UNIVERSITY OF TARTU FACULTY OF SCIENCE AND TECHNOLOGY

INSTITUTE OF CHEMISTRY

Liina Kruus

Determination of Calcium, Potassium, Phosphorus and

Magnesium in Forages by Energy Dispersive X-ray Fluorescence

Spectrometry

Master's thesis

Supervisors: Ivo Leito, PhD

Märt Nõges, PhD

TARTU 2010

2 

 

Table of Contents  

Table of Contents .................................................................................................................................... 2

1 Introduction..................................................................................................................................... 4

2 Literature overview ......................................................................................................................... 5

2.1 Macro elements in animal nutrition ...................................................................................... 5

2.1.1 Potassium in animal nutrition [1][2].................................................................................. 5

2.1.2 Phosphorus in animal nutrition [2][3] ............................................................................... 6

2.1.3 Calcium in animal nutrition [2] .......................................................................................... 6

2.1.4 Magnesium in animal nutrition [2].................................................................................... 8

2.1.5 Typical content ranges of nutritional elements in forages................................................ 8

2.2 Inductively coupled plasma optical emission spectroscopy (ICP‐OES) [4][5] ........................ 8

2.2.1 Sample preparation ........................................................................................................... 9

2.2.2 Microwave digestion system [18][19] ............................................................................. 10

2.3 X‐ray fluorescence spectrometry [20][21][22] .................................................................... 11

2.3.1 Sample preparation [22].................................................................................................. 13

2.3.2 Matrix effects [26] ........................................................................................................... 14

2.4 Validation [27]...................................................................................................................... 15

2.4.1 Traceability ...................................................................................................................... 15

2.4.2 Precision and trueness..................................................................................................... 16

2.4.3 Limit of detection and limit of quantitation .................................................................... 17

2.4.4 Measurement uncertainty............................................................................................... 17

3 Experimental ................................................................................................................................. 20

3.1 ICP‐OES method................................................................................................................... 20

3.2 Sample preparation for the ICP‐OES method ...................................................................... 21

3.3 EDXRF Method ..................................................................................................................... 21

3.4 Sample preparation for EDXRF analysis ............................................................................... 22

3 

 

3.4.1 Sampling and homogenization of forage material .......................................................... 22

3.4.2 Certified reference materials........................................................................................... 23

3.4.3 EDXRF calibrations ........................................................................................................... 23

4 Results and discussion................................................................................................................... 25

4.1 Evaluation of the EDXRF method......................................................................................... 25

4.2 EDXRF validation .................................................................................................................. 26

5 Summary ....................................................................................................................................... 32

6 References..................................................................................................................................... 33

7 Kokkuvõte...................................................................................................................................... 36

4 

 

1 Introduction

Commonly, determination of elements in plant material has been carried out by means of

atomic spectrometry techniques, including flame atomic absorption spectrometry (FAAS),

inductively coupled plasma atomic emission spectrometry (ICP-OES) and inductively

coupled plasma mass spectrometry (ICP-MS). Each of these techniques has advantages and

drawbacks. The use of these techniques usually involves sample-preparation procedures for

total destruction of the matrix by chemical treatment. Sample dissolution is usually a tedious,

time-consuming and expensive step that requires skillful personnel.

For this reason, study of the suitability of other methods for direct and multi-elemental

analysis of vegetal samples has been increased in recent years.

These previous studies have successfully demonstrated that the x-ray fluorescence

spectroscopy (XRF) is an efficient tool for measuring minerals in plant material.

The aim of this work was to develop an energy dispersive X-ray fluorescence (EDXRF)

method for the analysis of four nutrient elements – K, Ca, Mg and P – in forage samples, for

the ever-increasing workload at Agricultural Research Center, as an alternative to

conventional destructive methods.

The analytical results of the proposed EDXRF method were compared with those obtained

from the application of routine microwave assisted digestion procedure followed by

inductively coupled plasma optical emission spectrometry (ICP-OES) determination, that is

usually applied to analysis of plant material including forage samples. Also validation of the

EDXRF method was done via analysis of certified reference material and a set of validation

samples of forages.

5 

 

2 Literature overview  

2.1 Macro elements in animal nutrition 

2.1.1 Potassium in animal nutrition [1][2] Potassium (K) has been recognized as an important essential nutrient in animal nutrition since

its importance was pointed out by Sidney Ringer in 1883. Potassium is essential for life.

Young animals fail to grow and will die within a few days if their diet is extremely deficient

in K.

Potassium is the third most abundant mineral element in the animal body surpassed only by

calcium (Ca) and phosphorus (P). Potassium comprises about 5 percent of the total mineral

content of the body. Potassium is contained almost entirely within the cells and is the most

plentiful ion of the intercellular fluids. Potassium is found in every cell and it is present in

every cell and tissue only in ionic form (K+).

Functions of potassium in animal body:

a) Maintaining water balance

b) Maintaining osmotic pressure

c) Maintaining acid-base balance

d) Activating enzymes

e) Helping metabolize carbohydrates and proteins

f) Regulating neuromuscular activity (along with Ca)

g) Helping to regulate the heartbeat rate.

Potassium deficiency causes depressed growth, decreased feed intake, stiffness, muscular

weakness and nervous disorders. The first sign of potassium deficiency is decreased feed

intake. Potassium must be supplied in the daily ration because it is a mobile nutrient and there

are not any appreciable reserves in the body.

Ruminants have a higher K requirement than nonruminants. Potassium is essential for rumen

microorganisms. The single most consistent effect of suboptimal K in ration of ruminants is

6 

 

decreased feed intake. Lactating dairy cattle require the highest levels of dietary K. According

to Estonian norms the recommended daily potassium intake for ruminants is 1.0 percent of K

of dietary dry matter. The maximum amount of K desirable in the dry cow diet depends on the

use of anionic salts and other factors, but generally forage K should be less than 2.5 percent.

