Annie T. Carter

An interlaboratory comparison study for the measurement of arsenic species in rice, kelp, and apple juice was carried out. The purpose of the study was to enable participating laboratories to evaluate their analytical capability to determine inorganic arsenic, arsenite, arsenate, monomethylarsonic acid, and dimethylarsinic acid, assess the intercomparability of the data generated, and look for any correlation trends between the results and the analytical procedures used. Approximately, 66% of laboratories achieved an overall score of 3 (good) or 4 (outstanding), based on the absolute 

-value statistical analysis procedure, indicating that there was generally good agreement between the labs reporting arsenic speciation data for food matrices. However, only 15% of laboratories achieved an overall score of 4, which could be indicative that further method development needs to be carried out to achieve a higher level of consistency across multiple laboratories worldwide using different analytical procedures. 

	This interlaboratory comparison study for the determination of arsenic and arsenic species in rice, kelp, and apple juice was initiated to provide a reliable means for laboratories to evaluate their competency in the analysis of arsenic species in these matrices, as well as a metric for assessing the intercomparability and validity of the data generated by different laboratories. An additional objective of the study was to look for any correlation trends between the matrices analyzed, species determined, and the analytical procedures used by the different participating laboratories.

	The study materials included white rice flour, brown rice flour, kelp powder, and apple juice. Participants were asked to analyze the samples for arsenic (As) species using the methods that are commonly used in their laboratory. Participants were asked to report results for as many of the following analytes as possible, based on their analytical methodology: total As in the sample, total arsenic in the speciation extract (if applicable), inorganic As, As(III), As(V), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA). Participants were asked to measure and report the total arsenic concentration in the sample and in their speciation extract for the purpose of determining extraction efficiency.

	Some of the key features of the study were a broad invitation to participate; no participation fee; a large group of participating laboratories from around the world; anonymous data submission, analysis, and reporting; and the inclusion of analytical method reporting. In total, 46 laboratories from 15 countries registered to participate and were sent the study materials, and 41 datasets were received from 39 laboratories (two of the laboratories submitted datasets by two different methods).

	Four materials were studied, as described here:

	Milled white rice flour and brown rice flour were provided by the Dale Bumpers National Rice Research Center (part of the U.S. Department of Agriculture [USDA] Agricultural Research Service) in Stuttgart, Arkansas. The rice flours were grown in the United States and were identified as "2012 Stuttgart, AR/USDA ARS CLXL745 Row Irrigation 40% FC." The rice flours were analyzed before use in this study, and the total arsenic concentrations met our criteria for use of greater than 5 ppb.

) was purchased commercially from the bulk foods section of a natural food co-op in Seattle, Washington. This organic product was harvested from a remote bay off the northwest coast of Iceland. According to the manufacturer, the kelp was grown deep in the tidal zone, away from the shipping lanes, where it would hypothetically not be exposed to the pollutants present in more industrialized areas. The kelp powder was analyzed before use in the study, and was found to be high in total arsenic (approximately 50–100 ppm).

	The apple juice was purchased commercially from a grocery store in Seattle, Washington, and was 100% pure juice from apples grown in the United States. The juice was analyzed before use in this study, and the total arsenic concentration met our criteria for use of greater than 5 ppb.

	The rice flour and kelp powder samples were thoroughly homogenized, and aliquots were placed in 50-mL homopolymer polypropylene vials. Juice samples were distributed into 125-mL high-density polyethylene (HDPE) bottles. Both the vials and the bottles were pretested and found to be from a lot that was low in total arsenic. Vials and bottles were individually double-bagged in zip-type bags and stored in cardboard shipping boxes.

	All samples were shipped to the participating laboratories during the first two weeks in April 2013. Participating laboratories were asked to analyze samples for total arsenic and arsenic species in accordance with their standard operating procedures and were given no further guidance on analytical methodology, although they were also asked to supply details of their standard methodology. However, many of the laboratories that used inductively coupled plasma–mass spectrometry (ICP-MS) for carrying out total As determinations also cited two well-established publications that coupled ICP-MS to high performance liquid chromatography (HPLC) for the determination of As species, as the basis for their speciation analytical methodology (1,2).


	Figure 1: A typical configuration for HPLC coupled with an ICP-MS system.

	The vast majority of participating laboratories in this study used the HPLC–ICP-MS approach for carrying out the analyses. This trace elemental determination of arsenic species in biological samples has been used with great success for more than 20 years (3,4). Some of the early research showed that it was possible to separate and detect extremely low levels of both inorganic and organic arsenic compounds by coupling an HPLC system to an ICP-MS. In this technique, the species of interest are separated from one another by injecting the sample matrix into an HPLC system and then passing the separated eluent into the ICP-MS system for detection. Through optimization of the chromatographic separation procedure, column material, elution parameters, and ICP-MS operating conditions, the different species can be separated to produce a time-resolved elemental species chromatograph, where the separated peaks can be detected, measured, and quantified. A typical configuration for an HPLC–ICP-MS system is shown in Figure 1. A time-resolved chromatogram of five As species - arsenobetaine (AsB), dimethylarsinic acid (DMA), As(III), monomethylarsonic acid (MMA), and As(V) - is shown in Figure 2.


