of the degradant in any mixture measured here was
1 mg, indicating that the absolute degradant mass should not limit sensitivity. Nevertheless, future work
will expand analysis to actual products containing excipients and explore methods to improve the limits
of detection for all of the ISS drugs with a goal of 10% ± 1% degradant. These methods will include
various Raman excitation wavelengths, laser powers, more sensitive detectors, acquisition times, optical
arrangements (such as transmission [26,43]), and multivariate analysis.
Acknowledgments
Real-Time Analyzers, Inc. personnel are grateful for support provided by the NASA SBIR program
(NNX13CA52P) and would like thank Virginia Wotring of Johnson Space Center for helpful discussions.
Author Contributions
Chetan Shende and Stuart Farquharson conceived the experiments, which were conducted by
Chetan Shende; Wayne Smith wrote the analysis software, which was used by Chetan Shende and
Stuart Farquharson; Carl Brouillette designed and built the Raman analyzer used, which was used by
Chetan Shende; Stuart Farquharson wrote the paper.
Conflicts of Interest
The authors declare no conflict of interest.
References
1. Hughes-Fulford, M. Metabolic changes in bone function in space flight. Receptor 1993, 3, 145–154.

Pharmaceutics 2014, 6 660
2. Kohl, R.L.; MacDonald, S. New pharmacologic approaches to the prevention of space/motion
sickness. J. Clin. Pharmacol. 1991, 10, 934–946.
3. Santy, P.; Mungo; M. Pharmacologic Considerations for Shuttle Astronauts. J. Clin. Pharmacol.
1991, 31, 931–933.
4. LeBlanc, A.; Schneider, V. Can the Adult Skeleton Recover Lost Bone? Exp. Gerontol. 1991, 26,
189–201.
5. Lane, H.W.; LeBlanc, A.D.; Putcha, L. Whitson, P.A. Nutrition and human physiological
adaptations to space flight. Am. J. Clin. Nutr. 1993, 58, 583–586.
6. Tipton, C.M.; Greenlead, J.E.; Jackson, G. Neuroendocrine and immune system responses with
spaceflights. Med. Sci. Sports Exerc. 1996, 28, 988–998.
7. Du, B.; Daniels, V.R.; Vaksman, Z.; Boyd, J.L.; Cready, C.; Putcha, L. Evaluation of physical and
chemical changes in pharmaceuticals flown on space missions. AAPS J. 2011, 13, 299–308.
8. Wotring, V.E. Stability analysis of ISS medications, NASA Human Research Program
Investigator’s Workshop, (2014). Available online: http://www.hou.usra.edu/meetings/hrp2014/
pdf/3242.pdf (accessed on 11 September 2014).
9. Song, H.; Chen, T.S. P-aminophenol-induced liver toxicity: tentative evidence for a role of
acetaminophen. J. Biochem. Mol. Toxicol. 2001, 15, 34–40.
10. International Conference on Harmonization 2003. Stability testing of new drug substances
and products Q1A (R2). Available online: http://www.ich.org/fileadmin/Public_Web_Site/
ICH_Products/Guidelines/Quality/Q1A_R2/Step4/Q1A_R2__Guideline.pdf (accessed on 13 June
2014).
11. Sornchaithawatwong, C.; Vorrarat, S.; Nunthanavanit, P. Simultaneous determination of
paracetamol and its main degradation product in generic paracetamol tablets using reverse-phase
HPLC. J. Health. Res. 2010, 24, 103–106.
12. Ahmad, I.; Shaikh, R.H. Stability of paracetamol in packaged tablet formulations. Pak. J. Pharm. Sci.
1993, 6, 37–45.
13. Aguilar, C.A.; Montalvo, C.; Ceron, J.G.; Moctezuma, E. Photocatylitic degradation of acetaminophen.
Int. J. Environ. Res. 2011, 5, 1071–1078.
14. Kamble, R.M.; Singh, S. Stability-indicating RP-HPLC method for analysis of paracetamol and
tramadol in pharmaceutical dosage form. J. Chem. 2012, 9, 1347–1356.
15. Miguel, L.; Barbas, C. LC determinations of impurities in azithromycin tablets. J. Pharm. Biomed.
Anal. 2003, 33, 211–217.
16. Al-Rimawi, F.; Kharoaf, M. Analysis of azithromycin and its related compounds by RP-HPLC with
UV detection. J. Chromat. Sci. 2010, 48, 86–90.
17. Smith, F.M.; Nuessle, N.O. HPLC method for determination of lidocaine in admixture with dextrose
injection. Anal. Lett. 1981, 14, 567–575.
18. Kerddonfak, S.; Manuyakorn, M.; Kamchaisatian, W.; Sasisakulporn, C.; Teawsomboonkit, W.;
Benjapoonpitak, S. The stability and sterility of epinephrine prefilled syringe. Asian Pac. J. Allergy
Immunol. 2010, 28, 53–57.
19. Bukowski, E.J.; Monti, J.A. FTIR-ATR Spectroscopy for Identification of Illicit Drugs Seized From
Clandestine Laboratories. Am. Lab. 2007, 39, 16–19.