2.1.2 Phosphorus in animal nutrition [2][3] In the animal body, about 80 percent of P is found in the skeleton. Its major role is as a

constituent of bones and teeth. Phosphorus is widely distributed throughout the body in

combination with proteins and fats and as inorganic salts.

Phosphorus constitutes about 22 percent of the mineral ash in an animal’s body, a little less

than one percent of total body weight. It is essential in transfer and utilization of energy.

Phosphorus is also present in every living cell in the nucleic acid fraction. Ca and P are

closely associated with each other in animal metabolism. Adequate Ca and P nutrition

depends on three factors: a sufficient supply of each nutrient, a suitable ratio between them,

and the presence of vitamin D. These factors are interrelated. The desirable Ca:P ratio is often

between 2:1 and 1:1.

Earliest symptoms of P deficiency are decreased appetite, lowered blood P, reduced rate of

gain, and “pica”, in which the animals have a craving for unusual foods such as wood or other

materials. If severe deficiency occurs, there will be skeletal problems. Milk production

decreases with P deficiency, and efficiency of feed utilization is depressed. Long-term P

deficiency results in bone changes, lameness, and stiff joints.

Supplemental dietary P is needed under most practical feeding situations. Deficiency of P is

the most widespread and economically important of all the mineral deficiencies affecting

grazing livestock. On grazed pasture, where soils are low in P, fertilizing with P can reduce

risk of grass tetany.

2.1.3 Calcium in animal nutrition [2] The availability of calcium in forage during growing process is influenced by weather

conditions. In dry and drought season the ability of plants to bind Ca is greater than in rainy

seasons. The calcium availability is also influenced by soil type. There is less calcium in

plants growing on sandy and peat soils.

7 

 

Calcium is the most widespread element in animal body and also its functions in animal body

are very diverse:

a) Development on teeth and bone structure

b) Normal function of muscular system

c) Maintenance of osmotic pressure in blood

d) Metabolism of vitamin D

e) Regulation of neuromuscular activity

f) Activation enzymes

g) Coagulation of blood

h) Regulation of membranes

i) Regulation of hormonal effectiveness

j) Formation of products (milk)

Although most of the calcium found in animal body is stored in the skeleton the main function

of calcium is not the formation of the skeleton but in the physiologic reactions taking place in

soft tissues.

Excessive concentrations of calcium in animal feed are not dangerous to the animals but

nevertheless not recommended. The sensitivity to the excessive calcium is very different.

Excessive calcium in ruminants nutrition can cause reduced function of digestion of feed and

reduce the intake of other mineral nutrients (P, Mg, Zn, Cu, Fe, Mn). For nonruminants the

excessive calcium content in feed intake can cause difficulties in digestion of fat and decrease

the feed intake.

Moderate calcium deficiency is called hypocalcaemia. Deficiency in calcium can cause

osteoporosis and rachitic. Since bones consist of calcium and phosphorus salts it is often

difficult to make sure which deficiency causes these disease because the symptoms are the

same: decreased appetite, reduced rate of gain, and “pica”, in which the animals have a

craving for unusual foods such as wood or other materials. If severe deficiency occurs, there

will be skeletal problems and decrease of milk production.

Calving fever is also caused by the drastic drop of calcium concentration in body fluids.

According to Estonian feeding regulations the average calcium content in feed should be 0.4-

0.7 percent of dietary dry matter.

8 

 

2.1.4 Magnesium in animal nutrition [2] In plants, magnesium is part of chlorophyll so the deficiency of Mg is clearly seen from the

pale green colour of the leaves. The magnesium content in plants is dependent of soil type,

soil pH and fertilization.

In animal body magnesium is contained mostly in the intercellular fluid. Mg concentration in

the intercellular fluid is 10-15 times higher than in extracellular. The main functions of

magnesium in animal body:

a) Lipid synthesis and metabolism

b) Regulation of heartbeat

c) Function of muscular and nervous system

d) Thermoregulation

e) Activation of enzymes

Excessive concentrations of magnesium in animal feed can cause decreased uptake of calcium

and therefore problems with bone structure.

Magnesium deficiency in an early stage causes accelerated heartbeat, when deficiency

penetrates the animal become irritated and restless. Also it may cause muscular trembling and

cramping.

2.1.5 Typical content ranges of nutritional elements in forages The typical content ranges of nutritional elements in Estonian forage material are presented in

Table 1.

Table 1. Typical element contents (%) in forages [2]

Ca K P Mg

Element contents in forage (%) 0.4-1.90 1.4-2.7 0.20-0.45 0.10-0.40

2.2 Inductively coupled plasma optical emission spectroscopy (ICP‐OES) [4][5] 

A sample solution (solid samples are first dissolved and mixed with water) is pumped at 1

mL/min (usually with a peristaltic pump) into a nebulizer, where it is converted into a fine

aerosol with argon gas at about 1 L/min. The fine droplets of the aerosol, which represent

9 

 

only 1 - 2% of the sample, are separated from larger droplets using a spray chamber. The fine

aerosol then emerges from the exit tube of the spray chamber and is transported into the

plasma torch via a sample injector.

The plasma is produced by the interaction of an intense magnetic field (produced by radio

frequency passing through a copper coil) on a tangential flow of gas (normally argon), at

about 15 L/min flowing through a concentric quartz tube (torch). This ionizes the gas and,

when seeded with a source of electrons from a high-voltage spark, forms a very high

temperature plasma discharge (~10,000 K) at the open end of the tube. The plasma, usually

oriented vertically, excites ground-state atoms to higher energy levels. Upon relaxation the

atoms emit photons at wavelengths that are element-specific. An atomic emission spectrum is

obtained.