	Figure 2: An example of a time-resolved HPLC–ICP-MS chromatogram of five 10 ppb arsenic species standards using a Hamilton PRP X100 column.

	Participants were asked to report the following results, or subset of results based on their methodology:

		Total As in the extract (if applicable)

	Other arsenic species, such as AsB, or unknown arsenic species were also reported by some laboratories.


	Table I: Details of the sample preparation methods used to determine total As

	All results were reported to an independent third party, EcoChem, Inc., which is a data validation company that had no role in the study other than data management. At EcoChem, the dataset was compiled, and a unique identifier was assigned to each laboratory before it was transmitted to Brooks Rand Labs. Following the release of the study results (6), each participating laboratory received its own unique identifier, but identifiers were not disclosed to any other parties, including Brooks Rand Labs. This research design ensured there would be no bias by the preparers of this report against any participating laboratory and that participants could submit data with the comfort of anonymity.


	Table II: Details of the sample preparation methods used to determine total As

	For the analysis of total As, the majority of participating laboratories used microwave digestion and ICP-MS, while two of the laboratories used heated graphite furnace atomic absorption (HGFAA) and one laboratory used high-resolution (HR) ICP-MS. Tables I and II give details of the sample preparation and analysis methods used to determine total As. Tables III, IV, and V give a breakdown of the As speciation extraction, separation, and detection methods, respectively.


	Table III: Laboratory breakdown of the As speciation extraction methods used

	Each laboratory was asked to report an analytical result, detection limit, and date of analysis for each sample and analyte. These data were the basis of the scoring used to evaluate the intercomparison study. In addition, each laboratory reported information on the sample preparation, extraction method, separation technology, analytical methodology, and equipment used. These data were used to compile assessments of the performance of various analytical methods, but were not used in laboratory scoring.


	Table IV: Laboratory breakdown of the As speciation separation methods used

	Statistical data analysis was performed following the method favored by the United States Geological Survey's (USGS) Standard Reference Sample Project, with data evaluation using nonparametric statistics (5). This statistical approach was chosen because it is median-based and therefore unaffected by the influence of outliers.


	Table V: Laboratory breakdown of the As speciation detection methods used

-value for each result is calculated using the following equations:

-value assigned to each result for the purpose of assigning a rating; 

 = the median value reported by all laboratories (excluding values below the reported detection limit); 

-pseudo-sigma (approximates the standard deviation of traditional statistics when the data has a Gaussian distribution); calculated by dividing the interquartile range (or fourth-spread) by 1.349. The 1.349 value is derived from the number of standard deviations that encompasses 50% of the data; and 

 = the interquartile range (the difference between the first quartile and third quartile of a set of data).

	Participating laboratories were requested to report undetected (u) values as being less than their detection limit. If a value was less than that laboratory's reported detection limit, then that value was omitted and the "u" qualifier was added. For these samples, performance was not rated, with the following exception. If the laboratory's detection limit was less than the median value reported by all laboratories and had a 

-value greater than 2, then the laboratory would receive a rating of 0 for that analyte (potential false negative).

	To assign a score to each laboratory's performance, ratings were assigned based on each laboratory's absolute 

-value for each analyte, as listed in Table VI.


	Table VI: Ratings of laboratory performance based on calculated absolute Z-values

	Scores were not assigned if the overall number of data points for the analyte and sample in question (omitting values that were less than the reported detection limit) was less than seven or when the calculated 

	The results reported by each laboratory for each of the material types can be found in the final Brooks Rand Labs report (6), along with the median (

) value, and number of laboratories reporting results above their detection limit (

	The complete data set will not be reported in this article, but a summary of the 

 values for each analyte are listed in Table VII. Values in parentheses were associated with 

 values, or with datasets with less than seven results; therefore, the variability in the data was too high or the number of results was too small to use the 

 value as a consensus value for the purpose of scoring the individual laboratories' results. A reported result of "ND" means that all reported values were less than their associated detection limits. It should also be noted that two laboratories reported measuring unidentified arsenic species at levels above their detection limit in the kelp powder sample.


	Table VII: A summary of the median values (M) for each analyte of each studied material in μg/kg (ppb)

	The arsenic speciation results as a fraction of the total arsenic in the extract are shown in Figure 3. For the rice samples, the inorganic arsenic made up about 80% of the total arsenic in the extract, with the sum of the species totaling just over the result for total arsenic in the extract (shown as >100%). This is consistent with inorganic arsenic results reported in the literature (7,8). For the kelp powder sample, the sum of the species totaled approximately 50% of the total arsenic found in the extract, indicating that there were forms of arsenic present that were extracted but not determined by the analytical techniques used. With the apple juice extract, nearly 95% of the arsenic found was in the inorganic form.


	Figure 3: The mean arsenic speciation results for each matrix as a fraction of the total arsenic in the extract.

