New guidelines governing the analysis of elemental impurities in pharmaceuticals are being implemented in the manufacturing of pharmaceutical products. X-ray fluorescence (XRF) spectrometry is a nondestructive analysis technique offering high sensitivity, precision, and accuracy without requiring chemical pretreatment. This installment of “Atomic Perspectives” evaluates the ability of XRF spectrometry to perform elemental impurity analysis of 12 elements in various pharmaceutical materials, such as cellulose, talc, and a mixture of cellulose, talc, and TiO

, by the calibration curve method using water solution standard samples, and verifying the qualification of 

	New guidelines governing the analysis of elemental impurities in pharmaceuticals are being implemented. Toxic heavy metals and residual metal catalysts may exist in the raw materials of active pharmaceutical ingredients (APIs) or be added during the manufacturing process, which may be a risk to human health. 

The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use 

 (1), which limits 24 elements, including Cd, Pb, As, Hg, V, Co, Ni, Ir, Pt, Rh, Ru, and Pd, in drug products and pharmaceutical ingredients. This guideline has now reached the implementation stage. In May 2015, the 

) established X-ray fluorescence (XRF) methodology as 

<735>, “X-ray Fluorescence Spectrometry.” XRF spectrometry is a nondestructive analysis technique offering high sensitivity, precision, and accuracy, typically without requiring lengthy chemical pretreatment (2).

	This study assesses the capability of XRF to carry out the determination of these 12 elemental impurities in various pharmaceutical materials, including cellulose, talc, and a mixture of cellulose, talc, and TiO

, using aqueous-based calibration standards, and verifying the use of 

<735> as a screening tool for this analysis. 

	Permitted Daily Exposures and Concentration Limits

<232> “Elemental Impurities: Limits” (3), defines permitted daily exposure (PDE) values for oral, parenteral, and inhalational drug products. There are 24 elements included in the risk assessment and they are classified into four groups: Class 1 (Cd, Pb, As, and Hg), Class 2A (V, Co, and Ni), Class 2B (Tl, Au, Pd, Ir, Os, Rh, Ru, Se, Ag, and Pt), and Class 3 (Li, Sb, Ba, Mo, Cu, Sn, and Cr). Class 1 and 2A are very important elements and must be analyzed regardless of whether they are added intentionally or as result of the production process. Class 2B elements are not required to be checked for the risk assessment if these elements are not intentionally added; however, catalyst elements such as Ir, Pt, Rh, Ru, and Pd are commonly used in the process of producing APIs. It is necessary to convert the maximum daily intake to concentration limits because the PDE units are defined in micrograms per day. 

gives guidance in converting between PDEs and concentration limits as described below: 

	1.     Option 1 is for common permitted concentration limits of elements across drug product components for drug products with daily intakes of no more than 10 g. 

	2.     Option 2a is for common permitted concentration limits across drug product components for a drug product with a specified daily intake. 

	3.     Option 2b is for permitted concentration limits of elements in individual components of a product with a specified daily intake. 

	4.     Option 3 is for permitted concentration limits from finished product analysis. The most important thing is to change the concentration limits depending on the conversion method. Table I shows the oral concentration limits from PDE by options 1 and 2a for the assessment elements. The maximum daily intake assumes 1.0 g for option 2a.

	Table I shows oral drug PDE limits of Class 1, 2A, and 2B elements, together with the converted concentration limits based on 10 g and 1 g per day, daily dosages.

	The XRF technique, generally speaking, does not require chemical pretreatment, and since it is a nondestructive analysis it can identify and determine the concentrations of elements in solid, powdered, and liquid samples. XRF is capable of measuring a wide elemental range, at levels from below parts per million up to percent concentrations. 

	There are two types of XRF spectroscopic techniques: wavelength dispersive (WDXRF) and energy dispersive (EDXRF). EDXRF systems comprise an X-ray tube and a semiconductor detector and can simultaneously capture many types of fluorescence X-rays generated from the sample using a multichannel analyzer. EDXRF uses a lower power X-ray source, and will not significantly damage the sample. EDXRF systems are very convenient for users. In addition, EDXRF systems are smaller than WDXRF systems, they do not require any external utilities, such as chillers or gases, they typically use a 100-V power supply, and have no moving parts when measuring.

	Quantitation is conducted using external calibration standards containing varying concentrations of elements. XRF calibrations have the advantage of being stable for long periods of time before requiring recalibration, in contrast to other elemental analysis techniques, such as inductively coupled plasma–optical emission spectrometry (ICP-OES) and ICP–mass spectrometry (MS) or atomic absorption spectroscopy (AAS). As the bulk of pharmaceutical materials are organic in nature, the EDXRF technique has the best potential for the nondestructive measurement of the aforementioned 12 elements in drug materials without the need for sample dissolution or chemical pretreatment.

	Proposed Elemental Impurities Control Method for Drug Materials

 <233> (4) specifies either the use of ICP-OES or ICP-MS for the determination of elemental impurities in pharmaceutical materials, or any other technique as long as it can meet the validation protocols. ICP techniques are typically more sensitive than XRF; however, they require chemical digestion techniques to dissolve the samples before measurements can be carried out. This process is very complicated and labor intensive and can be prone to human error and contamination of the sample. To reduce these time-consuming sample preparation procedures for ICP-OES and ICP-MS, the EDXRF technique can be used as a preliminary screening tool to determine if additional chemical analyses are required.

	Furthermore, the XRF method may achieve similar performance for low-level chemical analysis if the measurement time is extended, because the detection limit improves with increasing sampling time. This makes EDXRF a very useful screening method to reduce analysis time and lower cost by minimizing the number of samples requiring precise analysis using ICP-OES or ICP-MS (5).

	Solid, liquid, and powder samples can be analyzed by XRF with minimal sample preparation. The only preparation requirement is reducing the sample to a size that fits in the sample cell or chamber. Basically, the larger the sample volume, the smaller the sampling errors.

	Almost all solid samples can be analyzed directly by simply placing them in the sample chamber, while liquid samples are poured in a sample cell, with a supporting film at the bottom. Typical films are made from polypropylene of a few micrometers thickness. The majority of drug materials are in organic matrices and have relatively low X-ray absorption, allowing measurement of the element impurities. Powder samples are placed directly into the sample cell using the tapping method to remove voids in the sample. Coarse powders must be ground to a fine particle size, and nonhomogenous samples should be ground by means of mortar and pestle or a grinding device, such as a mill.

