Radiation Protection Dosimetry (2014), Vol. 162, No. 3, pp. 220 – 223 Advance Access publication 8 November 2013
doi:10.1093/rpd/nct279
GAMMA SPECTROMETRY EFFICIENCY CALIBRATION USING MONTE CARLO METHODS TO MEASURE RADIOACTIVITY OF 137CS IN FOOD SAMPLES T. Alrefae1,2,* 1 Deptartment of Physics, Faculty of Science, Kuwait University, Khaldia, Kuwait 2 Center for Research in Environmental Radiation, Faculty of Science, Kuwait University, Khaldia, Kuwait
Received 11 July 2013; revised 11 October 2013; accepted 16 October 2013 A simple method of efficiency calibration for gamma spectrometry was performed. This method, which focused on measuring the radioactivity of 137Cs in food samples, was based on Monte Carlo simulations available in the free-of-charge toolkit GEANT4. Experimentally, the efficiency values of a high-purity germanium detector were calculated for three reference materials representing three different food items. These efficiency values were compared with their counterparts produced by a computer code that simulated experimental conditions. Interestingly, the output of the simulation code was in acceptable agreement with the experimental findings, thus validating the proposed method.
INTRODUCTION Environmental radioactivity is caused by natural and man-made sources. The presence of natural radionuclides is ubiquitous with different levels of activity. Examples of natural radionuclides are 238U and its decay series, 232Th and its decay series and the primordial radionuclide 40K. Thus, the detection of such naturally occurring radioactive materials is expected in environmental samples. Man-made radionuclides, on the other hand, are products of nuclear processes in industrial, medical and military applications. The release of such radionuclides into the environment can be either controlled (regulated discharges) or uncontrolled (accidents). For example, it was estimated that 9` 1016 Bq of the cesium isotope 137Cs were released into the environment from the Chernobyl accident (1). Thus, the detection of man-made radionuclides in environmental samples is an indicator of a previous contaminating event. Whether natural or man-made radionuclides find its way to humans through the food chain via aquatic and terrestrial pathways. Hence, exposure of humans to radioactivity from food consumption is directly related to the amount and type of food consumed. This firm relation raised global awareness with regard to internal exposure from ingestion of food, especially exposure caused by man-made radionuclides(1 – 6). Having a relatively long half-life (30 y), a special concern was given to the presence of 137Cs in food(1 – 6). Measurement of 137Cs in food samples is typically performed with gamma spectrometry. This reliable technique requires obtaining the efficiency of the detection system for the energy of the photon emitted by
the 137Cs radionuclide, namely 662 keV. Such procedure is known as efficiency calibration. Evidently, the efficiency value is largely dependent on the investigated sample density and geometry(3,4). Consequently, a common method that enables efficiency calibration is performed experimentally, where measurements are done with the aid of a standard or reference source that has the same density and geometry of the investigated sample(3,4). Although this method is known to give accurate results, a problem arises when the standard or reference source lacks a radionuclide of interest. Moreover, application of this experimental method is not possible when the standard or reference source is unavailable. Fortunately, a solution for such cases exists, where utilisation of computer simulations is a popular option(7,8,9). Interestingly, computer programs are made to model the detector as well as the sample of interest. This modelling is followed by application of Monte Carlo methods, in conjunction with laws of physics to simulate the interaction of radiation with the material of the detector and the sample(7,8). Indeed, some of these software toolkits are commercially available, while others are free of charge. The commercial toolkit generally comes with the detector at an additional cost, nonetheless, customised to work with the purchased system to provide a user-friendly platform. Examples of such commercial toolkits are LabSOCS (Canberra, USA) and Angle (Ortec, USA). On the other hand, the free-of-charge toolkits do not usually offer user-friendly platforms. In fact, such free software toolkits require a skilled knowledge in computer programming. Yet, this cost comes for a privilege of a full control of the software toolkit. Having such
# The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
Downloaded from http://rpd.oxfordjournals.org/ at University of Pennsylvania Library on August 14, 2015
*Corresponding author: [email protected]
EFFICIENCY CALIBRATION WITH MONTE CARLO
control grants access to the large libraries of functions and classes that may be used for a wide variety of applications. An example of such a free toolkit is GEometry ANd Tracking (GEANT 4)(8). The goal of this work was to perform efficiency calibration for a gamma spectrometry system using GEANT 4 to measure 137Cs in food samples. MATERIALS AND METHODS Computer simulations
x ¼ sin u cos w;
y ¼ sin u cos w;
z ¼ cos u ¼ 1 þ 2d1 ;
w ¼ 2pd2
where d1 and d2 are random numbers with uniform probability from 0 to 1. Moreover, the location of the point source was made to randomly change within the volume of the sample, thus ensuring the homogeneity of the modelled sample. This randomisation was achieved by applying the transformation. y ¼ r sin w; z ¼ hsample d2 þ l; pffiffiffiffiffi r ¼ rsample d4 ; w ¼ 2pd5
x ¼ r cos w;
where hsample and rsample are the modelled sample’s height and radius, respectively, l is the distance from the end of the detector to the beginning of the sample and d2, d4 and d5 are random numbers with uniform probability from 0 to 1. Table 1. Dimensions provided by the manufacturer and their optimised counterparts.