	Of the 47 laboratories that agreed to participate in this study, 46 of them received the study materials; one laboratory in Mexico was not able to import the materials. Of the 46 laboratories that received the study materials, 41 datasets were received from 39 laboratories (two laboratories reported data by two methods). The list of the participating laboratories can be found in the final report. Of the participating laboratories, only 11 laboratories reported all of the requested parameters in every material type. Nearly half of the laboratories (20 out of 41) did not report results for both total arsenic in the sample and total arsenic in the extract; therefore, extraction efficiencies for those laboratories could not be calculated. In fact, four of the laboratories (numbers 6, 19, 26, and 27) did not report any total arsenic data.

	Of the 39 participating laboratories, approximately one-third of them were from outside of North America. Of the North American participants, all were from the United states, with the exception of one laboratory from Canada. Of the laboratories from the United States, nine (38%) were private commercial or industry testing laboratories, eight (33%) were state laboratories representing seven states, five (21%) were laboratories associated with the U.S. Food and Drug Administration (FDA), and two (8%) were university laboratories. There were eight participating laboratories from Europe (21%) representing five countries. In addition, there were four participants from Asia and one each from Australia and New Zealand. Table VIII summarizes the regional participation in this study.


	Table VIII: Number of participating laboratories by region

	In total, the participating laboratories generated results comprising 546 data points, which were used for scoring. The mean scores for the various analytes were relatively consistent, with an overall score for the study of 2.6, and with mean scores ranging from 2.6 for the total As in the sample to 2.9 for the DMA. This can be seen more clearly in Table IX.


	Table IX: The mean scores for each analyte for all material types

	Similarly, the mean scores for the sample matrices were also relatively consistent, ranging from 2.5 for the brown rice flour to 2.9 for the kelp powder, as shown in Table X.


	Table X: The number of laboratories receiving each score for each of the sample matrices analyzed in the study, along with the mean score for each matrix

	Figure 4 groups the participating laboratories based on the performance scores for each of the studied materials. The distribution of average scores for these four matrices were quite similar, with an average score of 2.7 for the white rice flour, 2.5 for the brown rice flour, 2.9 for the kelp, and 2.6 for the apple juice. The scores for all matrices combined are summarized in Figure 5. These data generated an overall mean score for the study of 2.6.


	Figure 4: Numbers of laboratories per mean score for the four samples studied. Note: nm = not measured.

	It should be emphasized that the majority of laboratories participating in this study (66%) achieved an overall score of 3 or 4 (good or excellent) indicating that there is generally good intercomparability amongst most laboratories that are reporting total arsenic and arsenic speciation data for these matrices. In total, 93% of the laboratories received an overall score of 2 (acceptable) or better, and only two laboratories (7%) received scores of 1 or 0 (poor or unacceptable). However, with only six laboratories (15%) receiving an overall score of 4, it is clear that further analytical method development is required to achieve a high level of consistency across multiple laboratories using various methods worldwide.


	Figure 5: Numbers of laboratories per mean score for all test matrices combined.

	It's also important to keep in mind that the studied matrices do not have "true" or "assigned" values because they are not certified reference materials. The median of all results was used as the most probable value (

-value) for each analyte. Therefore, if many laboratories used the same method, and that method is prone to errors from species conversion or species coelution, then the resulting 

-values have the risk of being biased. This is exemplified by the fact that many laboratories used a nitric acid sample extraction for the speciation analyses, which has the potential to cause oxidation of some or all of the As(III) species to As(V) (2). In addition, when extracts of samples containing significant levels of arseno-sugars or AsB (for example, sea plants) are analyzed on some LC columns, there is a strong possibility of coeluted As(III), AsB, and other large organic arsenic molecules such as dimethylarsinic acid (DMA) (2). For that reason, if the appropriate steps are not taken, these types of interferences will most likely produce erroneous results. This is exemplified in the chromatogram in Figure 6, which shows that the first eluted peak is probably an overlap of AsB, DMA, and As(III). The second small peak is monomethylarsonic acid (MMA), while the final peak eluted peak is As(V).


	Figure 6: Chromatogram of the separation of different arsenic species on an ESI CF-Cr-01 column, showing the AsB, DMAs, and As(III) being coeluted (chromatogram provided by unidentified participating laboratory).

	For the determination of arsenic species in any solid food matrix, a successful extraction method must meet three important criteria:

		It must be capable of extracting all the arsenic species from the matrix.

		It must preserve both oxidation and complexation states, unless only the inorganic species are of interest.

Articles

An interlaboratory comparison study for the measurement of arsenic species in rice, kelp, and apple juice was carried out. The purpose of the study was to enable participating laboratories to evaluate their analytical capability to determine inorganic arsenic, arsenite, arsenate, monomethylarsonic acid, and dimethylarsinic acid, assess the intercomparability of the data generated, and look for any correlation trends between the results and the analytical procedures used.

Annie T. Carter

Articles

An Interlaboratory Comparison Study for the Determination of Arsenic and Arsenic Species in Rice, Kelp, and Apple Juice