	XRF calibration is performed for each element by determining the relationship between measured intensities and concentration using a number of standard samples. In general, the measured intensity is corrected by some spectral deconvolution method because the fluorescence X-ray intensity is affected by sample matrices, sample volume, and sometimes interference from other peaks. These effects are known and well understood, and as a result, several methods have been developed to compensate for them, including the internal and overlap correction methods.

 <735> was implemented in May 2015. The validation protocol includes operational qualification (OQ) and performance qualification (PQ) to verify that the system operates within target tolerances using appropriate samples with known spectral properties. OQ checks key operating parameters, such as peak position, detector resolution, and count rate, using specific calibration samples. The purpose of PQ is to determine that the instrument is capable of meeting the user’s requirements for all critical-to-quality measurements. These validation and verification protocols include linearity, accuracy and specificity, repeatability, intermediate precision, range, quantification limit, and robustness as described below.

	Analysts should demonstrate a linear relationship between the analyte concentration and corrected XRF response by preparing no fewer than five standards at concentrations that encompass the anticipated concentration of the test sample. The standard curve then should be evaluated using appropriate statistical methods such as least squares regression. The correlation coefficient (

-intercept, and slope of the regression line must be determined. Acceptance criteria: 

	Analysts can determine accuracy by conducting recovery studies using the appropriate matrix spiked with known concentrations of elements. It also is an acceptable practice to compare assay results obtained using the XRF method under validation to those from an established analytical method. Acceptance criteria: 70–150% recovery.

	Analysts should measure the concentrations of three replicates of three separate samples. Acceptance criteria: The relative standard deviation is not more than 20.0%.

	Analysts should establish the effect of random events on the method’s analytical precision. Typical variables include performing the analysis on different days, using different instrumentation, or having two or more analysts perform the method. As a minimum, any combination of at least two of these factors totaling six experiments will provide an estimation of intermediate precision. Acceptance criteria: The relative standard deviation (RSD) is not more than 25.0%.

	Range is the interval between the upper and lower concentration of an analyte in the sample, which has been demonstrated by meeting the accuracy requirement. 100%-centered acceptance criteria: The range is 80–120%. Noncentered acceptance criteria: 10% below the lower limit of specification to 10% above the upper limit of the specification.

	The limit of quantitation (LOQ) can be estimated by calculating the standard deviation of no less than six replicate measurements of a blank and multiplying by 10. Acceptance criteria: The analytical procedure should be capable of determining the analyte precisely and accurately at a level equivalent to 50% of the specification.

	The reliability of an analytical measurement should be demonstrated by deliberate changes to experimental parameters. Acceptance criteria: The measurement of a standard or sample response following a change in experimental parameters should differ from the same standard measured using established parameters by no more than ±20%.

	EDXRF spectroscopy measurements were carried out using an EDX-7000 benchtop spectrometer (Shimadzu Scientific Instruments). The spectrometer is equipped with an air-cooled X-ray tube (1000 μA, 50 kV, 50 W) and an electronically cooled silicon-drift detector. The calibration samples and unknown samples were measured using a counting time of 1800 s and an X-ray beam diameter of 10 mm.

	The calibration samples were prepared by combining two aqueous stock solutions of Cd, Pb, As, Hg, V, Co, and Ni for Classes 1 and 2A, and Ir, Pt, Ru, Rh, and Pd for Class 2B. These standard solutions were prepared at more than five different concentrations including a blank by dilution with reagent water. The validation and verification samples were prepared from cellulose, talc, and a mixture of cellulose, talc, and titanium oxide powders spiked at three different concentrations with the aforementioned elements using standard solutions for AAS. The standard solutions were added to the above three types of blank powder samples and mixed in an agate mortar to prevent adhesion to the walls. 

	Next, 8.0 mL of each calibration standard solution together with 2.0-g powder samples were placed directly into a sample cell (32 mm diameter) with a 5-μm polypropylene support film. The powder samples were prepared using a tapping method to settle them in the cell in an effort to reduce voids in the sample packing. Then 1.0-g and 0.5-g powder samples were also placed directly into a sample cell using the tapping method with a 5-μm polypropylene support film for robustness testing. All concentrations of calibration samples were used to generate a calibration curve for each of the above elements. The range of calibration standards together with the validation and verification samples for each element are shown in Tables II and III.

	Continuous scattered X-rays at each specific energy range for the above elements were used as an internal standard to correct for sample matrix and volume effects. The quantitation of the impurity elements was calculated by the ratio of fluorescent X-rays for each element with continuous scattered X-rays.

	The correlation coefficient of all target elements for the calibration curve meets the acceptance criteria defined in the performance criteria section of 

 <735> by using a minimum of five points and achieving a least squares correlation of at least 0.99. Overlap correction was used for As because the energies of AsKα (10.53 keV) and PbLα (10.55 keV) are very close together. The calibration curves for Cd, Pb, As, Hg, V, Co, Ni, Ir, Pt, Ru, Rh, and Pd are shown in Figure 1.

	Please see the supplemental online information for Tables sI–sIX described below at: 

www.spectroscopyonline.com/supplementary-information-Atomic-Perspectives-July-2017

	Tables sI and sII show the accuracy and specificity and range results for Class 1, 2A, and 2B elemental impurities. For the range, the concentration of 100%-centered range (80–120%) defined the concentration limit at a maximum daily intake of 1.0 g from 

 <232> oral drug PDE values; thus, the “High” sample is within the 100%-centered range. For this test, acceptance criteria for accuracy should be 70–150%.

	The “Middle” samples are in the noncentered concentration range, and accuracy was interpreted such that it should be within the 63.0–165% range. However, the mixture sample could not validate or verify the V because of overlapping with TiKβ derived from the titanium oxide in the mixture sample. 

	For the talc sample, the elements at the low-energy region less than 10 eV, such as Hg, Ni, and V, have a low recovery rate because they are influenced by the matrix effect of other major components such as Si and Mg. However, the accuracy, specificity, and range of all target elements and matrices meet the acceptance criteria.

	Tables sIII and sIV show the repeatability results. In this test, only the “Middle” samples were checked. The relative standard deviation was calculated from a total of nine data points for each material by measuring three replicates of three separate samples. All target elements and matrices meet the acceptance criteria of <20% RSD. 

	Tables sV and sVI show the intermediate precision results. In this test, only the “Middle” samples were evaluated. The variable test evaluated two different days, three separate samples, two different EDXRF instruments and two different analysts. It is also valid to apply different combinations of the above events. For example, three analysts can be used if it is difficult to utilize more than two instruments. The relative standard deviation was calculated from a total of 12 data points for each event. All target elements meet the acceptance criteria of <25% RSD.

