Helmut Ernstberger

To address the challenges associated with a broad, complicated food and beverage supply chain, innovative solutions for contaminant detection are continually being sought. Easy-to-operate instrumentation and methodology can be adopted over the entire food supply chain, allowing contaminated food samples to be identified more readily. Here, we describe a simple method capable of identifying and quantifying a diverse range of elements, including trace metals that are toxic in low concentrations, and also the major elements that are essential to healthy sustainable life. Inductively coupled plasma mass spectrometry (ICP

MS) is the definitive technique used to identify and quantify potential metal contaminants; here it has been applied to the metals present in samples of milk powder and evaporated or condensed milk. The ICP

MS method used enabled the detection of metals with quantitation limits to the order of parts per billion. It was found that all milk samples contained elemental concentrations below those levels directed by regulation, with milk powders containing the highest concentrations of major metal elements.

	Mass spectrometry has increasingly become associated with the detection of contaminants and nutrients in food, overtaking other less common methods of detection. Under the umbrella of mass spectrometry are inductively coupled plasma mass spectrometry (ICP-MS) and liquid chromatography and tandem mass spectrometry (LC–MS/MS), which have both been adopted increasingly by researchers throughout the world to identify and quantify contaminants and nutrients in food. As demonstrated by the 2017 fipronil contamination scare (1), there is an industrial necessity to provide reliable analysis methods that can be applied by any food scientist within any processing facility. In tandem with the detection of contaminants, food must also be nutritious, supplying consumers with the essential components of life, such as the metals that are involved in important biological processes.

	Elements are the building blocks of life, and we need to ensure that we receive the right mixture of elements at the appropriate dosage for our continued sustainable development and healthy growth. Of the 118 elements on the periodic table, the majority are metals. For humans, receiving the appropriate levels of certain metals is vital to our existence. Iron, for instance, is an essential component of hemoglobin, and assists in transporting oxygen throughout the body. Other major elements include sodium, potassium, magnesium, copper, and zinc, all of which facilitate a range of biological functions and roles within the human body, making them nutritionally essential. Therefore, in order to function appropriately, we need to be sure we are accumulating the right mixture of elements at the appropriate levels. Dosage of any element is important; an element must be present at a safe enough level to provide functionality, but not so high that it becomes toxic. This means reliable quantitation is essential.

	However, there are also those elements that are toxic even in low concentrations. For instance, the metalloid arsenic (2), and heavy metals such as mercury (3), lead (4), and cadmium (5) have been associated with a variety of health issues. While heavy metals occur naturally, their release into the environment is often a result of anthropogenic activities that are commonly industrially related (6), which can facilitate their dispersion into the food chain. To address the variety of health issues associated with heavy metal contamination, government or independent health experts have imposed maximum levels of metals in foodstuffs to further protect the public.

	ICP-MS itself is becoming renowned as the method of choice for metal analysis across a variety of applications from pharmaceutical development and cancer research (7) to environmental and food analysis (8,9). Specifically considering its use in the food industry, ICP-MS has been used across a variety of applications such as arsenic detection in rice (10). One of the specialties of ICP-MS is its intrinsic use of plasma that completely ionizes the entire sample, ensuring the detection of all metals or analytes. Methods such as atomic absorption spectroscopy and ICP-OES have been successfully applied to the analysis of metal elements within samples of food (11). However, both these methods lack either the multi-element detection capabilities or the sensitivity that ICP-MS offers.

	For food samples, the ability to identify and quantify the various metal elements within a given sample is essential both for contaminant detection and accurate nutrient reporting. Here, we describe a fast and simple method capable of detecting a dynamic range of elements to a high level of reliability and accuracy within various samples of milk with variable fat levels. Milk is well known as a source of nutrition in the early development of children. During these early stages, confidently knowing the quantities of nutritional elements being ingested is critical to healthy growth. This underpins the need for a useful methodology that can provide simple solutions to food scientists across the globe (12).

	Six samples of milk were purchased from local stores for analysis. Samples were chosen to provide a concise coverage of various types of milk with a varied concentration of fat. The forms of milk used were: skimmed milk powder (non-fat), light evaporated milk (4% fat), evaporated milk (9% fat), sweetened condensed milk, skimmed milk (ultra-high temperature (UHT) milk, <0.5% fat), and semi-skimmed milk (UHT, < 2% fat). For experimental controls, three certified reference materials were also obtained. Two European Reference Materials (ERMs) were obtained from the Joint Research Centre of the European Commission, denoted as ERM-BD 150 and ERM-BD 151. The National Measurement Institute of Japan (NMIJ) sample, NMIJ 7512-a, was obtained from GL Sciences B.V.