A spectrometer is used to separate the specific emission lines of interest. Because atomic

emission lines are very narrow, a high-resolution monochromator or multi-channel detector is

essential. Simultaneous detection makes it possible to measure all elements of interest at the

same time. Most often a CCD (charge-coupled device) detector is used, which provides high

resolution and simultaneous detection possibility.

2.2.1 Sample preparation Commonly, dry ashing (via combustion of the sample) or wet ashing (via digestion with

strong acids) have been used to destroy the organic matrix and to convert the sample into a

homogenous solution [6]. Compared to dry-ashing methods, wet-ashing procedures using acid

digestion present a wide range of options, depending on the choice of reagents and their

mixtures as well as the devices used for the procedure.

At first sight, a plant matrix seems to be very similar to other biological matrices in the

environment, which are well known to be relatively easy to decompose using mixtures of

strong acids and oxidants. However, there are large differences between plant material matrix

and organic matrices of animal origin, e.g. the higher content of silicon in most of vegetal

samples (by up to10% of mineral content). This factor is of prime importance when an

efficient mineralization procedure has to be considered, since, in the case of high silicon

content, use of hydrofluoric acid is essential to achieve total destruction of the matrix and,

thus, determination of the total concentration of analytes. The large number of acid mixtures

in wet-digestion procedures that may be found in the literature [8–16] shows that there is no

10 

 

consensus on the best way to dissolve plant samples. Classically, open systems (digestions at

atmospheric pressure) have been applied using conventional sources of heating (e.g., sand

baths and heating plates). However, some works have revealed the problem of loss of volatile

compounds for some plant materials using these kinds of devices [16]. Besides, in most cases,

long periods of time are needed to ensure complete sample digestion (up to 10 h) [11]. These

problems can be minimized if wet ashing is carried out in closed systems (i.e. pressurized acid

digestion).

In the past 15 years, microwave ovens have been employed extensively in closed systems to

shorten the time required for sample dissolution and to reduce the amount of reagents

employed as well as to avoid analyte losses and contamination from other samples or from the

surroundings [17]. Also, the International Standardization Organization has accepted the use

of microwaveassisted digestion as a standard method (ISO 27085:2009 Animal feeding stuffs

– Determination of calcium, sodium, phosphorus, magnesium, potassium, iron, zinc, copper,

manganese, cobalt, molybdenum, arsenic, lead and cadmium by ICP-OES [29]) for the

digestion of organically based matrices (e.g., plant matrices).

2.2.2 Microwave digestion system [18][19] Microwave digestion system consists of a microwave oven, rotating carousel holding several

sample digestion bombs and system for venting them in a controlled fashion. A built in

microcontroller controls the temperature and pressure in the containers. The sample

containers are high-pressure containers, usually made of strong high-pressure-resistant

polymers often polycarbonate or PTFT for chemical resistance.

Each bomb has a pressure relief valve which vents into the manifold. This exhausts the acid

fumes into the tube. The relief valves are set so that the sample is heated under the pressure,

allowing higher temperatures and more rapid digestion than is possible in open container.

Microwave digestion systems monitor both temperature and pressure in the containers. As the

temperature or pressure reaches the set point, the power to the oven is cut. The oven power as

well as the maximum pressure and temperatures can be set. Both the digestion time and the

oven power can be set so that each sample is treated in a reproducible manner.

The sample and the decomposition reagents are weighed into the reaction vessel made of

quartz or fluoropolymers. The seal is placed on the reaction vessel and closed with the screw

11 

 

cap. The pressure vessels are inserted into the rotor and closed rotor is placed into the

Microwave oven and the door is closed. The sample is heated by microwave radiation.

2.3 X‐ray fluorescence spectrometry [20][21][22] 

Norris and Hutton (1977) first explored and described the use of XRF spectrometry in plant

analysis for determination of low atomic number elements such as Ca and for high atomic

number elements such as iron (Fe), copper (Cu), and zinc (Zn). Mineral element analysis of

plants by XRF have also been used by numerous authors (Clark et al., 1981; Knudsen et al.,

1981). It has been found that XRF is an effective technique for simultaneous analysis of plant

nutrient elements with the exception of nitrogen (N) and boron (B). For diagnosis purposes,

Knudsen et al. (1981) found that XRF provides sufficient sensitivity for phosphorus (P),

sulfur (S), Ca, manganese (Mn), Fe, Zn, and in most situations for magnesium (Mg) and Cu.

A great advantage of the X-ray fluorescence technique compared to wet chemical analysis is

that the measurement can be carried out directly on solid samples. This avoids sample

digestion dissolution, using toxic and corrosive acids. Less preparation and manipulation

means time and cost savings. These reference methods require qualified personnel, for daily

instrument calibrations using standard solutions and preparation of samples prior to analysis.

This sample preparation step is time-consuming, and contamination may occur (e.g., iron)

from the laboratory environment, leading to inaccurate results.

Applications of good laboratory practices allied to a suitable internal control plan are essential

to obtain accurate results.

Most X-ray fluorescence (XRF) techniques comply with desired features for analysis of

vegetal specimens, including:

a) the possibility of performing analysis directly on solid samples;

b) multi-element capability;

c) the possibility of performing qualitative, semi-quantitative and quantitative

determinations;

d) a wide dynamic range;

e) high throughput;

12 

 

f) low cost per determination.

The main drawbacks of XRF instrumentation, restricting its more frequent use for

environmental purposes, have been its limited sensitivity for some important pollutant

elements (e.g., Cd and Pb) and a somewhat poorer precision and accuracy (unless special

sample preparation by melting is carried out) compared to atomic spectroscopic techniques.