	The quantitation limit was calculated as the standard deviation of the intensity for 10 measurements of a blank cellulose sample. The quantitation limits for all target elements are shown in Table sVII for measurement times of 1800 s, with oral PDE values and control limits in the top four rows. In this investigation the control limit is defined as the PDE limit from 

. The concentration limits shown are from conversion method option 2a, based on a dosage of 1 g per day. The two types of control values are based on 

 (30% of PDE). From these data, it can be concluded that the quantitation limit of the EDXRF technique can meet both control limits at 1.0 g maximum daily intake and can successfully perform screening measurements for elemental impurity control at these levels.

 <735> states that the reliability of an analytical measurement should be demonstrated by evaluating deliberate changes to experimental parameters. In this study, the experimental parameter was defined as sample volume. Tables sVIII and sIX exemplify the robustness results for three different sample volumes, showing the quantitation of 1.0 g and 0.5 g compared to 2.0 g. As demonstrated, all target elements meet the acceptance criteria of ±20%.

<735> acceptance criteria for linearity, accuracy, specificity, range, repeatability, intermediate precision, quantitation limit, and robustness validation protocols. If 1 g per day is the maximum daily intake of a drug product, the technique can be applied to the screening and control of impurities for the most important risk assessment elements such as Cd, Pb, As, Hg, V, Co, and Ni, as well as for important catalyst elements including Ir, Pt, Ru, Rh, and Pd in pharmaceutical materials. The major benefit of the technique is that it reduces the need for complicated and time-consuming chemical digestion procedures and can be a very useful addition as an initial screening tool to reduce the number of samples that need to be analyzed by ICP-OES or ICP-MS.

		International Conference on Harmonization, 

ICH Q3D Step 4 Guidelines: Guideline for Elemental Impurities

		General Chapter <735> “X-ray Fluorescence Spectrometry” in 

United States Pharmacopeia 37-National Formulary 32

 (United States Pharmacopeial Convention, Rockville, Maryland, 2015).

		General Chapter <232> “Elemental Impurities - Limits” in 

United States Pharmacopeia 39-National Formulary 34

 (United States Pharmacopeial Convention, Rockville, Maryland, 2016).

		General Chapter <233> “Elemental Impurities - Procedures” in 

		“The Use of EDXRF for Pharmaceutical Material Elemental Impurity Analysis,” white paper, Shimadzu Scientific Instruments (2016).

 is the Elemental Spectroscopy Product Manager for Shimadzu Scientific Instruments. He received a B.S. degree in Chemistry from Towson State University. He is responsible for managing the elemental spectroscopy products in North and Central America for Shimadzu including: ICP emission systems, atomic absorption systems, X-ray fluorescence and diffraction systems, and total organic carbon analyzers. He has more than 15 years of experience in the industry and contributes to ASTM and Standard Methods through review and submission of analytical methods.

 is the General Manager of the X-ray and Surface Analysis Business Unit for Shimadzu Corporation, Japan. He received a masters degree in Functional Materials from Niigata University. He is responsible for managing the electron probe microanalyzer systems, X-ray fluorescence and diffraction systems, and scanning probe microscope systems in Japan and overseas for Shimadzu. He has almost 20 years of experience in development and as an application engineer of X-ray products including total reflection X-ray fluorescence, X-ray reflectivity, and X-ray absorption fine structure.

Atomic Perspectives

An evaluation of the ability of XRF spectrometry to perform elemental impurity analysis of 12 elements in various pharmaceutical materials

Using XRF as an Alternative Technique to Plasma Spectrochemistry for the New USP and ICH Directives on Elemental Impurities in Pharmaceutical Materials

We finish our examination of the infrared (IR) spectroscopy of the C-O bond with a discussion of carbohydrates. These important biological molecules contain both alcohol and ether linkages, making their C-O stretching regions particularly rich and challenging to interpret. To round out this potpourri I’ve added alkynes, a type of unsaturated functional group for which there wasn’t room in earlier columns. 

 is “a mixture, especially of unrelated objects, subjects etc.” One of the challenges of writing multiple column installments for a magazine is making the topics fit into the space allotted in each issue. More than once in the past there has been a topic that was too short to stand by itself as an installment, but too long to combine with any related topics. Hence the need for the occasional catch-all column like this one, where these orphaned topics can find a home. The reason that this installment is a potpourri is that the topics included here are unrelated to each other. There will be more potpourri columns in the future.

	This is also the 14th installment of this workshop, and in the last two and a half years we have covered a lot of territory. The topics covered in potpourri installments will be new to you but are related to things we have discussed before. Therefore, you will learn something new from these columns while exercising some of what you have already learned, hopefully aiding in your learning process.

	Carbohydrates are an important class of biological compounds including sugars and cellulose. The term was originally coined because their molecular formulas suggested they were “hydrates of carbon” (2), although we now know this is not true. Structurally speaking, carbohydrates contain saturated rings with ether linkages in them, and multiple alcohol groups as shown in Figure 1.

	So far in these columns most of the compounds whose spectra have been examined have been synthetic compounds of industrial importance, but of course biochemicals are important too because they allow life on planet earth to exist! In many of the infrared (IR) spectral interpretation training courses I have taught, one of the most frequent questions has been, “Can IR tell the difference between synthetic and natural materials?” The short answer is no. Many of the most important classes of biochemicals, including proteins, fats, carbohydrates, and nucleic acids, are structurally similar to organic compounds and contain the same functional groups as synthetic chemicals. The spectrum of any functional group will be the same regardless of whether it was made in a living cell or a test tube. For example, the spectrum of a methyl group will be pretty much the same whether it is part of an alkane or a protein. Thus, IR spectroscopy is not a good “natural versus synthetic” discriminator. However, since biochemicals do contain organic functional groups, we can determine their structure by IR spectroscopy the same way we do for synthetic materials.

	The chemical structure of an important and well known carbohydrate, glucose, is shown in Figure 1. 

	Glucose is the product of photosynthesis, and is the molecule many animals burn for energy. Note that it is a cyclic six-membered ring with the shape of a distorted hexagon. Glucose is an example of a monosaccharide, meaning it contains one cyclic sugar unit. Five of the vertices of this saturated ring represents a carbon atom, and the ring also contains an oxygen atom attached to two saturated carbons giving a saturated ether linkage. Additionally, there are also five -OH groups, four of which are secondary alcohols and one of which is a primary alcohol. We have studied the spectra of all these functional groups before (3–5). The plethora of hydroxyl groups means that carbohydrates engage extensively in hydrogen bonding and that there will be strong, broad O-H stretches. The seven separate saturated C-O bonds means there will be a lot of C-O stretching peaks between 1200 and 1000 cm

 (going forward assume all peak positions are in cm

 even if not noted). The IR spectrum of glucose is shown in Figure 2.