Crystal radius Crystal length Core hole radius Core hole depth Figure 1. The simulated model of the detector and the sample.
Nominal (mm)
Optimised (mm)
36 58 6a 13a
32 46 7 14
a Unavailable by the manufacturer. Values used here were obtained from a similar detector.
221
Downloaded from http://rpd.oxfordjournals.org/ at University of Pennsylvania Library on August 14, 2015
The set-up in Figure 1 was modelled with a Cþþ program that utilised classes and functions offered by the GEANT4 toolkit (8). Mainly, the model consisted of two main components, the detector and the sample. To model a high-purity germanium (HPGe) detector, the first component was constructed as a solid cylinder made of Ge with a hole at the lower end. The upper end of the detector was followed by a gap that came before a thin layer of Al to serve as the detector end cap. It is noteworthy that the dimensions of the modelled detector were taken from the specifications provided by the manufacturer. Where needed, optimisation of the dimensions was performed(9, 7). The optimised dimensions are shown in Table 1. The second main component of the model was the investigated sample. To mimic real samples, the
modelled sample was made to have a cylindrical geometry enclosed inside a plastic container. Three different substances of the modelled sample were selected, namely fish, milk powder and spinach. This selection was made to correspond to the International Atomic Energy Agency (IAEA) reference materials 414 (fish), 152 (milk powder), and 330 (spinach), respectively(10). These reference materials were advisedly chosen to represent aquatic (fish), and terrestrial (milk powder and spinach) pathways of exposure. Furthermore, the elemental percentages of the modelled samples were taken in accordance with a laboratory elemental analysis (Elementar Vario Micro Cube, Germany) as shown in Table 1. It is worth mentioning, however, that the reference materials themselves were not opened or tampered with. Rather, similar food items were obtained from the local market, before undergoing elemental analysis (Table 2). The radioactive source was modelled as a 662-keV gamma emitting point source. Mimicking the isotropic nature of a real point source, the modelled source was made to emit photons at random directions. This randomisation was achieved by applying the transformation:
T. ALREFAE
Experimental measurements Experimental measurements were performed for the purpose of comparison to the simulated output. The measurements were done with an HPGe detector of 80 % relative efficiency (Canberra, USA). This detector, which was modelled, underwent energy calibration Table 2. Elemental analysis for the investigated food items (% by mass).
C (%) H (%) N (%) O (%) Density (g cm23)
Fish
Milk powder
Spinach
51 10 12 27 0.60
50 10 4 36 0.4
30 5 12 36 0.78
using a set of point sources, prior to measuring the three above-mentioned samples. To reduce statistical uncertainties, each sample was counted for at least one full day (86 400 s). Afterwards, the experimental efficiency values were calculated using the relation: 1exp ¼
N APg t
ð1Þ
where N is the net counts of the corresponding full absorption peak, Pg the emission probability per disintegration for 662-keV photons, t the counting time and A is the activity of the sample. In addition to sample counting, a 137Cs point source measurement was conducted for the purpose of comparison with a point source simulation.