	Milk samples of varying weights (5 g of UHT milk, 2 g evaporated milk, 1 g condensed milk, and 0.5 g milk powder) were digested with a combination of concentrated 2.5 mL nitric acid (Fluka, TraceSelect Ultra) and 2.5 mL 30% hydrogen peroxide (Sigma-Aldrich, H

 ≥30%, for Ultratrace analysis). Samples were made up to equivalent volume by the addition of water, which was altered to account for the varying concentrations in the different types of milk (0 mL for UHT milk, 3 mL for evaporated milk, 4 mL for condensed milk, and 5 mL for milk powder). Samples were then heated using a Titan MPS Microwave Sample Preparation System using standard 75 mL vessels. Digests were transferred to 50 mL autosample tubes and spiked with 10 µL of 1000 mg/L gold solution before being made up to 50 mL with deionized water.

	Analyses were performed using the NexIon 2000 P ICP-MS instrument (PerkinElmer) with sample detection achieved using a collision mode to reduce elemental interference. The technical parameters for the instrument are reported in Table I. No modifications were made to the instrument and the default sample introduction system was used.

Detection of Major and Trace Elements in Milk Samples

	Before we discuss the analysis of store-bought milk samples in detail, the methodology of sample preparation and comparisons between the control and current methods should be explained in further detail. The first important step in preparing samples for analysis is the removal of the fat content. Milk samples vary in the quantity of fats. For instance, skimmed milk powder is considered to contain no fat, whereas evaporated milk typically contains 9% fat. Excess fat in the samples will negatively affect the performance of the ICP-MS. Therefore, excess fat must be broken down prior to sample analyses to ensure quality and consistency between samples and the uninhibited detection of the elements.

	To break down fats, the sample is digested. While there are several conceivable methods of achieving this, the method employed here involves a mixture of nitric acid and hydrogen peroxide. This mixture breaks down the proteins and makes the sample more viscous. It is then necessary to heat samples, which facilitates digestion. The method used here involves the use of a microwave to ensure uniform heating across the entire sample. An alternative could involve using heating blocks, however, this might result in uneven heating.

	Following digestion, the certified reference materials are analyzed to demonstrate the accuracy of the methodology being used. The results of NMIJ 7512-a milk powder sample and ERM-BD 150 and ERM-BD 151 skimmed milk powders are reported in Tables II and III, respectively. Recovery rates (the difference between the measured and certified samples) are in the range of 89–107%, 93–105%, and 94–99% for the three CRM samples, respectively. These excellent recovery rates demonstrate an overall agreement between the control experimental levels and the presently employed methodology, meaning we can be confident in the elemental breakdown of the milk samples.

	Next, the limits of quantitation (LOQ) must be discussed further. These limits are essential in order to identify the limitations of the instrumentation and determine whether results obtained are trustworthy. The limits of UHT milk samples were calculated following recommendations of the Commission Regulation (EC) No 333/2007 (13) as 10 times the standard deviation of 10 consecutive blank measurements. Milk powder LOQs were taken as 100 times the limit of the UHT samples.

	One of the challenges of using ICP-MS is in the potential for the interference of elements that can lead to false positive element detection. This is a common issue, and is the result of cross-reactivity between various other species. For instance, 

Fe (14). In this example, to prevent false detection of iron, the argon oxide analyte needs to be removed. This is where collision cells become useful. Collision cells work on the principle that the interfering ion, in this case ArO

, is much larger than the analyte, in this case Fe

. Upon ionization, both species travel through a stream of inert gas. The larger analytes are more likely to collide with gas particles that remove them from the mass spectrometer instrument. The intensity of all analytes is reduced, including the desired analyte as a result of a low proportion of collisions with the inert gas stream. However, any analyte intensity reduction is offset by the reduction in the intensity of the interference.