Nevertheless, there have recently been improvements in XRF instrumentation (e.g.,

development of spectrometers using digital signal processing and enhancement of X-ray

production with better designs for excitation-detection), which have added the advantage of

increased instrumental sensitivity, thus allowing improvement in both precision and

productivity [20][30]. These improvements have increased interest in the possibility of using

XRF spectroscopy as a technique in the environmental field.

There are many types of XRF spectrometer available on the market today, most of which can

be separated into two categories: wavelength-dispersive XRF (WDXRF) and energy-

dispersive XRF (EDXRF). In WDXRF, the characteristic radiation emitted from the sample is

separated into wavelengths using a diffraction device. The energy resolution in the WDXRF

spectra is governed by the appropriate use of diffraction crystals in each region of the

spectrum. Usually in WDXRF spectrometers, the analysis of different elements is carried out

in a sequential way by scanning synchronously the orientation of the monochromatic device

and the detector. However, in multi-channel spectrometers, the use of several diffraction

devices or detector set-ups allows one to measure several elements simultaneously (although

the number of channels is limited). Unlike WDXRF systems, conventional EDXRF

spectrometers comprise only two basic units – the excitation source and the spectrometer or

detection system. In this case, the resolution of the EDXRF system is directly related to the

resolution of the detector. Typically, a semiconductor detector of high intrinsic resolution is

employed [Si(Li)] [22]. The use of this type of detector allows one to record an electronic

signal for every detected photon the current of which is proportional to the energy of the

photon. Then, a multi-channel analyzer is used to collect, integrate and display the resolved

pulses. Interestingly, using this configuration, all of the X-rays emitted by the sample in a

certain direction range are collected at the same time (simultaneously measured), resulting in

high speed of acquisition and display of data. Both configurations [EDXRF (Figure 1 a) and

WDXRF in (Figure 1 b) ] have been widely used in plant-sample analysis. The selection of

the most suitable configuration is based on the requirements for a given purpose.

13 

 

Compared to the WDXRF the EDXRF has poorer limit of detection and accuracy. This is due

to the lower intensity of the X-ray source (X-ray tube) and the larger effect of the

interferences (absorption of the fluorescence by the sample) to the measurement and detection

[32]. The LODs for high Z elements are, in general, at the low µg/g range, the LODs for light

elements (i.e. Li, Na, Mg, and Al) are usually at levels of tens or hundreds of µg/g, mainly

due to the inherently low fluorescent yield of these elements. However, this factor is not a

shortcoming, if we consider that these elements are present at high concentrations in plant

tissues. [26]

Figure 1. Configurations of XRF spectrometers.

2.3.1 Sample preparation [22] Sample decomposition procedures used for determination of elements in the classical plant

analysis influence the results of element mass fraction determination, depending on the

analyte of interest and the matrix composition of the sample. In contrast, the energy dispersive

a) 

b) 

14 

 

X-ray fluorescence techniques has proven to be an effective tool for screening and

determining simultaneously the mass fractions of several elements in vegetal matrices. Since

matrix digestion stage is not required in direct XRF analysis, the risk of having poor

recoveries for some elements or contaminations are avoided. The proposed method is less

time consuming and requires fewer amounts of reagents.

Most solid materials require sample pretreatment to make them homogeneous and to ensure

the quality and the reproducibility of measurements. Commonly, this procedure is based on

crushing or grinding the materials into fine powder followed by pelletization at high pressure.

Specifically, this is the most frequent way of preparing vegetal samples for analysis by XRF

techniques.

Prior to grinding, vegetal samples are usually oven-dried or freeze-dried to remove or to

immobilize water content. In reducing the vegetal material to a fine powder, grinding or

milling is usually employed. This is bound to the risk of contamination arising from the

material of the grinding device. Particularly for trace elements, precautions should be taken by

choosing suitable containers (e.g., agate, silicon carbide, boron carbide, and tungsten carbide).

Agate grinding elements may introduce into biological material significant contamination by

Ti, V, Cr, Mn, Fe and Pb. For plant samples, the effects of segregation and grain size are

considered to be relatively small [22][31].

The addition of a binding agent before pelletization is often used to make vegetal pellets more

stable. The binder must be free from significant contaminant elements and must have low

absorption. It must also be stable under vacuum and irradiation conditions and it must not

introduce significant inter-element interferences. Boric acid [23], cellulose [24] and wax [25]

are the most used binders for forming vegetal pellets. Different pelletization tests with vegetal

samples showed that the necessary amount of binder depended strongly on the original

vegetal material (e.g., grasses, leaves or stalks).

2.3.2 Matrix effects [26] In XRF, the analytical signal is the intensity of measured characteristic radiation (in counts/s),

which is proportional to the mass fraction of the element from which it originates in the

sample being analyzed. However, this relationship is not generally linear, since it depends on

physical and chemical effects of matrices. Physical effects of matrices result from variations

in physical characteristics of the sample, including particle size, uniformity, homogeneity and

15 

 

surface condition. As stated in the previous section, for plant samples, the effects of

segregation and grain size are considered to be relatively small if adequate procedures are

used for grinding and pelletization.

Chemical effects of matrices result from differences in concentrations of interfering elements

present in the sample. Vegetal materials comprise mainly C, N, H and O (98%), which are

mostly transparent to X-rays. As a result, absorption of the measured X rays by vegetal

matrices is relatively small compared to absorption by matrices of other materials (e.g., rock

or soil samples), so absorption will be influenced by minor concentrations of other elements

present in the vegetal matrix (e.g., S, Cl, K, Ca and Fe).

2.4 Validation [27] 

The implementation of QMS in a chemistry laboratory implies the validation/verification of

the used analytical methods. In the particular case of EDXRF determination of major nutrients

in forage, there seem to be no methods approved by international standardization bodies, and

method validation therefore becomes an unavoidable task. Validation serves as a means for

assessing possible sources of error and facilitating their control by elimination, reduction or

correction. Validation must take into consideration the scope of the method, as well as a clear

description of the main characteristics of performance, i.e. sensitivity, selectivity, linear range,

precision, trueness, limits of detection and uncertainty quantification. These characteristics are

the basis for confirming the fitness for purpose of the implemented method.