	There is a broad, strong O-H stretching envelope in Figure 2 with two peaks at 3402 and 3318. This is the first time we have seen an O-H stretching peak like this. It means there are two chemically distinct alcohol groups in the molecule with different bond strengths, force constants, and hence peak positions. My guess is that one of the peaks is from the secondary alcohols present, and the other from the primary alcohol present. There is also an O-H wagging peak at 606.

	There is a peak at 1622 from adsorbed water, although the peak is small and water’s contribution to the -OH stretching peak intensity would also be small. It is no surprise that glucose and many other carbohydrates absorb water from the atmosphere since they contain a lot of -OH groups, are highly polar, and hence are hygroscopic.

	Four of the carbons in the glucose ring are structurally similar-each being attached to two other carbons, one oxygen, and one hydrogen, with the oxygens each being part of a secondary alcohol. We will call the situation where there is only one hydrogen attached to a carbon a 

. The lone hydrogens in glucose are all saturated and hence will have C-H stretches below 3000 (2). However, these peaks will not be as intense as typical C-H stretches since there is only one bond contributing to the peak intensity instead of two or three as is the case for methylene and methyl groups. Saturated lone C-H groups tend to have their stretching peaks around 2900 (7). The oddball fifth carbon in the glucose ring has a primary alcohol group hanging off it that contains a CH

 moieties that are responsible for the small C-H stretching peaks shown in Figure 2 around 2900.

	Recall that saturated ethers have a C-O stretching peak from 1140 to 1070, secondary alcohols have one from 1150 to 1075, and in primary alcohols this peak appears between 1075 and 1000 (3–5), making for a potentially complex C-O stretching region. As can be seen in Figure 2 there are four peaks in this region. This C-O stretching peak region complexity is typical of carbohydrates, and makes it difficult to assign the C-O stretching peaks of these molecules exactly.

	The IR spectrum of a disaccharide, sucrose, otherwise known as table sugar, is shown in Figure 3.

	Sucrose is a disaccharide because it contains two sugar rings. It consists of a glucose ring attached via a saturated ether linkage to a five-membered fructose ring as noted in Figure 3. Fructose gets its name from the fact that it is a sugar found in fruit (2). Note in the spectrum that there are multiple O-H and C-O stretching peaks, even more so than in the spectrum of glucose. Many carbohydrates consist of multiple sugar rings connected by ether linkages. As these molecules get bigger there are many more types of O-H and C-O bonds, hence the increase in spectral complexity. Also note in the spectrum of sucrose that the C-H stretch and O-H wag peaks have turned into shapeless blobs thanks to spectral complexity and hydrogen bonding.

 are polymeric carbohydrates consisting of many sugar units strung together. Perhaps the most common polysaccharide is cellulose, whose IR spectrum is shown in Figure 4.

	Cellulose is found in the cell walls of plants and is found in our every day lives as wood, paper, and cotton. In fact, the spectrum in Figure 4 is of a paper towel. The cellulose molecule is a series of glucose units stitched together by saturated ether linkages. There is so much hydrogen bonding in cellulose that the complex O-H stretching peak patterns we saw in glucose and fructose are gone, replaced by a broad blob. The C-H stretches are similarly blobby, and the C-O stretching region is best characterized as an envelope with any number of shoulder peaks on top of it.

	Notice then as we proceeded from mono- to di- to polysaccharides the spectra got more complex because of the many types of O-H and C-O bonds and eventually became so complex that the peaks overlapped enough to form blobs. Hydrogen bonding also helped increase spectral band broadening. The spectra of carbohydrates then are characterized by multiple O-H stretches and multiple C-O stretches. This information is summarized in Table I.

	In past columns (10,11) we discussed unsaturated carbons and looked at the spectra of two families of unsaturated hydrocarbons, substituted benzene rings and alkenes. I left out one last family from the discussion, alkynes. Alkynes contain the C≡C structural unit, properly called a carbon-carbon triple bond. These bonds are unsaturated because they can be hydrogenated to form saturated compounds just like aromatic rings and alkenes (2). The two varieties of alkynes, monosubstituted and disubstituted, are shown in Figure 5. The “R-” symbols in Figure 5 represent nonhydrogen substituent atoms.

	A monosubstituted alkyne has one hydrogen and one R-group attached, and a disubstituted alkyne has no hydrogens and two R-groups attached. The simplest alkyne with the chemical formula C

, or H-C≡C-H, is called acetylene. Sometimes alkynes are named as acetylene derivatives. We can distinguish the two types of alkynes from each other using IR spectroscopy. The IR spectrum of a monosubstituted alkyne, 3,3-dimethyl-1-butyne (or 

-butyl acetylene) is shown in Figure 6, and its relevant peak assignments are shown in Table II.

	As we read the spectrum from left to right the first thing we encounter is an intense, relatively sharp peak labeled A at 3311. We have seen in the past (3,4) that O-H stretches fall in this same region. However, recall that these peaks are widened by hydrogen bonding. There is no hydrogen bonding in alkynes, hence the peak at 3311 is narrow and cannot be from an O-H bond. It is in fact the C-H stretch of the hydrogen attached directly to the C≡C group, and this peak generally falls from 3350 to 3250 for monosubstituted alkynes. This is far and away the highest wavenumber C-H stretch we have seen yet and may ever see. Its unique position and sharpness set it apart and make it a great monosubstituted alkyne group wavenumber. The H-C≡C group also has a C-H wag, shown as a big peak labeled C at 631 in Figure 6. This is also an excellent group wavenumber because of its intensity, sharpness, and low peak position. This peak can be found generally from 700 to 600 for monosubstituted alkynes. O-H wags also show up in this region (3,4), but again the sharpness of this peak means it is not from an OH group. The spectra of monosubstituted alkynes then have two sharp, intense peaks at high and low wavenumber, in a sense bookending the spectrum.

	The C≡C linkage in monosubstituted alkynes has a stretching peak between 2140 and 2100, which is shown in Figure 6 labeled B at 2106. This is a very unusual peak position, which should make this peak a useful group wavenumber. However, like the C=C stretch of alkenes (11) this peak has variable intensity and hence limited utility because it may be intense, weak, or nonexistent depending on the molecule. Recall that one of the things that determines peak intensity is the change in dipole moment with respect to bond length (dµ/d

) for a vibration (8). For a monosubstituted alkyne with a polar substituent dµ/d

 will be large and the C≡C stretching peak will be large. For a monosubstituted alkyne with a nonpolar substituent like 

-butyl acetylene, dµ/dx will be small and hence the peak intensity will be small as seen in Figure 6. For a symmetrically substituted alkyne such as acetylene (H-C≡C-H) dµ/d

 for the triple bond stretch is zero and there is no IR peak due to C≡C stretching. The unreliable appearance of this peak makes it a poor group wavenumber. Again, the best peaks to identify a monosubstituted alkyne are the H-C≡C stretch and wag.