	Analytes of any form are then compared against regulation approved levels to ensure that the food is safe. Levels of specific metals must be within defined limits, typically sub parts per million. As defined by the Commission Regulation (EC) 1881/2006, the regulated levels for lead and tin in milk are 0.02 and 50 mg/kg, respectively (15). In this instance, it was possible to detect metals to the level of singular parts per billion, well below the relevant limits. The measured LOQs for lead and tin in milk powders are 0.0017 and 0.012 mg/kg, respectively, several orders of magnitude below the limits. In addition, the LOQ for mercury was found to be 0.0083 mg/kg, indicating the potential to detect ultralow levels of mercury in milk. LOQs to this order of magnitude below the regulatory limits add credence and confidence to the results of the experiment.

	Using the methodology outlined in the experimental section, the store-bought samples were analyzed to identify and quantity a range of elements. This included the so-called essential major elements such as sodium, magnesium and calcium, amongst others, and also the toxic trace elements such as mercury, cadmium and lead. Measured concentrations for the various samples of milk are reported in Figure 1. Any sample with a reported measurement below the LOQ has not been plotted.


	Figure 1: Results of analysis for store-bought samples (milk powder in black; evaporated milks in shades of orange; UHT milks in shades of blue-grey). Values below the LOQ have not been plotted.

	From Figure 1, it is clear that the concentrations of major elements are highest in milk powder samples, followed by condensed and evaporated milk samples and finally are lowest in the UHT samples. Certain spikes in samples can potentially be explained as a result of the storage method of the milk. For instance, the elevated levels of tin in evaporated and condensed milk have been hypothesized to be a result of their storage in tin-steel cans. This phenomenon is not unique to milk, and in fact has previously been detected in other examples of canned foods (16).

	Fundamentally, this method offers an advantageous analytical technique for the analysis of metal elements over previous instrumentation and methodology due to its twofold advantages. First, it presents a simple method to reliably quantitate a dynamic range of elements that include both trace toxic metals and those essential for health and well-being within milk samples. Secondly, it demonstrates the ability to implement a relatively simple methodology for the employment of metals detection.

	This method provides an analytical technique for the analysis of metal elements in milk demonstrating a twofold advantage. First, it provides a simple method for quantitative analysis over a wide dynamic range of elements, including both toxic metals and those metals essential for health and well-being. Secondly, it demonstrates a simple methodology for a wide spectrum detection of metals.

	(1) European Commission Fipronil Factsheet, 2017, available at: 

https://ec.europa.eu/jrc/en/publication/brochures-leaflets/fipronil-eggs-factsheet-december-2017

https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2009.1351

https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2004.34

https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2010.1570

	(7) E.M. Brouwers, M. Tibben, H. Rosing, J.H.M. Schellens and J.H. Beijnen, 

http://aaes.auburn.edu/wrc/wp-content/uploads/sites/108/2015/11/ICP-MS-review-.pdf

	(9) The Analysis of Food Substances by ICP-MS, 2008 

http://www.spectroscopyonline.com/analysis-food-substances-icp-ms

	(10) PerkinElmer application note, Arsenic Speciation Analysis in White Rice by HPLC/ICP-MS Using the NexION 300D/350D, 2014, available through the following link: 

http://www.perkinelmer.co.uk/lab-solutions/resources/docs/NexION300D-ArsenicSpeciationInWhiteRice.pdf

	(11) PerkinElmer application note, Determination of Lead and Cadmium in Foods by Graphite Furnace Atomic Absorption Spectroscopy, 2014, available through the following link: 

http://www.perkinelmer.co.uk/lab-solutions/resources/docs/APP_PinAAcle-900H-Lead-Cadmium-in-Food-011965_01.pdf

	(12) R. Pilarczyk, J. Wójcik, P. Czerniak, P. Sablik, B. Pilarczyk and A. Tomza-Marciniak, 

	(13) Commission Regulation (EC) No 333/2006, Laying down the Methods of Sampling and Analysis for the Official Control of the levels of Lead, Cadmium, Mercury, Inorganic Tin, 3-MCPD and benzo(a)pyrene in Foodstuffs, Official Journal of the European Union, European Commission, 2007, L 88, 29 

https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1529508094989&uri=CELEX:32007R0333

	(14) PerkinElmer technical note, The 30 minute guide to ICP-MS, available through the following link: 

https://www.perkinelmer.com/lab-solutions/resources/docs/TCH-30-Minute-Guide-to-ICP-MS-006355G_01.pdf

	(15) Commission Regulation (EC) No 1881/2006, Setting Maximum Levels for Certain Contaminants in Foodstuffs, Official Journal of the European Union, European Commission, 2006, L 364, 5 

https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1529507427510&uri=CELEX:02006R1881-20170728

is an inorganic field application specialist at PerkinElmer, in Milan, Italy. 

 is the ICP-MS portfolio director at PerkinElmer in Toronto, Ontario, Canada. Direct correspondence to: 

Articles

The method described here is a simple method for detection of a wide spectrum of metals and for quantitative analysis over a wide dynamic range of elements, including both toxic metals and those metals essential for health and well-being.