Unfortunately, there is still little consensus on how to report the analytical results from the

EDXRF analysis. Different approaches are used assess the traceability of the results and to

evaluate the characteristics of performance of the analytical methods, including linearity,

working range, precision, trueness and detection limits.

2.4.1 Traceability Traceability is defined by the International Vocabulary of Basic and General Terms in

Metrology [33], as “. . . the property of the result of a measurement or the value of a standard

whereby it can be related to stated references, usually national or international standards,

through an unbroken chain of comparisons, all having stated uncertainties”.

The most suitable ways to link the EDXRF results to stated references are the analysis of

certified reference materials (CRM) or the comparison with the results obtained by alternative

16 

 

methods, accepted as reference methods. The expression of the results must include stated

uncertainties.

2.4.2 Precision and trueness The concept of precision is adopted in the ISO/IEC 17 025 [34] as defined in ISO standard

5725-1 [35]: as a measure of closeness of agreement between results (spread of results from

replicate analyses). The standard deviation of n replicate measurements from a single sample

constitutes the primary contribution to precision, and such measure is often defined as

repeatability. The main sources of such spread in the case of EDXRF analysis are due to

counting statistics, and when the method includes x-ray spectrum evaluation and

interpretation, the quality of the spectrum fit performed by the analyst. Random errors

performed in other operational steps (sample preparation, quantification) also affect the

method precision. Therefore, the precision must be evaluated as the spread of results from

different replicates (each one involving all steps of the analytical procedure), so as to

comprise all of the sources of uncertainty in the concept of reproducibility. If replicate pellets

of a sample are measured, the uncertainty due to deviations in sample preparation will be also

taken into account. If the measurements are carried out on different days a more overall

estimation of precision is achieved - between-day reproducibility.

Trueness is also adopted in the ISO/IEC 17025 as defined in ISO standard 5725-1: as a

measure of closeness of agreement between the arithmetic mean of a large number of test

results and the true or accepted reference value. The trueness of the achieved results can be

assessed only when the uncertainties of the reference values are comparable to the precision

of the method. There are two sources of bias in the results of EDXRF analysis: (1) due to

blank interferences and (2) due to inaccuracies in the quantification procedure.

Characteristic Zeta-score from ISO 13528:2005 [36] can be used for the evaluation of the

measured value to the certified reference material value,

(1)

where the Xlab is the result obtained in the laboratory with the EDXRF method and ulab is the

standard uncertainty of that value, the Xref is the value of the certified reference material and

17 

 

uref is the standard uncertainty from the certificate of that material. The absolute values of

Zeta-scores (|Z| values) are used for assessing the acceptability of the results as described in

Table 2.

Table 2. Assessemnt of acceptability of the Results.

|Z| Value Acceptability of the result

|Z|≤ 2 Acceptable result

2 <|Z| < 3 Doubtful result

|Z|≥3 Unacceptable result

2.4.3 Limit of detection and limit of quantitation The limit of detection is defined as the value resulting from a signal corresponding to 3 times

the standard deviation of the noise signal. In EDXRF practice, detection limit for an element i

is customarily calculated by using this value, the instrumental sensitivity Si (counts s-1 w/w-1

[mA-1]) and the measuring time tmeas.[27] A main contribution to noise signal in XRF spectra

comes from the continuum under the peak (Ncont). Some peaks are also observed in a

measurement performed for a blank sample with a net peak area Nblank, or in the absence of

sample (instrumental background, net peak area Nbkgd). In general, the probability distribution

of the results of a series of measurements for any of these signals can be considered as close

to a Poisson distribution, and in such case the limit of detection can be calculated as:

(2)

where I is the respective count rate (s-1). LOQ is estimated in this work as LOQ = 3 x LOD.

2.4.4 Measurement uncertainty Measurement uncertainty (or simply uncertainty) is the parameter associated with the result of

a measurement that characterizes the dispersion of the values that could reasonably be

attributed to the measurand. It incorporates all components of uncertainty from the various

18 

 

stages of the measurement and the analytical process (procedure used to calculate the overall

uncertainty is called uncertainty budget). Since EDXRF has different quantitative approaches

or methodologies for the determination of element mass fractions, the uncertainty budget must

be carried out for the specific quantitative method used.

However, in many cases the theoretical model applied is very complex or even unknown.

Many commercial instruments are provided without detailed specifications on the model used

for calculations. In such cases, a simplified method can be used to calculate the combined

uncertainty. Through specific experiments one can assess the uncertainty due to different

individual sources or combination of several sources affecting the measurand; finally the

uncertainty can be calculated by mathematically combining these uncertainty sources.

The use of such approach is advised if the sources contributing to uncertainty are independent,

and no significant sources are neglected. It is worth noticing that the uncertainty estimate

obtained by this approach reflects only the uncertainty for the mass fraction value and the

particular matrix of the used CRM. An expression relating the uncertainty with the mass

fraction uc(wi)= f (wi) can be obtained by analyzing several CRMs with different mass

fractions.

The uncertainty of each component in the case of this approach can be estimated as the

standard deviation of the replicate results of an experiment designed in such a way that the

effect of a particular source of uncertainty is reflected.