	A disubstituted alkyne has two non-hydrogen substituents attached, such as dimethyl acetylene, CH

. Since there are no hydrogens attached to the triple bond, disubstituted alkynes do not have a H-C≡C stretching or bending vibration. Disubstituted alkynes do have a C≡C stretching peak that falls between 2260 and 2190, and this peak can be used to distinguish disubstituted from monosubstituted alkynes. However, just like for monosubstituted alkynes, the C≡C stretch is also an unreliable group wavenumber for the same reasons. For a symmetrically substituted disubstituted alkyne like dimethyl acetylene there are no H-C≡C peaks, and since dµ/d

 for the C≡C stretch is zero there is no triple bond stretching peak. Symmetrically substituted alkynes then are one of the rare organic functional groups that have no useful IR group wavenumbers, and their presence in a sample must be detected by means other than IR spectroscopy.

	In this first IR spectral interpretation potpourri we have looked at the spectra of two dissimilar molecules, carbohydrates and alkynes. Carbohydrates contain many different types of O-H and C-O bonds, and hence have complex O-H stretching and C-O stretching regions from 3500 to 3300 and 1200 to 1000, respectively.

	Alkynes contain C≡C bonds and come in mono- and disubstituted forms. Mono-substituted alkynes contain a unique H-C≡C group with a sharp, intense high wavenumber C-H stretch, and a sharp, intense, low wavenumber C-H wag. C≡C stretching peaks appear uniquely around 2200 but suffer from variable intensity, making them a questionable group wavenumber. This is the only diagnostic peak that disubstituted alkynes possess, sometimes making them difficult to detect.

 (G.C. Merriam, Cambridge, Massachusetts, 1946).

Infrared Spectral Interpretation: A Systematic Approach

		Beilstein Electronic Organic Chemical Database, contents as of 6/2017.

, PhD, is the West Coast Business Development Manager for CAMO Software, a company that sells chemometric, multivariate analysis, and process control software. Before joining CAMO, Dr. Smith ran his own FT-IR training and consulting business for more than 20 years. Dr. Smith has written three books on infrared spectroscopy: 

Quantitative Spectroscopy: Theory and Practice

 published by Academic Press. He can be reached at: 

IR Spectral Interpretation Workshop

Two unrelated discussions are presented: carbohydrates and alkynes.

An IR Spectral Interpretation Potpourri: Carbohydrates and Alkynes

In advance of the Denver X-Ray Conference, we asked leading scientists who will be speaking at the conference about current trends and advances in X-ray fluorescence and X-ray diffraction. Here, these investigators discuss their work to advance these techniques and the sample preparation for such analyses, in areas such as photonics for telecommunications, steel structures, metals in pharmaceuticals, and imaging.

Simplifying Metal Analyses in Pharmaceutical Labs with XRF

	Metal catalysts may be found at multiple points along the synthetic route of a drug product, including starting materials, intermediates, excipients, and in the synthesis of the active pharmaceutical ingredient (API). Although the procedures in the 

International Conference on Harmonisation

 (1–3) govern elemental impurities in drug products, analyses of metal concentrations in starting materials, intermediates, in-process materials, and during process optimization of API synthesis are also beneficial. Since the primary goal of providing life-saving drugs to patients more quickly is often the driving force behind many decisions in the pharmaceutical development process, alternatives to traditional metal analysis methods that can provide more timely results with less required skill are desirable.

	Traditionally, metal analysis in pharmaceuticals is conducted using flame- and plasma-based techniques, including flame and graphite furnace atomic absorption spectroscopy, inductively coupled plasma–mass spectrometry (ICP-MS), and inductively coupled plasma–optical emission spectroscopy (ICP-OES). These techniques offer sensitivity, selectivity, and precision; however, they often require lengthy sample preparation and trained analysts, regardless of the stage of development. Time is an important consideration during the pharmaceutical development process, and often a quick estimate of metal concentration is all that is necessary when deciding the next step in the optimization of a synthetic process. A wish list for an alternative technique includes fast and simple method development, the potential for open access and sample flexibility (including phase and sample size), while still maintaining the required sensitivity for screening of metal content and for trending data. X-ray fluorescence (XRF) provides an alternative approach to determine elemental content. Although portable XRF may not afford the same level of sensitivity as ICP-MS or ICP-OES, it is relatively inexpensive, is easy for an untrained individual to use, requires little-to-no sample preparation, and is nondestructive.

	A portable XRF system was provided for use in process chemistry laboratories to enable rapid screening of metal catalyst content to isolate samples of high metal concentrations and determine trends in metal concentrations during metal catalyst removal processes (4). Through the use of XRF, synthetic chemists were able to generate data in their laboratory within minutes to aid in the design of future experiments. Sample quantity is often limited during the development process; the nondestructive behavior of XRF afforded the recovery of samples after analysis for use in subsequent experiments. Moreover, the volume of samples submitted for ICP analysis was greatly reduced, saving both instrument and personnel time, reducing consumable and reagent costs, and decreasing the potential for instrument contamination by samples with high metal concentrations. Since the deployment of this XRF system, more than 900 samples were analyzed by this XRF screening method, saving approximately two months of development time.

	Portable XRF spectroscopy has proven useful in the screening of effective metal catalyst scavengers during the pharmaceutical development process and for metal analysis at in-process control steps during the synthesis of a pharmaceutical product, results from which have been used in critical decisions. This technique has also shown to be invaluable in foreign matter investigations, metal analysis of excipients and vendor-sourced materials, as well as materials that are not easily solubilized, and in the analysis of in-process control samples during the synthetic process of APIs.

ICH Q3D, Guideline for Elemental Impurities

http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q3D/Q3D_Step_4.pdf)

 (United States Pharmacopeial Convention, Rockville, Maryland, 

http://www.usp.org/sites/default/files/usp_pdf/EN/USPNF/key-issues/c232-usp-39.pdf)

		General Chapter <233> Elemental Impurities - Procedures” in second supplement to 

United States Pharmacopeia 38-National Formulary 33

http://www.usp.org/sites/default/files/usp_pdf/EN/USPNF/key-issues/c233.pdf)

 is a Research Investigator II at Bristol-Myers Squibb in Global Product Development and Supply - Chemical and Synthetic Development in New Brunswick, New Jersey.