A Simple and Effective ICP-MS Method Capable of Detecting Trace and Major Elements in Milk Samples

Speciation analysis of elemental contaminants in food and beverages has received a lot of attention in recent years. Recent regulations limit inorganic arsenic, taking into account that arsenic toxicity is dependent on the species present. Thus, the analysis procedure needs to be able to differentiate inorganic from organic arsenic forms. Liquid chromatography–inductively coupled plasma-mass spectrometry (LC–ICP-MS) is commonly used for the separation and detection of arsenic species, with the most widely used implementation based on ion exchange and characterized by relatively long run times. Testing of increasing sample numbers means that analysis speed becomes a focal point for potential improvements. We developed a method based on ion interaction chromatography, allowing a reduction in run times to <3 min. The method was applied to a range of food and beverages samples. Here we discuss the results of these analyses and associated method validation tests.

	With the expanding knowledge of element species and their distribution in foods, elemental speciation analysis has received increasingly wider attention by the food industry. The relevance of speciation analysis for the elements As, Se, Cr, and Hg has long been established based on the significant toxicity differences between the species of these elements. For arsenic, the number of identified species found in food is still growing, as evidenced by new research findings into arsenolipids (1); therefore, our knowledge of the biochemistry and toxicity of arsenic-containing biomolecules is also still expanding. It is a very active research field, and most new findings have been made in aquatic species and seafood. For terrestrially grown food, the number of arsenic species is more limited. For this report we investigated rice and juice, which have been the target of recent regulatory oversight that singles out and limits the toxic inorganic arsenic forms.

	The increased demand for testing requires methods that are fast, sensitive, and sufficiently robust to cope with the matrix changes associated with various food products being subjected to extraction and analysis. Various methods exist to analyze for inorganic arsenic, including hydride generation coupled to spectrometric detection (2) and the development of field kits (3). The gold standard for this analysis, however, is liquid chromatography–inductively coupled plasma-mass spectrometry (LC–ICP-MS). It is the most widely used method for arsenic speciation (4) and is investigated in this study. The methodology to separate and quantitate common arsenic species by LC–ICP-MS is mature and well established. However, one disadvantage of current LC–ICP-MS methods is their relatively long run times, even for food matrices where the speciation is not complex.

	Ion-Interaction Chromatography or Ion Pairing: Differences and Novelties

	For the food examined in this paper, the number of arsenic compounds quantitated is typically restricted to four species: As3, As5, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA). These are ionizable compounds, and the LC separation techniques are therefore based on ion-exchange or ion-pair chromatography. The latter is also referred to as 

 to more correctly denote the wider scope of the interactions encountered beyond formation of ion pairs (5). Nevertheless, Cecchi still titled her book “Ion Pair Chromatography” because of the widespread recognition of the term (5). The term 

 is emphasized in this article to better highlight the difference in this approach compared to conventional ion-pair chromatography. Some of the pros and cons of ion-exchange and ion-pair chromatography have been summarized earlier (6). Among other factors, ion interaction approaches may use less-expensive columns with well-characterized stationary phases, while methods based on ion exchange may suffer less from matrix interferences.

	Ion-interaction chromatography gives great flexibility in tailoring the separation to the speciation analysis in terms of ion-pairing reagents to use for the separation. Whereas ion-exchange separations are based mainly on electrostatic interactions, the retention behavior of analytes in ion-interaction mode may be more complex and involve more than one retention mechanism (5). In addition to electrostatic interaction of analyte ions with the electrical charge imparted by the ion-pairing reagent to the stationary phase, retention may also occur through interaction of uncharged analytes with the nonpolar component of the stationary phase or interaction with residual silanol groups.