The estimation of uncertainty resulting from random errors in all steps of the analytical

method can be defined by the concept of reproducibility. For example, replicate

measurements of a single sample pellet are supposed to reflect the uncertainty due to

instability in x-ray tube flux or electronic processing (repeatability in measurement). If

replicate pellets of a sample are measured, the uncertainty due to deviations in sample

preparation will be also taken into account. If the measurements are carried out on different

days and by different operators, a more overall estimation of uncertainty is achieved between-

day reproducibility. The relative uncertainty associated to reproducibility can be quantified as

the ratio of the standard deviation to the average value of the replicate results:

(3)

19 

 

An experiment designed to reflect the uncertainty due to reproducibility is supposed to

comprise all of the sources of random errors during analysis. However, performing specific

experiments aimed at assessing the uncertainty due to a single component is of great value to

evaluate the effect of each component on the analytical performance.

The assessment of the uncertainty due to bias resulting from the quantification model, matrix

effects, the presence of instrumental blank signal or due to other sources can be estimated by

repeated measurement of the bias of the results (qi) of replicate analysis. Bias is defined as the

difference between the obtained result and the certified value.

(4)

The average bias is subsequently used to correct the results, and the uncertainty due to bias

correction can be quantified as

(5)

The calculation of the combined uncertainty is based on the law of error propagation [28]. In

this approach, the combined uncertainty shall be calculated by a simple error propagation

formula; at least the uncertainties due to precision and bias must be considered:

(6)

Finally, the expanded uncertainty is calculated by multiplying the value of the combined

uncertainty by a coverage factor k=2, in order to ensure a confidence level of roughly 95%

[28]. All uncertainties in this work are presented at k = 2 level, unless specifically noted

otherwise.

20 

 

3 Experimental 

3.1 ICP‐OES method 

ICP-OES with the Perkin Elmer Optima 2000DV inductively coupled plasma optical

emission spectrometer was used in this study as the reference method. The calibration of the

ICP-OES was done using the stock standard solution from Perkin Elmer. The wavelengths

selected for the elements are listed in Table 3.

Table 3. Used wavelengths

Analyte Wavelength (nm)

Ca 317.933

K 766.490

Mg 285.213

P 213.617

The internal quality control of the digestion procedure was achieved by analyzing duplicate

samples and method blanks for each batch of sample run. Results of the analysis of Certified

reference materials with ICP-OES are given in Table 4 and Table 5.

Table 4. Results of analysis SRM 1515 Apple leaves. Element/analyte ICP-OES Ceritfied Ca 1.60±0.19 1.526±0.015 K 1.51±0.15 1.61 ±0.02 P 0.156±0.020 0.159±0.011 Mg 0.263±0.06 0.271±0.008 All results expressed as c ± U (%), k=2 Table 5. Results of analysis NCS DC 73348 Branches and leaves. Element/analyte ICP-OES Ceritfied Ca 2.22±0.27 2.22±0.07 K 0.78±0.08 0.85±0.03 P 0.076±0.010 0.083±0.003 Mg 0.259±0.060 0.287±0.011 All results expressed as c ± U (%), k=2

21 

 

The results obtained with reference method (Table 4 and Table 5) are in good agreement with

the standard reference materials certified values which are well within the estimated expanded

uncertainties of our measurements.

3.2 Sample preparation for the ICP‐OES method 

In this work, the measurements on powdered forage samples was carried out according to

standard reference method [29]. Microwave wet digestion was used for sample preparation.

All reagents used in digestions were of analytical (Suprapur) quality: nitric acid (Riedel-de

Haen), hydrogen peroxide (Riedel-de Haen). Ultrapure water obtained from a Milli-Q purifier

system (Millipore Corporation) was used throughout the work.

In digestion approximately 0.5 g of sample was placed in 100mL PTFE reactor with 5 mL

HNO3 (65%) and 2 mL H2O2 (33%). When the foam caused by organic matter decomposition

disappeared, the vessel was capped and heated following a two-stage digestion program using

an Anton Paar Microwave. The first step of digestion consisted of heating of 10 minutes to

reach 170°C and the second step of 10 minutes at 170°C. After 20 min cooling step, sample

digests were transferred into a 25 mL flask and diluted to the mark with Milli-Q water.

3.3 EDXRF Method 

The energy dispersive X-ray fluorescence spectrometer Twin-X from Oxford instruments

Analytical (High Wycombe- United Kingdom) with palladium X-ray tube and two analysis

heads was used for this study. The Focus – 5+ detector allows the determination of elements

with low atomic number, i.e. from magnesium (Z=12) to zinc (Z=30) and the PIN detector

that allows the determination of a wider range of elements, from calcium (Z=20) to uranium

(Z=92).

Standard operation uses air environment in the measurement head. However detector Head 1

(Focus 5+) is fitted with Helium purge that is useful in cases where low energy X-rays are

likely to be absorbed in air. Helium flushing also eliminates the argon peak from argon in the

air which can interfere with other peaks of interest.

During the analysis a special rotating and moveable sample holder was used. This sample

holder provides controlled rotation movements of the sample while a measurement is carried

22 

 

out, consequently simulating homogeneous excitation conditions over the sample. In this way

it compensates for effects caused by the typical inhomogeneous distribution of intensity

within the x-ray beam exciting the elements in the sample.

Measurement conditions for the method EDXRF were chosen based on the literature [37, 38]

and recommendations from manufacture of Twin- X instrument (Oxford Instruments) to allow

a better compromise between different elemental fluorescence sensitivities. Measurement

conditions used in this study are presented in Table 6.

Table 6. Measurement conditions for EDXRF analysis

Element Voltage

(KV)

Current

(µA)

Detector Atmosphere Acquisition

time (s)

Filter

Ca 12 50 PIN Air 50 Primary

K 8 374 Focus 5+ He 40 Secondary

P 7 208 Focus 5+ He 50 Primary+Secondary

Mg 4 750 Focus 5+ He 120 Secondary

The total measuring time was approximately 300 s per sample, including sample loading,

target exchange, current and voltage regulation and helium flushing.