RESULTS AND DISCUSSION The efficiency values obtained by simulation (1sim) were compared with their experimental counterparts (1exp) in Table 3. The comparison revealed biases of 8, 25 and 16 % for the samples of fish, milk powder and spinach, respectively. Being within the generally accepted region of +20%, all 1sim values are given a pass status(11). Since all three 1sim values reported herein are acceptable, the approach used in this study is validated. This validation is further reinforced by observing the 1sim/1exp value for the 137Cs point source case of 1.08, thus exhibiting a bias of less than +10 %. It is assumed, however, that the presence of biases in this study was mainly due to inaccuracies in the detector dimensions used as input for the simulations. These dimensions were initially taken from the specifications provided by the manufacturer. Nonetheless, it is well known that these specifications are not always sufficiently accurate(9, 7), thus making it necessary to optimise the modelled detector’s dimensions prior to simulation(9, 7). The acceptance of the results denotes the success of the presented work. Being successful with the three investigated food items denotes the adequacy of the presented approach in other food items as well. Moreover, this approach can be further expanded to measure not only 137Cs, but even more man-made radionuclides. This feature, which is left for future work, is simply done by including relevant gamma
Table 3. Simulation output compared with experimental values.
1sim 1exp 1sim/1exp
Fish
Milk powder
Spinach
0.026+0.00057 0.024+0.00056 1.08
0.018+0.00070 0.019+0.00003 0.95
0.036+0.00046 0.031+0.00036 1.16
222
Downloaded from http://rpd.oxfordjournals.org/ at University of Pennsylvania Library on August 14, 2015
Passage of photons through the material of the modelled sample and detector was tracked on a pointby-point basis. Besides the simple, non-interacting transport, the modes of photon–material interaction were set to Compton scattering and photoelectric absorption. Thus, photons that interacted with the sample material, plastic container and the end cap of the detector resulted in attenuation in the flux of photons. On the other hand, photons that reached the detector were either (i) photoelectrically absorbed, thus resulting in full-energy depositions to the detector, (ii) Compton scattered, thus resulting in partial energy depositions or (iii) left to escape the detector without interaction, thus depositing no energy to the detector. Accordingly, output data were arranged in histograms that formed energy spectra. From these data, the efficiency was calculated as the ratio of the number of photoelectrically absorbed photons to the total number of emitted photons in each run. Ensuring acceptable statistics was an important task to meet. Therefore, a relatively large number of photons (105) were emitted by the modelled point source for each run. Furthermore, multiple independent runs were conducted for each of the modelled samples with different random number seeds for each run. Hence, the simulated efficiency values were taken as the average of the multiple runs, with the standard deviation as the standard uncertainty.
EFFICIENCY CALIBRATION WITH MONTE CARLO
ACKNOWLEDGEMENTS The author thanks Mr Tiruvachi Natrajan Nageswaran for his help in gamma spectrometry measurements. Thanks are extended to Ms Lima Mathew for her help in elemental analysis. FUNDING This work was partially funded by the General Facility Project (GS 01/05), Kuwait University. REFERENCES 1. IAEA. Environmental consequences of the Chernobyl accident and their remediation: twenty years of experience. Radiological Assessment Reports Series (2006). 2. Ababneh, Z. Q., Alyassin, A. M., Aljarrah, K. M. and Ababneh, A. M. Measurement of natural and artificial radioactivity in powdered milk consumed in Jordan and estimates of the corresponding annual effective dose. Radiat. Prot. Dosim. 138, 278–283 (2009). 3. Al-Azmi, D., Saad, H. R. and Farhan, A. R. Comparative study of desert truffles from Kuwait and other countries in the Middle East for radionuclide concentration. Biol. Trace Elem. Res. 7, 71–72 (1999). 4. IAEA. Measurements of radionuclides in food and the environment. Technical Report Series 295 (1989). 5. IAEA. Assessment of doses to the public from ingested radionulices. Safety Report Series No. 14 (1999). 6. Franic, Z., Marovic, G. and Mestrovic, J. Radiocaesium contamination of beef in Croatia after the Chernobyl accident. Food Chem. Toxicol. 46, 2096– 2102 (2008). 7. Hurtado, S., Garcia-Leon, M. and Garcia-Tenorio, R. GEANT4 code for simulation of a germanium gamma-ray detector and its application to efficiency calibration. Nucl. Instrum. Methods Phys. Res. A 518, 764–774 (2004). 8. Agostinelli, S. et al. GEANT4-a simulation toolkit. Nucl. Instrum. Methods Phys. Res. A 506, 250– 303 (2003).