	Both traditional anion-exchange and anion-pairing methods for the separation of As3, As5, MMA, and DMA use circumneutral or alkaline conditions to ionize most of the target analytes, resulting in run times typically ranging from 9–15 min. Reducing the run time required by anion exchange is still a pressing issue and the subject of recent research (7). The traditional ion-pairing approach for this problem involves anion-pairing reagents, such as tetrabutylammonium hydroxide (8). Our approach investigated here differs in that we use a cation-pairing reagent in a mobile phase at acidic conditions. The purpose of the cation-pairing reagent is not to interact with cations, but rather to repel like charged anions.

	It has previously been noted that when working with cation-pairing reagents the separation of early eluted anions or neutral molecules, such as those studied here, can be achieved (9). The problem was, however, that the cationic analytes eluted at long retention times, leading to attempts of combining two ion-pairing reagents in the same solution to shorten the elution times of the analyte cations while trying to maintain the separation of the early eluted species (9). Similarly, Miyashita used a mixture of ion-pairing reagents, with a less effective, shorter-chain cation-pairing reagent to decrease the retention of cations (10). The resulting compromise was, however, not ideal in those cases and was characterized by loss of baseline resolution for some of the early eluted peaks. Reproducibility problems ensued when implementation was attempted in different laboratories, and these techniques did not catch on. The aim of those analyses was to also determine cationic species such as AsC. They, therefore, did not focus on a dedicated optimization for the separation of the early eluted species for situations where cationic species are absent as target analytes. The benefit of the fast run times of the approach explored here was yet untapped.

	Ion-Interaction Chromatography: Method Development

	To make the output from the column amenable to introduction to the ICP-MS system, we considered in our optimization both peak separation and minimizing the dissolved solids content of the mobile phase. Using the conditions in the study by Miyashita and colleagues (10) as a starting point, we changed the cation-pairing reagent to octane sulfonate (OSA), omitted the anion pairing reagent, retained malonic acid as a buffer, and used the same column. However, the pH, OSA concentration, and malonic acid concentration were reoptimized to separate the four species As3, As5, MMA, and DMA. Furthermore, arsenobetaine was included in the study on the sideline to possibly serve as an internal standard and explore the potential to expand the technique to seafood analysis. Accurate species quantitation in seafood was hindered by the early elution of large amounts of AsB in anion exchange techniques, prompting the development of fast techniques that aim to only quantitate inorganic As in LC runs (11). The late elution of AsB after the other analyte peaks may be an attractive feature here. Proper pH selection is crucial as it is in other ion-pairing or ion-exchange applications. At the experimentally determined optimum pH 4.0, As5 is anionic (p

 = 6.2) are neutral (acidity constants taken from reference 12). Thus, the anions arsenate (As5) and MMA are eluted first as a result of electrostatic repulsion from OSA adsorbed onto the C18 surface. The neutral species arsenous acid (As3) and DMA are retained stronger and are eluted later. Retention of those species is likely based on weak partitioning. AsB is zwitterionic at this pH (p

 = 2.2) and is eluted last. Its stronger retention may be influenced by electrostatic attraction between its cationic moiety with the negatively charged stationary phase. In summary, a variety of interaction mechanisms, both attractive and repulsive, take part in the separation. Overall, the limited interaction by attractive forces results in desirable short run times while maintaining baseline separation of the species. Understanding the separation mechanism may be aided by the illustration provided in Figure 1.

 Ion interaction chromatography mechanism. Repulsive forces between anions and a negatively charged surface lead to their early elution and separation from neutral molecules, which are weakly retained by partitioning. The diagram includes the four most abundant arsenic species in apple juice and rice.

	The regulatory bodies initially focused on the need for testing apple juice, therefore, we first applied our method to the analysis of apple juice.

	The analysis was carried out on an Altus-10 HPLC system coupled to a NexION 350D ICP-MS system (both from PerkinElmer). The mobile phase for the apple juice analysis consisted of 2 mM octanesulfonate, 2 mM malonic acid, adjusted to pH 4.0 and blended with 1% methanol. The separation was carried out on a C18 column, monitoring As75 in standard mode. The injection volume was 20 µL. Complete operational details are given elsewhere (13).