3.4 Sample preparation for EDXRF analysis 

3.4.1 Sampling and homogenization of forage material A total of 39 fresh samples of grass silage from all over Estonia brought to Agricultural

research center by Estonian farmers were dried at 70 °C for 24 h. The dried samples were then

ground with grinder (Retsch SM 1000). In order to obtain highly homogeneous samples,

further grinding was performed by a ball mill (Fritsch, Idar-Oberstein, Germany) for 2

minutes. Samples were then stored in plastic containers, and used for analyses by the

analytical techniques described below.

All ED-XRF measurements were made on three different pellets of the same sample. Pellets

were pressed with a manual hydraulic press (max pressure, 12 tons) (Specac, Kent, United

Kingdom) using sleeve-and-plunger technique where the binder matrix supports the layer of

sample, but is not mixed with the sample. The diameter of the pellet die was 40 mm.

23 

 

The samples were split into two sets:

a) Calibration set consisting of 18 silage samples

b) Validation set consisting of 21 silage samples

The samples used in this study were chosen to represent the typical concentration ranges of

forage material made in Estonia (Table 1.)

3.4.2 Certified reference materials Two certified reference materials were used to check the trueness and global uncertainty for

the analytical method proposed. In all cases, the reference materials were supplied in the form

of fine powder. The certified concentrations of K, Ca, P and Mg are presented in Table 7.

a) SRM 1515 – Apple leaves from National Institute of Standards and Technology, USA

b) NCS DC73348 - Bush branches and leaves from the National Research Centre for

Certified Reference Materials, China

Table 7. Element contents (%) of certified reference materials*

CRM/Element Ca K P Mg

SRM 1515 1.526±0.015 1.61±0.02 0.159±0.011 0.271±0.008

NCS DC73348 2.22±0.07 0.85±0.03 0.083±0.003 0.287±0.011

* All results expressed as c ± U (%), k=2

3.4.3 EDXRF calibrations Calibrations were established for K, Ca, P and Mg using a set of 18 forage samples, their

reference values obtained with ICP-OES are listed in Table 8.

24 

 

Table 8. Reference values (%) of calibration samples Sample

no Ca K Mg P caliba

1 0.715 1.84 0.138 0.22 * 2 0.341 2.61 0.102 0.31 * 3 1.73 2.59 0.211 0.29 * 4 1.59 2.36 0.143 0.29 * 5 0.75 1.92 0.163 0.29 v 6 2.03 3.78 0.273 0.41 * 7 0.891 1.19 0.149 0.15 * 8 1.53 2.43 0.279 0.33 * 9 0.935 2.10 0.188 0.30 * 10 0.932 2.26 0.171 0.25 * 11 2.04 2.11 0.354 0.26 * 12 2.15 1.84 0.410 0.26 * 13 2.09 2.07 0.375 0.27 * 14 0.81 1.77 0.167 0.28 v 15 0.42 2.12 0.093 0.24 v 16 0.17 0.56 0.089 0.13 * 17 0.354 2.31 0.086 0.26 * 18 0.494 2.74 0.106 0.27 v 19 1.35 1.72 0.240 0.25 * 20 2.27 2.61 0.162 0.23 * 21 0.47 3.86 0.180 0.51 * 22 1.65 2.16 0.185 0.28 v 23 1.65 2.19 0.184 0.29 * 24 0.556 2.59 0.200 0.39 v 25 0.555 2.58 0.200 0.38 * 26 27 28 29 30 31 32 33 34 35 36 37 38 39

2.39 0.94 1.39 1.24 1.13 1.23 1.06 0.71 1.14 0.66 0.66 1.08 1.63 1.93

2.62 2.07 2.87 0.84 1.55 2.70 2.84 2.47 2.99 2.77 1.51 2.34 3.16 2.08

0.1500.2680.3330.4110.3740.2830.2960.1610.2320.2000.0950.1380.2280.276

0.216 0.28 0.337 0.300 0.275 0.318 0.242 0.341 0.380 0.304 0.181 0.285 0.308 0.227

v v v v v v v v v v v v v v

a Samples with * were used in the calibration set

25 

 

4 Results and discussion 

4.1 Evaluation of the EDXRF method 

For the evaluation of calibrations linear range, linearity, limit of detection and standard error

of calibration was evaluated. The obtained characteristics are presented in Table 9.

Table 9. Calibration performance characteristics of the EDXRF method.

Element/analyte Min (%) Max (%) N R2 SEC (%) LOD (%) LOQ (%)

Ca 0.35 2.27 10 0.996 0.048 0.1 0.3

K 0.56 3.78 8 0.997 0.015 0.3 0.9

P 0.13 0.51 12 0.980 0.055 0.12 0.36

Mg 0.07 0.28 8 0.875 0.023 0.12 0.36

According to Table 9 potassium had the best calibration performance characteristics (SEC is

the lowest and R2 is closest to 1) compared to other elements. The standard error of

calibration was derived from linear regression and in all cases it was acceptable for the

respective elements according to the concentration ranges studied. Magnesium shows the

highest scatter of calibration points as can be seen from calibration graph in Figure 2 and also

has the lowest R2 value compared to other elements. This may be caused by the fact that Mg

is the lightest element studied and therefore gives the lowest signals (238.3- 357.2cps) by the

EDXRF apparatus.

The linear ranges of the developed calibrations cover the ranges of typical element

concentrations in forage samples given in Table 1. Based on the calibration graphs on Figure

2 and correlation coefficients in Table 9 it can be concluded that the developed method has

acceptable linear ranges for all studied elements and these ranges could be considered the

working ranges of the developed EDXRF method for determination of Ca, K, P and Mg in

forages.