Analysis of Beverages by Total Reflection X-ray Fluorescence

	Total reflection X-ray fluorescence spectrometry (TXRF) is a well established and versatile multielement trace analytical method (1). Its ease of use and low detection limits make it an ideal tool for routine analysis of samples ranging from liquids and solids to biological tissues. The project presented in this short article was inspired by undergraduate research experience and showcases TXRF as an undergraduate teaching and research tool. When exposing undergraduate students to research one has to take into account the lack of laboratory experience of the student, in addition the project should spark and retain the interest of the student, and also maintain a high research standard. With this in mind, the subject of study were common beverages. Beverages such as juices are recommended as a means of delivering essential nutrients and trace elements to children and adults alike. Many of them are fortified and most are processed to remove unwanted residues and pulp or are reconstituted from concentrate. We purchased three common fruit juices-apple juice, cranberry juice, orange juice-and one bottle of lemonade for this study and compared their metal content to water obtained from a drinking fountain at the university. All fruit juices were pulp free and none of the beverages were from concentrate. Table I lists the name, origin, and daily values of the beverages stated on the label. 

	Two sample preparation methods were compared with each other to examine the difference in trace metal content between the liquid part of the juice and the solid–liquid mixture that includes plant fibers. For the liquid-only element content, a 5-mL aliquot of the purchased beverages was centrifuged at 9000 rpm for 10 min and 1 mL of the supernatant was analyzed. For the solid–liquid mixture, a 1-mL aliquot of the beverages was diluted with the same amount of ultrapure water after shaking the bottle vigorously and then analyzed. Drinking fountain water was analyzed directly. For all samples gallium was used as the internal standard.

	It was found that each purchased beverage contained a suite of common metals, with potassium and calcium having the highest concentrations and manganese, iron, copper, zinc, rubidium, and strontium present as traces. The concentrations of all detected metals varied not only between beverages, but also between the methods of preparation substantially. Drinking fountain water contained much lower concentrations of potassium and calcium, but comparable concentrations for the other metals were detected. This is not surprising because the plant tissue is enriched with potassium and calcium.

	Figure 1 shows the concentrations of calcium (Figure 1a) and copper (Figure 1b) for all samples. Please note that the calcium concentrations are expressed in units of milligrams per liter and the copper concentrations in micrograms per liter. The fountain water concentration of calcium was a factor of 1000 smaller than for the other beverages. When calculated for the 340-mL bottle content and compared to daily intake values, the freshly pressed apple and orange juices delivered more calcium and copper. In fact, the copper amount delivered by one 340-mL bottle of orange juice is about a quarter of the daily recommended value for children and 10% for adults. (2). The juice that contributed substantially to daily intake values was 100% orange juice and only when the solids or pulp were included in the drink. All other beverages had only marginal daily amounts of the metals detectable by TXRF.

Total Reflection X-ray Fluorescence Analysis and Related Methods,

 2nd Edition (Wiley and Sons, Hoboken, New Jersey, 2015).

Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium, and Zinc

 (The National Academies Press, Washington, DC, 2001) 

 is an Associate Professor and Graduate Program Director with the Department of Chemistry and Biochemistry at Loyola University Chicago in Chicago, Illinois.

In Situ XRD Studies of Microstructural Changes in Steel

	With in situ X-ray diffraction (XRD), microstructure changes in steel can be investigated at elevated temperatures. The method collects large amounts of data in a short time from a single sample, which reduces experimental effort and possible deviations caused by sample preparation. Such experiments can be used to study and optimize annealing treatments of advanced steel grades. 

	A Bruker D8 Discover X-ray diffractometer with a Vantec2000 area detector was equipped with a DHS1100 commercial heating stage from Anton Paar. With the large angular coverage of the area detector, fast measurements are possible with a temporal resolution of down to 3 s. The heating stage is enclosed by a graphite dome that can be filled with different annealing atmospheres. After preliminary testing of different atmospheres (air, vacuum of 10

 mbar, helium, and nitrogen) a mixture of helium with 3% hydrogen was chosen because it offers high thermal conductivity and the hydrogen content prevents surface oxidation but does not damage the oxide heating plate. The maximum temperature of the heating stage is 1100 °C and the maximal heating rate is 400 °C/min.

	In the following section, two examples of in situ XRD measurements of steel are presented.

Recrystallization and Phase Transformation

	Recrystallization and recovery are softening processes that are driven by the reduction of dislocation density of deformed materials. A reduction in dislocation density can be directly determined with in situ XRD by the reduction of X-ray peak widths (1). In the current case, a cold-rolled dual-phase steel was heated to temperatures between 700 °C and 800 °C with a heating rate of 200 °C/min. To derive a recrystallized fraction, the change of the {110}- and {200}-peak widths was normalized. The trend of this recrystallized fraction with annealing time could be fitted with the Johnson-Mehl-Avrami-Kolmogorov (JMAK) function with a high degree of confidence (Figure 1). Hence, the thermodynamic parameters that describe the recrystallization kinetic of this steel could be derived from only three samples within a few hours.

	If the same dual-phase steel was heated to higher temperatures, phase transformation sets in. Unfortunately, a decarburization of the sample surface occurred to a larger extent than the X-ray penetration depth. Because the reduction of carbon content increases the transformation temperature considerably, no reliable results of transformation kinetics could be gained. Most likely the decarburization was caused by residual moisture in the annealing atmosphere. In the future, additional gas cleaning filters will be installed to prevent this decarburization effect.

	To investigate changes of the crystallographic texture caused by the transformation from the low-temperature α-Fe phase (bcc) into the high-temperature γ-Fe phase (fcc), a sample of a bainitic steel was repeatedly heated up to 1000 °C and cooled down to room temperature. As the heating stage is small and flexible enough to allow rotations and tilts of the sample stage, it was possible to measure the complete texture of the sample. For this, three incomplete pole figures were measured at 1000 °C and at room temperature within 20 min. In accordance with other studies (2), a weakening of texture by subsequent phase transformations was observed. Thus, with oscillations around the phase transformation temperature a virtually texture-free microstructure can be obtained. As crystallographic texture is the main cause of plastic anisotropy, such annealing treatments could be used for increasing the isotropy of steels.