	Standards were prepared in mobile phase, and the apple juice samples were injected directly without further dilution. The only sample treatment was filtration, if required. Figure 2 shows the peaks are well resolved. Not only is the run time very short, but also DMA is well resolved from As3, whose adequate separation is typically challenging in anion-exchange chromatography. Furthermore, AsB, which is not present in the samples, is eluted well after the analyte peaks and may be used as an internal standard.

 Sample chromatogram of an apple juice sample (20 µL injection) with and without AsB spike. AsB may be used as an internal standard.

	Several store bought apple juice samples were analyzed, and the results (Figure 3) show that both inorganic arsenic forms dominate. Levels of inorganic arsenic are well below the action limit of 10 ppb proposed by the United States Food and Drug Administration (US FDA). The check standards analyzed periodically throughout the run showed 6% maximum deviation over an 8 h period, thus obviating the need for an internal standard.

	Arsenic Speciation in Rice: Method Optimization

	Arsenic speciation in rice is receiving a lot of attention because of legislation being implemented in Europe and China limiting the content of permissible inorganic arsenic. The most stringent limit of 100 ppb inorganic arsenic applies to rice for production of food for infants and young children.

	We investigated arsenic speciation in rice after extraction with 0.28 M nitric acid according to the method published by Huang and colleagues (14). This extraction method preserves species identity and has been thoroughly validated (15). Initial tests with direct injection of the acidic extract impacted the chromatography: The DMA peak broadens and shifts to a longer retention time, while the As5 peak shape deteriorated. There is also a very strong effect on AsB retention time and peak shape. Those effects were less pronounced upon dilution, but not eliminated (Figure 4a).

 Effect of acidity: Analysis of diluted rice extracts without (a) and with (b) neutralization. Neutralization eliminates adverse effects on DMA and AsB. Solutions were spiked with 5 ppb for all As species. 20-µL injections.

	Neutralizing the sample extract restored peak shape and retention times for DMA and also allowed AsB to be used as internal standard (Figure 4b), indicating that the adverse effect on those peaks was caused by the introduced acidity. However, the As5 peak shape remained poor (Figure 4b), suggesting this effect is related to ionic strength. In a subsequent optimization of the mobile phase, we aimed to reduce the difference between sample matrix and mobile phase to improve the As5 peak shape. We obtained both optimum separation and peak shape for all four analyte peaks with the addition of 50 mM ammonium nitrate to the mobile phase. Those experiments were carried out with two-fold diluted, neutralized extract matrix at 10 and 20 µL injection volumes.

	The results achieved with neutralization allow for lower detection limits (because of limited sample dilution) and display good method robustness. The benefits of a neutralization step led to its incorporation in the arsenic speciation protocol used by the FDA (16). However, the addition of a neutralization step also increases the analysis complexity and needs more user awareness than a direct analysis of acidic extracts. We therefore tested the possibility of analyzing extracts simply diluted with deionized water without further neutralization. The results show that the improvement to As5 peak shape brought about by the ammonium nitrate addition to the mobile phase persisted, corroborating the earlier indication that the As5 peak shape had been affected by ionic strength differences between samples and mobile phase. However, at low dilutions (twofold), the DMA peak broadened and even split. The effect was seen in both rice extract and extract matrix and persisted when the DMA solution was prepared fresh from the solid chemical, eliminating the possibility that the peak split would be attributable to aging of solutions. Increasing the dilution ratio with deionized water alleviated the issue, and 10-fold diluted extracts were successfully analyzed at 10- and 20-µL injection volumes (Figure 5).

 Extract matrix, 10-fold diluted with deionized water, and spiked with 1 ppb As species.

	The results of the optimization experiment show that a mobile phase modified by the addition of 50 mM ammonium nitrate provides both good peak separation and peak shape of all four analyte peaks for twofold diluted neutralized extracts and 10-fold diluted extracts without neutralization, allowing flexibility in the sample preparation protocol to accommodate both approaches. If dilution with deionized water is selected as the sole analysis approach, a lower ammonium nitrate concentration in the mobile phase may also be used.

	Using the mobile phase composition of the apple juice analysis with the modification of 50 mM ammonium nitrate addition, we injected 10 µL of 1:10 diluted sample extracts. National Institute of Standards and Technology (NIST) 1568b rice flour reference material was digested in duplicate and analyzed to test the applicability of the method for rice analysis. The results agreed well with certified values with recoveries averaging 99% for MMA and DMA, and 97% for inorganic arsenic, thus validating the method.