9. Palacios, D. F., Alfonso, J. A., Barros, H., LaBrecque, J. J., Perez, K. and Lossada, M. R. A semi-empirical approach to determine gamma activities (Bq kg(-1)) in environmental cylindrical samples. Radiat. Meas. 43, 77– 84 (2008). 10. IAEA. Reference materials [cited 13 September 2013]. Available on http://nucleus.iaea.org/rpst/Reference Products/ReferenceMaterials/Radionuclides/index.htm. 11. DOE-MAPEP. Mixed Analyte Performance Evaluation Program (MAPEP) (2012) [cited 28 June 2013]. Available on http://www.id.energy.gov/resl/mapep/ handbookv13.pdf. 12. Al-Masri, M. S., Mukallati, H., Al-Hamwi, A., Khalili, H., Hassan, M., Assaf, H., Amin, Y. and Nashawati, A. Natural radionuclides in Syrian diet and their daily intake. J. Radioanal. Nucl. Chem. 260, 405– 412 (2004). 13. ICRP. Age-dependent doses to members of the public from intake of radionuclides. ICRP Publication 72 (1996). 14. Al-Masri, M. S., Nashawati, A., Amin, Y. and Al-Akel, B. Transfer of Ra-226, Sr-85 and Cs-137 from milk to milk products. J. Radioanal. Nucl. Chem. 268, 289– 295 (2006). 15. Alrefae, T., Nageswaraan, T., Al-Failakawi, A. and AlShemali, T. Radioactivity of long lived gamma emitters in milk powder consumed in Kuwait and estimates of annual effective doses. Kuwait J. Sci. Eng. 39, 1– 19 (2012). 16. Altzitzoglou, T. and Bohnstedt, A. Characterisation of the IAEA-152 milk powder reference material for radioactivity with assigned values traceable to the SI units. Appl. Radiat. Isot 66, 1702–1705 (2008). 17. Hermanspahn,, N. Environmental radioactivity in New Zealand and Rarotonga—annual report 2008 (2009). 18. Hosseini, T., Fathivand, A., Barati, H. and Karimi, M. Assessment of radionuclides in imported foodstuffs in Iran. Iran. J. Radiat. Res. 4 (2006). 19. Kamenik, J., Skrkal, J. and Rulik, P. Long term monitoring of Cs-137 in foodstuffs in the Czech Republic. Appl. Radiat. Isot. 67, 974– 977 (2009). 20. Shiraishi, K., Tagami, K., Bannai, T., Yamamoto, M., Muramatsu, Y., Los, I. P., Phedosenko, G. V., Korzun, V. N., Tsigankov, N. Y. and Segeda, I. I. Daily intakes of Cs-134, Cs-137, K-40, Th-232, and U-238 in Ukrainian adult males. Health Phys. 73, 814–819 (1997). 21. Baccouche, S., Al-Azmi, D., Karunakara, N. and Trabelsi, A. Application of the Monte Carlo method for the efficiency calibration of CsI and NaI detectors for gamma-ray measurements from terrestrial samples. Appl. Radiat. Isot. 70, 227– 232 (2012). 22. Ebaid, Y. Y. On the use of reference materials in gammaray spectrometric efficiency calibration for environmental samples. J. Radioanal. Nucl. Chem. 280, 21–25 (2009). 23. Khater, A. E. M. and Ebaid, Y. Y. A simplified gammaray self-attenuation correction in bulk samples. Appl. Radiat. Isot. 66, 407– 413 (2008). 24. Ozben, C. S. and Emirhan, E. M. A hybrid method to determine efficiency curve of HPGe detectors. Appl. Radiat. Isot. 67, 1110–1113 (2009).
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energies in the simulation, such as 834 keV for 54Mn, keeping in mind the discrepancies that may arise with low gamma energies. This work introduced a simple method to perform efficiency calibration for gamma spectrometry for measurement of 137Cs in food samples. In addition to programming skills, this method required knowledge of the properties of the investigated samples, namely main elemental composition and densities. In return, this method provided acceptable results without the need of any standard or reference sources, or even expensive, commercial software. Hence, this method can serve as a simple, straightforward procedure to measure man-made radionuclides in food (12 – 24).