	Furthermore, the grain size of the same bainitic steel could also be measured in the high-temperature phase. The aim of this investigation was to determine if the prior γ-Fe grain size can be measured by in situ XRD. For coarse-grained materials the Bragg reflexes recorded by an area detector are not smooth cones anymore, but are spotty. Every spot along the Bragg cone originates from a single grain, and from the number of these spots within a certain angular range, the size of the grains can be calculated (Figure 2) (3). For this calculation the interaction volume of the X-ray beam with the sample has to be known or, as in our case, a calibration with a known standard can be applied. Because the penetration depth of the X-ray was approximately 10 µm and thus smaller than the mean grain size, a two-dimensional (2D) approach was derived. In addition to the mean grain size, the grain size distribution can also be determined because the intensity of each spot along the Bragg cone corresponds to the respective size of a certain grain. From a reconstruction of the prior γ-Fe grain structure from electron backscatter diffraction data (EBSD) data, the prior γ-Fe grain size of the investigated bainitic steel was determined to be larger than 500 µm, while after the reverse transformation into the high temperature γ-phase a grain size of only ~25 µm was determined by in situ XRD. From this result, it follows that the prior γ-Fe grain size is not restored after a reverse transformation into γ-Fe because the change of crystal structure that occurs is diffusive and not displacive (like γ-Fe → martensite or bainite).

	In conclusion, in situ XRD measurements are a versatile tool to observe and understand microstructural changes in steel and optimize annealing treatments.

 (John Wiley & Sons, Hoboken, New Jersey, 2009). 

 is with Salzgitter Mannesmann Forschung GmbH in Salzgitter, Germany. 

 is with the Department of Crystallography at Georg-August University Göttingen in Göttingen, Germany.

High-Resolution X-ray Emission Spectroscopy Without a Synchrotron

Kathryn McIntosh, George J. Havrilla, Mark Croce, Rachel Huber, David Podlesak, Michael Rabin, Fernando Vila, Matthew Carpenter, and Robin Cantor

	Transition-edge sensor (TES) microcalorimeter detectors are capable of high-resolution X-ray emission spectroscopy (XES) that rivals X-ray absorption near edge structure (XANES) spectroscopic probes found only within the confines of synchrotrons. Commercial microcalorimeters offer a spectral resolution of around 5–7 eV, which is comparable to that provided by wavelength dispersive X-ray fluorescence (WDXRF) instruments yet provides the full spectra of the material of interest, not merely a single element, thereby surpassing WDXRF capabilities.

	The bonding of carbon is predicated on the sp

 orbitals. The peak position of the carbon Kα peak, as well as the peak shape, provides information about the bonding of the carbon atoms. In a paper we are presenting at the 2017 Denver X-ray Conference (1), we report the measurement of the carbon Kα peak around 282 eV for several materials of interest, including amorphous carbon, nanodiamond, and graphite, since all three have different carbon forms and sp

 bonding ratios. In addition, a soot specimen from a contained detonation of explosive material was measured to determine the carbon species formed from the detonation. A high-resolution microcalorimeter mounted on a scanning electron microscope was used to measure the carbon peak for these different materials. The powder samples were pressed into indium foil which was then placed onto a stub. The microcalorimeter detector has a resolution of around 5 eV and 16 individual pixels each collecting a full spectrum. The individual spectra from each pixel were summed to obtain increased counting statistics for more accurate peak position measurements.

	In examining the collected spectra for the materials mentioned, it was noted that the peaks are all downshifted from the expected 282 eV position. Secondly, each of the peaks has a distinct position and differences in shape indicating differences in the sp

 ratio for each material. A Gaussian fit to the peaks provides the peak positions. Using the amorphous carbon as the carbon reference point, the nanodiamond shows the largest shift toward higher energy relative to amorphous carbon. The graphite shows a small negative shift toward lower energy relative to amorphous carbon. The soot sample appears to be the most similar to the amorphous carbon, but does exhibit a small positive shift indicating potential nanodiamond character.

	Preliminary theoretical simulations of the XES of these materials using the transition potential approximation in density functional theory indicate similar trends in both the shape and position of the peaks, and support the experimental measurements. We used simple molecular models for the systems of interest that are representative of the different local environments found around the excited centers. The theoretical results predict a clear relation between the shift relative to the graphite peak and the local out-of-plane distortion around the absorbing center, a quantity that is closely related to its sp

	X-ray photoelectron spectroscopy provides the sp

 hybridizations through bombardment of the sample surface with X-rays that cause the subsequent release of electrons with the characteristic bonding energy of carbon: sp

 at 285 eV. Graphite samples show purely sp

 character, and nanodiamond shows a mixture of sp

 (26–39%). The soot sample is a majority 67% sp

 character. Raman spectroscopy provides further information as to the sp

 allotropes and surface functionalization. 

	The microcalorimeter detector offers new X-ray measurement capabilities not found with conventional synchrotrons in that full spectra are measured compared with limited XANES scan ranges from synchrotron measurements. In addition, these measurements can be done within your laboratory. 

		K. McIntosh, G. Havrilla, M. Croce, R. Huber, D. Podlesak, M. Rabin, F. Vila, M. Carpenter, and R. Cantor, “Study of Carbon Bonding with XES Using a TES Microcalorimeter Detector,” to be presented at the 2017 Denver X-Ray Conference, Big Sky, Montana.

Kathryn McIntosh, George J. Havrilla, Mark Croce, Rachel Huber, David Podlesak

 are with Los Alamos National Laboratory in Los Alamos, New Mexico. 

 is with the University of Washington, Seattle, Washington. 

 are with Star Cryoelectronics in Santa Fe, New Mexico.

Making X-ray Fluorescence Movies in the Laboratory Using CCD and CMOS Cameras

	For many years, viewing elements as they move in chemical systems has been a dream for scientists. Because chemical reactions are coupled with the transportation and redistribution of elements, such a capability would be an excellent tool for elucidating many unknown phenomena in pure sciences as well as in medical and industrial applications. X-ray fluorescence (XRF) is a powerful nondestructive technique for determination of chemical composition. So far, however, capturing moving images with XRF, particularly in the laboratory, has been extremely challenging. Although we have reported the creation of some XRF movies using highly brilliant synchrotron sources (1,2), it is extremely important to enable similar experiments in ordinary laboratories, schools, industries, and hospitals. This year, at the Denver X-ray Conference, we will report our latest progress toward this goal (3,4). 

	Roughly speaking, in XRF, one can choose either a wavelength-dispersive X-ray spectrometer, which uses an analyzing crystal, or an energy-dispersive spectrometer equipped with a semiconductor detector such as a silicon drift detector. In XRF still-imaging, one typically uses an energy-dispersive spectrometer to obtain XRF spectra efficiently, while scanning the sample position with a small X-ray beam (created by using a focusing device such as mono- or polycapillary optics). The measurement time to obtain each individual image is quite long, and large pixel numbers are essential to provide enough information. In this situation, movie XRF imaging looks difficult. How can one collect many such XRF images as a function of time?