	We analyzed two rice samples intended for baby food production and five additional rice-derived products aimed at babies that are 4 or 7 months old. The results are displayed in Figure 6. Inorganic arsenic is given as the sum of As3 and As5. Both species are plotted as adjacent bar segments so inorganic As can be easily read off the graph (Figure 6). The white rice sample (R1) was far below the relevant EU limit of 100 ppb inorganic arsenic, while the brown rice sample (R2) was above the limit. The rice cereal (R3) and porridge samples (R4, R5) range from 67 to 117 ppb, and the two rice cake samples (R6, R7) are 142 and 116 ppb, respectively, both below the total inorganic arsenic limit of 300 ppb set by the EU legislation. Inorganic arsenic was the dominant arsenic form. DMA is the major organic arsenic species, and MMA is a minor constituent of some samples.

 Distribution of arsenic species in rice and rice derived products for baby food.

	The mobile phase worked as a calibration matrix with species remaining stable during the run. Reanalyzing standards prepared in mobile phase in the range 0.05–5 ppb one day after preparation confirmed the species are stable.

	Detection limits expressed as parts per billion in solid were 2.5 µg/kg for As5 and As3 and 2.0 µg/kg for MMA and DMA (expressed on elemental As basis), demonstrating adequate sensitivity of the analysis. Detection limits were calculated based on 3*SD of analysis of seven replicates of a 0.05 ppb As standard. Detection limits may be further lowered by increasing the injection volume from 10 to 20 µL if desired, or by neutralizing samples before injection to utilize lower dilution ratios.

	Spike recoveries (1 ppb spike level) ranged from 94–115% for all samples and all species, with averages for all samples being 102%, 104%, 102%, and 102% for As5, MMA, As3, and DMA, respectively.

	An analysis method for arsenic in apple juice and rice by ion-interaction chromatography has been developed that uses electrostatic repulsion of analyte anions from a negatively charged stationary phase in addition to partitioning of neutral analytes. The limited role of attractive forces results in very short run times, substantially shorter than what is currently used for ion-exchange techniques. We adapted the technique for the analysis of apple juice and rice extracts and demonstrated accurate results and method robustness to cope with variations in sample type. Early elution of analyte peaks leads to tall and narrow peaks, which allow lower levels to be measured and increases quantitation accuracy. This approach provides advantages compared to anion-exchange methodology, which not only has longer run times, but also reverses elution order with As5 eluted as the last peak. With substantially shortened run times, recalibrations are much less time consuming (12 min for four standards). Also check standards and quality control solutions can be analyzed more frequently, improving data quality. Furthermore, higher sample throughput is desirable to meet the increased testing needs generated by the introduction of recent legislation for arsenic speciation in food.

		K.O. Amayo, A. Raab, E.M. Krupp, and J. Feldmann, 

		S. Musil, A.H. Petursdottir, A. Raab, H. Gunnlaugsdottir, E. Krupp, and J. Feldmann, 

		E. Bralatei, S. Lacan, E.M. Krupp, and J. Feldmann, 

		M.M. Nearing, I. Koch, and K.J. Reimer, 

Spectrochim. Acta Part B-Atomic Spectrosc.

Ion-Pair Chromatography and Related Techniques

		S. Afton, K. Kubachka, B. Catron, and J.A. Caruso, 

		J.J. Sloth, E.H. Larsen, and Y. Julshamn, 

		H. Ernstberger and K. Neubauer, “Accurate and Rapid Determination of Arsenic Speciation in Apple Juice,” PerkinElmer application note (2015).

		J.H. Huang, P. Fecher, G. Ilgen, K.N. Hu, and J. Yang, 

		K.M. Kubachka, N.V. Shockey, T.A. Hanley, S.D. Conklin, and D.T. Heitkemper. US Food and Drug Administration, “Elemental Analysis Manual for Food and Related Products,” Section 4.11 (FDA, Rockville, Maryland, 2012).

 is with PerkinElmer in Shelton, Connecticut. Direct correspondence to: 

This speciation method based on ion-interaction chromatography has a run time of 

Ensuring the Safety of the Food Supply: Speeding Up Arsenic Speciation Analysis

Download this application note to learn how to rapidly and accurately determine arsenic in apple juice using HPLC/ICP-MS.