doi:10.1093/rpd/nct279
GAMMA SPECTROMETRY EFFICIENCY CALIBRATION USING MONTE CARLO METHODS TO MEASURE RADIOACTIVITY OF 137CS IN FOOD SAMPLES T. Alrefae1,2,* 1 Deptartment of Physics, Faculty of Science, Kuwait University, Khaldia, Kuwait 2 Center for Research in Environmental Radiation, Faculty of Science, Kuwait University, Khaldia, Kuwait
Received 11 July 2013; revised 11 October 2013; accepted 16 October 2013 A simple method of efficiency calibration for gamma spectrometry was performed. This method, which focused on measuring the radioactivity of 137Cs in food samples, was based on Monte Carlo simulations available in the free-of-charge toolkit GEANT4. Experimentally, the efficiency values of a high-purity germanium detector were calculated for three reference materials representing three different food items. These efficiency values were compared with their counterparts produced by a computer code that simulated experimental conditions. Interestingly, the output of the simulation code was in acceptable agreement with the experimental findings, thus validating the proposed method.
INTRODUCTION Environmental radioactivity is caused by natural and man-made sources. The presence of natural radionuclides is ubiquitous with different levels of activity. Examples of natural radionuclides are 238U and its decay series, 232Th and its decay series and the primordial radionuclide 40K. Thus, the detection of such naturally occurring radioactive materials is expected in environmental samples. Man-made radionuclides, on the other hand, are products of nuclear processes in industrial, medical and military applications. The release of such radionuclides into the environment can be either controlled (regulated discharges) or uncontrolled (accidents). For example, it was estimated that 9` 1016 Bq of the cesium isotope 137Cs were released into the environment from the Chernobyl accident (1). Thus, the detection of man-made radionuclides in environmental samples is an indicator of a previous contaminating event. Whether natural or man-made radionuclides find its way to humans through the food chain via aquatic and terrestrial pathways. Hence, exposure of humans to radioactivity from food consumption is directly related to the amount and type of food consumed. This firm relation raised global awareness with regard to internal exposure from ingestion of food, especially exposure caused by man-made radionuclides(1 – 6). Having a relatively long half-life (30 y), a special concern was given to the presence of 137Cs in food(1 – 6). Measurement of 137Cs in food samples is typically performed with gamma spectrometry. This reliable technique requires obtaining the efficiency of the detection system for the energy of the photon emitted by
the 137Cs radionuclide, namely 662 keV. Such procedure is known as efficiency calibration. Evidently, the efficiency value is largely dependent on the investigated sample density and geometry(3,4). Consequently, a common method that enables efficiency calibration is performed experimentally, where measurements are done with the aid of a standard or reference source that has the same density and geometry of the investigated sample(3,4). Although this method is known to give accurate results, a problem arises when the standard or reference source lacks a radionuclide of interest. Moreover, application of this experimental method is not possible when the standard or reference source is unavailable. Fortunately, a solution for such cases exists, where utilisation of computer simulations is a popular option(7,8,9). Interestingly, computer programs are made to model the detector as well as the sample of interest. This modelling is followed by application of Monte Carlo methods, in conjunction with laws of physics to simulate the interaction of radiation with the material of the detector and the sample(7,8). Indeed, some of these software toolkits are commercially available, while others are free of charge. The commercial toolkit generally comes with the detector at an additional cost, nonetheless, customised to work with the purchased system to provide a user-friendly platform. Examples of such commercial toolkits are LabSOCS (Canberra, USA) and Angle (Ortec, USA). On the other hand, the free-of-charge toolkits do not usually offer user-friendly platforms. In fact, such free software toolkits require a skilled knowledge in computer programming. Yet, this cost comes for a privilege of a full control of the software toolkit. Having such
# The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
Downloaded from http://rpd.oxfordjournals.org/ at University of Pennsylvania Library on August 14, 2015
*Corresponding author: [email protected]
EFFICIENCY CALIBRATION WITH MONTE CARLO
control grants access to the large libraries of functions and classes that may be used for a wide variety of applications. An example of such a free toolkit is GEometry ANd Tracking (GEANT 4)(8). The goal of this work was to perform efficiency calibration for a gamma spectrometry system using GEANT 4 to measure 137Cs in food samples. MATERIALS AND METHODS Computer simulations
x ¼ sin u cos w;
y ¼ sin u cos w;
z ¼ cos u ¼ 1 þ 2d1 ;
w ¼ 2pd2
where d1 and d2 are random numbers with uniform probability from 0 to 1. Moreover, the location of the point source was made to randomly change within the volume of the sample, thus ensuring the homogeneity of the modelled sample. This randomisation was achieved by applying the transformation. y ¼ r sin w; z ¼ hsample d2 þ l; pffiffiffiffiffi r ¼ rsample d4 ; w ¼ 2pd5
x ¼ r cos w;
where hsample and rsample are the modelled sample’s height and radius, respectively, l is the distance from the end of the detector to the beginning of the sample and d2, d4 and d5 are random numbers with uniform probability from 0 to 1. Table 1. Dimensions provided by the manufacturer and their optimised counterparts.