Pharmaceutics 2014, 6 661
20. Scafi, S.H.; Pasquini, C. Identification of counterfeit drugs using near-infrared spectroscopy.
Analyst 2001, 126, 2218–2224.
21. Polli, J.E.; Hoag, S.W.; Flank, S. Comparison of Authentic and Suspect Pharmaceuticals. Pharm.
Tech. 2009, 33, 46–52.
22. McCreery, R.L.; Horn, A.J.; Spencer, J.; Jefferson, E. Noninvasive identification of materials inside
USP vials with Raman spectroscopy and a Raman spectral library. J. Pharm. Sci. 1998, 87, 1–8.
23. Yang, D.; Thomas, R. The Benefits of a High-Performance, Handheld Raman Spectrometer for the
Rapid Identification of Pharmaceutical Raw Materials. Am. Pharm. Rev. 2012, 15, 1–7.
24. Szostak, R.; Mazurek, S. Quantitative determination of acetylsalicylic acid and acetaminophen in
tablets by FT-Raman spectroscopy. Analyst 2002, 127, 144–148.
25. Matousek, P; Parker, A.W. Bulk Raman analysis of pharmaceutical tablets. Appl. Spectrosc. 2006,
60, 1353–1357.
26. Johansson, J.; Sparén, A.; Svensson, O.; Folestad, S.; Claybourn, M. Quantitative Transmission
Raman Spectroscopy of Pharmaceutical Tablets and Capsules. Appl. Spectrosc. 2007, 61, 1211–1218.
27. Farquharson, S.; Gift, A.; Smith, W. Pharmaceutical process applications of Raman spectroscopy.
Proc. SPIE 2004, 7272, 1–6.
28. Li, Y.; Du, G.; Cai, W.; Shao, X. Classification and Quantitative Analysis of Azithromycin Tablets
by Raman Spectroscopy and Chemometrics. Am. J. Anal. Chem. 2011, 2, 135–141.
29. Xie, Y.; Cauchon, N. Raman Scattering as a Probe for Properties of Active Pharmaceutical
Ingredients in Tablet Formulations. Am. Pharm. Rev. 2012, 15, 1–6.
30. Noonan, K.Y.; Tonge, L.A.; Fenton, O.S.; Damiano, D.B; Frederick, K.A. Rapid classification of
simulated street drug mixtures using Raman spectroscopy and principal component analysis. Appl.
Spectrosc. 2009, 63, 742–747.
31. Farquharson, S.; Shende, C.; Sengupta, A.; Huang, H.; Inscore, F. Rapid detection and identification
of overdose drugs in Saliva by surface-enhanced Raman scattering using fused gold colloids.
Pharmaceutics 2011, 3, 425–439.
32. Kalyanaraman, R.; Dobler, G.; Ribick, M. Portable Spectrometers for Pharmaceutical Counterfeit
Detection. Am. Pharm. Rev. 2010, 13, 38–45.
33. Pelow-Jarman, M.V.; Hendra, P.J.; Lehnert, R.J. The Dependence of Raman Signal Intensity on
Particle Size for Crystal Powders. Vibr. Spectrosc. 1996, 12, 257–261.
34. Wang, H.; Mann, C.K.; Vickers, T.J. Effect of Powder Properties on the Intensity of Raman
Scattering by Crystalline Solids. Appl. Spectrosc. 2002, 12, 1538–1543.
35. Dollish, F.R.; Fateley, W.G.; Bentley, F.F. Characteristic Raman Frequencies of Organic
Compounds; Wiley: New York, NY, USA, 1974; Appendix One.
36. Diniz, J.E.M.; Borges, R.S.; Alves, C.N.A. DFT study for paracetamol and 3,5-disubstituted
analogues. J. Mol. Struc. THEOCHEM 2004, 673, 93–97.
37. Hernández, B.; Pflüger, F.; Kruglik, S.G.; Ghomi, M. Characteristic Raman lines of phenylalanine
analyzed by a multiconformational approach. J. Raman Spectrosc. 2013, 44, 827–837.
38. Gunasekaran, S.; Kumar, R.T.; Ponnusamy, S. Vibrational spectra and normal coordinate analysis
of adrenaline and dopamine. Indian J. Pure. Appl. Phys. 2007, 45, 884–892.
39. Zhang, X.; Young, M.A.; Lyandres, O.; Van Duyne, R.P. Rapid Detection of an Anthrax Biomarker
by Surface-Enhanced Raman Spectroscopy. J. Am. Chem. Soc. 2005, 127, 4484–4489.

Pharmaceutics 2014, 6 662
40. Mark, H.; Workman, J., Jr. Classical least squares, Part 1, Mathematical theory. Spectroscopy 2010,
25, 16–21.
41. Rowe, R.C.; Sheskey, P.J.; Owen, S.C. Handbook of Pharmaceutical Excipients, 5th ed.; Royal
Pharmaceutical Society of Great Britain: London, UK, 2005.
42. de Veij, M.; Vandenabeele, P.; de Beer, T.; Remon, J.P.; Moens, L. Reference database of Raman
spectra of pharmaceutical excipients, J. Raman Spectrosc. 2008, 40, 297–307.
43. Townshend, N.; Nordon, A.; Littlejohn, D.; Andrews, J.; Dallin, P. Effect of Particle Properties of
Powders on the Generation and Transmission of Raman Scattering. Anal. Chem. 2012, 84, 4665–4670.
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