	The key is to change the XRF imaging strategy. We propose a new approach, which we call projection-type XRF imaging, in contrast to scanning-type imaging. In this approach, instead of making xy scans with a micro X-ray beam, we use a rather large beam to illuminate the whole sample area. All XRF data are collected by an energy-dispersive two-dimensional (2D) detector. To project an XRF image onto the 2D detector plane, we have two options: parallel optics based on a collimator plate, or a simple pinhole camera. One of the remaining problems was how to make a 2D X-ray detector that has a large number of tiny pixels with sufficient energy resolution in the X-ray wavelength region.

	There has been great progress in X-ray detectors. In the present research, we are using cooled charge-coupled-device (CCD) and complementary-metal-oxide-semiconductor (CMOS) cameras, which are designed for visible light applications. It is possible that other advanced 2D X-ray detectors could be used for XRF imaging, but we believe that the use of commercially available cooled CCD and CMOS cameras would be extremely convenient for many users, because they are not very expensive and are easy to obtain. One needs to remove the optical lenses as well as any covers, and affix an X-ray window there instead. The inside of the camera should be evacuated or at least replaced by dried gas because of the necessity of cooling. 

	Then the most likely next question is how to obtain X-ray energy information, which is absolutely necessary for XRF. In XRF instruments, as in other semiconductor detectors, X-ray photons create a large number of charges in a CCD or CMOS camera. Because the amount of charge corresponds to the energy of the X-rays, one can obtain information on impinged X-ray energy. This means that the intensity corresponds to the X-ray energy, if the image is read sufficiently quickly to avoid counting overlapped events. Such quick reading is often called single-photon counting. However, there are still some technical challenges. X-ray photons might not arrive at the center of the single pixel; for example, they may hit the corner of the pixel, in which case the created charges are eventually split into several pixels. Such events become more apparent when the pixel is small, particularly in the case of CCD and CMOS cameras made for visible light cameras. To deal with this problem of charge-sharing events, we are proposing a clever and fast filtering scheme.

	At the 2017 Denver X-ray Conference, we will present our latest scientific XRF movies. For this work, we used a 1.5-kW X-ray tube. The typical energy resolution of our system is around 150 eV@MnKα (depending on the cooling temperature). The spatial resolution is adjustable; we typically set it at 20–50 µm, depending on the XRF intensity of the sample. The time resolution for the movie is still not fast like a synchrotron experiment; it is on the order of a minute. 

	In our presentation, we will provide successful examples that visualize the real-time diffusion of elements such as calcium and iron in some chemical systems, from the beginning of the reaction to the end. One can understand how diffusion proceeds and how it depends on the element and matrix in real time. We hope this work will help to clarify the mechanism of chemical functions and reactions on a specific time scale. This novel XRF movie technique has the potential to open a new era of chemical science.

Special Feature

Several leading scientists discuss their work to advance XRF and XRD techniques.

Trends in X-Ray Techniques

This study focuses on the determination of a suite of Class 1 and 2A elemental impurities (Pb, As, Cd, Hg, Co, Ni, and V) in various over-the-counter antacid tablets by inductively coupled plasma–mass spectrometry (ICP-MS), as defined in the 

International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use 

. The analysis is performed according to the recommended sample preparation procedure and validation protocols laid out in 

 <233>, by optimizing both the microwave digestion procedure and the analytical methodology for these types of sample matrices that contain extremely high levels of calcium, magnesium, and other mineral components.

International Conference on Harmonization

) (2) have aligned the list and levels of 24 elemental impurities in pharmaceutical materials, which will be implemented on January 1, 2018. The elements of toxicological concern have been categorized into three groups based on their permitted daily exposure (PDE) levels, which are defined in 

 <232> (3). To show compliance, manufacturers will need to measure the elemental contaminants by following the analytical methodology described in 

 <233> (4). To supplement these new chapters, the United States Food and Drug Administration (FDA) also issued draft guidance recommendations for the pharmaceutical industry on elemental impurities in drug products that represent its current thinking on the topic from a regulatory perspective (5).

	Elemental Impurities Analytical Methodology

 <233> recommends the use of either inductively coupled plasma–mass spectrometry (ICP-MS), inductively coupled plasma–optical emission spectroscopy (ICP-OES), or any other suitable technique that meets the necessary validation protocols, for the determination of elemental impurities in finished drug products. The analytical capabilities of ICP-MS make it the most suitable technique for performing determinations of the Class 1 elements at these low levels on a routine basis-especially for drug products with a large daily dosage (>10 g/day). Among this category of medications and supplements, antacids present a unique set of analytical challenges for ICP-MS because of their extremely high calcium or magnesium content. High levels of total dissolved solids (TDS) can cause ionization suppression in the plasma as well as instrumental drift and increased maintenance because of deposition within the instrument. However, as shown in this work, these challenges can be overcome with optimized sample preparation and instrumental analytical conditions. A list of Class 1 and 2A impurities and their PDEs defined in 

	The eight antacids chosen for the evaluation represent a typical cross section of antacids available over the counter and are shown in Table II.

 <233> also makes suggestions on a closed-vessel or microwave digestion procedure to get solid samples into solution. A typical digestion for antacid samples uses nitric and hydrochloric acids, but because silica (SiO

) is present in some of the samples, a source of fluoride is required to dissolve the silica-typically hydrofluoric acid or tetraethyl fluoroboric acid.  

	In this work, all samples were digested using a microwave digestion system (Titan MPS System, PerkinElmer Inc.) with standard 75-mL TFM (modified polytetrafluoroethylene) vessels. Approximately 3–5 g of material were crushed and homogenized. Then 0.30 ± 0.01 g of each sample was added to a digestion cup and placed in a digestion vessel, followed by 5 mL of nitric acid (70%), 1 mL of hydrochloric acid (35%), 1 mL of hydrogen peroxide (30%), and 0.5 mL of hydrofluoric acid (49%) when needed. The vessels were allowed to sit uncapped for 10 min to allow for any prereactions to occur safely before being capped and digested following the program in Table III. To evaluate the effect of the sample preparation on analyte recovery, spikes were added to the microwave vessel before the addition of acid.

Antacids present a unique set of analytical challenges for ICP-MS. These challenges can be overcome with optimized sample preparation and instrumental analytical conditions.

Determination of Elemental Impurities in Antacids by ICP-MS According to the Validation Protocols Defined in USP Chapters  and  and ICH Q3D Step 4 Guidelines

Spectroscopy-07-01-2017