Crystal radius Crystal length Core hole radius Core hole depth Figure 1. The simulated model of the detector and the sample.
Nominal (mm)
Optimised (mm)
36 58 6a 13a
32 46 7 14
a Unavailable by the manufacturer. Values used here were obtained from a similar detector.
221
Downloaded from http://rpd.oxfordjournals.org/ at University of Pennsylvania Library on August 14, 2015
The set-up in Figure 1 was modelled with a Cþþ program that utilised classes and functions offered by the GEANT4 toolkit (8). Mainly, the model consisted of two main components, the detector and the sample. To model a high-purity germanium (HPGe) detector, the first component was constructed as a solid cylinder made of Ge with a hole at the lower end. The upper end of the detector was followed by a gap that came before a thin layer of Al to serve as the detector end cap. It is noteworthy that the dimensions of the modelled detector were taken from the specifications provided by the manufacturer. Where needed, optimisation of the dimensions was performed(9, 7). The optimised dimensions are shown in Table 1. The second main component of the model was the investigated sample. To mimic real samples, the
modelled sample was made to have a cylindrical geometry enclosed inside a plastic container. Three different substances of the modelled sample were selected, namely fish, milk powder and spinach. This selection was made to correspond to the International Atomic Energy Agency (IAEA) reference materials 414 (fish), 152 (milk powder), and 330 (spinach), respectively(10). These reference materials were advisedly chosen to represent aquatic (fish), and terrestrial (milk powder and spinach) pathways of exposure. Furthermore, the elemental percentages of the modelled samples were taken in accordance with a laboratory elemental analysis (Elementar Vario Micro Cube, Germany) as shown in Table 1. It is worth mentioning, however, that the reference materials themselves were not opened or tampered with. Rather, similar food items were obtained from the local market, before undergoing elemental analysis (Table 2). The radioactive source was modelled as a 662-keV gamma emitting point source. Mimicking the isotropic nature of a real point source, the modelled source was made to emit photons at random directions. This randomisation was achieved by applying the transformation:
T. ALREFAE
Experimental measurements Experimental measurements were performed for the purpose of comparison to the simulated output. The measurements were done with an HPGe detector of 80 % relative efficiency (Canberra, USA). This detector, which was modelled, underwent energy calibration Table 2. Elemental analysis for the investigated food items (% by mass).
C (%) H (%) N (%) O (%) Density (g cm23)
Fish
Milk powder
Spinach
51 10 12 27 0.60
50 10 4 36 0.4
30 5 12 36 0.78
using a set of point sources, prior to measuring the three above-mentioned samples. To reduce statistical uncertainties, each sample was counted for at least one full day (86 400 s). Afterwards, the experimental efficiency values were calculated using the relation: 1exp ¼
N APg t
ð1Þ
where N is the net counts of the corresponding full absorption peak, Pg the emission probability per disintegration for 662-keV photons, t the counting time and A is the activity of the sample. In addition to sample counting, a 137Cs point source measurement was conducted for the purpose of comparison with a point source simulation.
RESULTS AND DISCUSSION The efficiency values obtained by simulation (1sim) were compared with their experimental counterparts (1exp) in Table 3. The comparison revealed biases of 8, 25 and 16 % for the samples of fish, milk powder and spinach, respectively. Being within the generally accepted region of +20%, all 1sim values are given a pass status(11). Since all three 1sim values reported herein are acceptable, the approach used in this study is validated. This validation is further reinforced by observing the 1sim/1exp value for the 137Cs point source case of 1.08, thus exhibiting a bias of less than +10 %. It is assumed, however, that the presence of biases in this study was mainly due to inaccuracies in the detector dimensions used as input for the simulations. These dimensions were initially taken from the specifications provided by the manufacturer. Nonetheless, it is well known that these specifications are not always sufficiently accurate(9, 7), thus making it necessary to optimise the modelled detector’s dimensions prior to simulation(9, 7). The acceptance of the results denotes the success of the presented work. Being successful with the three investigated food items denotes the adequacy of the presented approach in other food items as well. Moreover, this approach can be further expanded to measure not only 137Cs, but even more man-made radionuclides. This feature, which is left for future work, is simply done by including relevant gamma
Table 3. Simulation output compared with experimental values.
1sim 1exp 1sim/1exp
Fish
Milk powder
Spinach
0.026+0.00057 0.024+0.00056 1.08
0.018+0.00070 0.019+0.00003 0.95
0.036+0.00046 0.031+0.00036 1.16
222
Downloaded from http://rpd.oxfordjournals.org/ at University of Pennsylvania Library on August 14, 2015
Passage of photons through the material of the modelled sample and detector was tracked on a pointby-point basis. Besides the simple, non-interacting transport, the modes of photon–material interaction were set to Compton scattering and photoelectric absorption. Thus, photons that interacted with the sample material, plastic container and the end cap of the detector resulted in attenuation in the flux of photons. On the other hand, photons that reached the detector were either (i) photoelectrically absorbed, thus resulting in full-energy depositions to the detector, (ii) Compton scattered, thus resulting in partial energy depositions or (iii) left to escape the detector without interaction, thus depositing no energy to the detector. Accordingly, output data were arranged in histograms that formed energy spectra. From these data, the efficiency was calculated as the ratio of the number of photoelectrically absorbed photons to the total number of emitted photons in each run. Ensuring acceptable statistics was an important task to meet. Therefore, a relatively large number of photons (105) were emitted by the modelled point source for each run. Furthermore, multiple independent runs were conducted for each of the modelled samples with different random number seeds for each run. Hence, the simulated efficiency values were taken as the average of the multiple runs, with the standard deviation as the standard uncertainty.
EFFICIENCY CALIBRATION WITH MONTE CARLO
ACKNOWLEDGEMENTS The author thanks Mr Tiruvachi Natrajan Nageswaran for his help in gamma spectrometry measurements. Thanks are extended to Ms Lima Mathew for her help in elemental analysis. FUNDING This work was partially funded by the General Facility Project (GS 01/05), Kuwait University. REFERENCES 1. IAEA. Environmental consequences of the Chernobyl accident and their remediation: twenty years of experience. Radiological Assessment Reports Series (2006). 2. Ababneh, Z. Q., Alyassin, A. M., Aljarrah, K. M. and Ababneh, A. M. Measurement of natural and artificial radioactivity in powdered milk consumed in Jordan and estimates of the corresponding annual effective dose. Radiat. Prot. Dosim. 138, 278–283 (2009). 3. Al-Azmi, D., Saad, H. R. and Farhan, A. R. Comparative study of desert truffles from Kuwait and other countries in the Middle East for radionuclide concentration. Biol. Trace Elem. Res. 7, 71–72 (1999). 4. IAEA. Measurements of radionuclides in food and the environment. Technical Report Series 295 (1989). 5. IAEA. Assessment of doses to the public from ingested radionulices. Safety Report Series No. 14 (1999). 6. Franic, Z., Marovic, G. and Mestrovic, J. Radiocaesium contamination of beef in Croatia after the Chernobyl accident. Food Chem. Toxicol. 46, 2096– 2102 (2008). 7. Hurtado, S., Garcia-Leon, M. and Garcia-Tenorio, R. GEANT4 code for simulation of a germanium gamma-ray detector and its application to efficiency calibration. Nucl. Instrum. Methods Phys. Res. A 518, 764–774 (2004). 8. Agostinelli, S. et al. GEANT4-a simulation toolkit. Nucl. Instrum. Methods Phys. Res. A 506, 250– 303 (2003).
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energies in the simulation, such as 834 keV for 54Mn, keeping in mind the discrepancies that may arise with low gamma energies. This work introduced a simple method to perform efficiency calibration for gamma spectrometry for measurement of 137Cs in food samples. In addition to programming skills, this method required knowledge of the properties of the investigated samples, namely main elemental composition and densities. In return, this method provided acceptable results without the need of any standard or reference sources, or even expensive, commercial software. Hence, this method can serve as a simple, straightforward procedure to measure man-made radionuclides in food (12 – 24).