The LOD values determined according to equation 1 as well as the LOQ values are given in

Table 9. Limits of detection of ICP-OES are estimated to be: K 0.0032%, Ca 0.0017%, P

0.001% and Mg 0.0001%. In the EDXRF analysis the LOD and LOQ values for the lighter

26 

 

elements are considerably higher due to the poorer excitation and the absorption of their

emitted characteristic X-rays in the sample itself. The LOQ values of the elements are at the

low end of the common element concentrations in forages according to Table 1. The main

purpose of the developed method would be evaluation of the elements concentration range in

forages to help the producers of these forages to fertilize their fields appropriately and

properly feed the animals according to normative. The LOQ values of Ca and K are below the

lower limit of the usual element content of forage. Therefore these elements can be readily

determined. In the case of P and Mg the LOQ is inside the typical element content range.

Therefore in most cases the determination of these elements is possible at semiquantitative

level. The accuracy of determination of Mg and P is further elaborated below. Nevertheless,

such determinations would still help the customer to make decisions since the LOD values are

close to the lowermost end that are allowed by the norms. The LOD values of the EDXRF

method can be considered acceptable and the method can be used as an alternative to the

standard method to evaluate the nutritional elements contests in forages.

4.2 EDXRF validation 

A second set of 21 forage samples was analyzed using the calibrations described above. In

Table 10 the obtained results of the studied elements in forage samples for EDXRF and

standard method (ICP-OES) are presented.

Even though the LOQ values presented in Table 9 are found to be higher than is acceptable

for Mg and P analysis, we can conclude from the results in Table 10 that the most values that

are below the LOQ still agree with the values obtained by ICP-OES method. So the LOQ

values found in this study might be overestimated. The Root Mean Square errors (RMS) of

the results in Table 10 show the average fit between the reference values obtained with ICP-

OES and the values acquired with EDXRF. The ratio of average signed error to the RMS

shows that there are no significant systematical effects of the results.

27 

 

Figure 2. Calibration graphs of the EDXRF method.

28 

 

Table 10. Comparison between reference method (ICP-OES) and EDXRF method. All results

are given in %.

No Ca K P Mg

ICP-OES EDXRF ICP-OES EDXRF ICP-OES EDXRF ICP-OES EDXRF

5 0.75 0.77 1.92 1.93 0.272 0.302 0.163 0.191

14 0.81 0.84 1.77 1.84 0.252 0.334 0.167 0.160

15 0.42 0.44 2.12 2.14 0.240 0.263 0.093 0.077

18 0.49 0.52 2.74 2.85 0.272 0.292 0.106 0.152

22 1.65 1.60 2.16 2.18 0.284 0.312 0.185 0.207

23 1.65 1.58 2.19 2.19 0.290 0.305 0.184 0.202

24 0.56 0.60 2.59 2.75 0.386 0.248 0.200 0.182

26 2.39 2.34 2.62 2.64 0.216 0.218 0.150 0.190

27 0.94 0.96 2.07 2.06 0.284 0.276 0.238 0.192

28 1.39 1.35 2.87 2.29 0.337 0.292 0.333 0.245

29 1.24 1.24 0.84 0.80 0.300 0.298 0.411 0.358

30 1.13 1.13 1.55 1.58 0.275 0.286 0.374 0.326

31 1.23 1.25 2.70 2.67 0.318 0.333 0.283 0.235

32 1.06 1.05 2.84 2.61 0.341 0.318 0.296 0.246

33 0.71 0.70 2.47 2.52 0.242 0.292 0.161 0.199

34 1.14 1.16 2.99 2.93 0.380 0.417 0.232 0.221

35 0.66 0.67 2.77 2.70 0.304 0.325 0.200 0.188

36 0.66 0.71 1.51 1.63 0.181 0.213 0.095 0.093

37 1.08 1.06 2.34 2.29 0.285 0.287 0.138 0.169

38 1.63 1.65 3.16 3.35 0.308 0.367 0.228 0.226

39 1.93 2.09 2.08 2.06 0.227 0.244 0.276 0.235

RMS (relative)

4% 7% 16% 18%

RMS (%) 0.047 0.154 0.044 0.038 Average

signed error (%)

0.01

-0.01

0.01

-0.01

29 

 

In order to state the capability of EDXRF for determination Ca, K, P and Mg in forage

samples, precision and accuracy were evaluated for each element. The precision of the

developed EDXRF method was investigated by measuring three pellets of each of the 21

validation samples, with different concentrations throughout the calibration range, under the

same calibration conditions. Compared to the reference method, the results of EDXRF

method for Mg and P show the highest discrepancies as can be already seen from the

calibration graph of magnesium on Figure 2 that may be caused by the fact that the

concentrations ranges of magnesium and phosphorus content in forages are very narrow (0,1-

0,4%) and are very close to the LOD values of the EDXRF method given in Table 9.

The relative standard deviations calculated from the measurements were below 4% for Ca and

K and below 14% for P and Mg, depending on the ratio of the element concentration to the

detection limit.

The accuracy and trueness of the results were assessed by measurements of two standard

reference materials (SRM 1515, elements in Apple leaves and NCS DC 73348, elements in

Branches and leaves), with matrix composition similar to real samples studied. The matrices

of the CRM-s (apple leaves and branches and leaves) do not ideally match with the actual

forage samples (made of grass) used for calibration. This small difference in matrices can

cause deviations between the EDXRF and actual certified reference material results.

Comparison of EDXRF method with ICP-OES with the certified reference material is

presented in Table 11 and Table 12. The results show good accuracies, which are well within

the estimated expanded uncertainty of our measurements of standard reference materials.

Table 11. Results of analysis SRM 1515 Apple leaves. All results presented in %

Element/analyte EDXRFa ICP-OESa Certified Zeta-scoreb

Ca 1.69±0.27 1.60±0.19 1.526±0.015 1.41

K 1.77±0.43 1.51±0.15 1.61 ±0.02 0.77

P 0.159±0.019 0.156±0.020 0.159±0.011 -0,04

Mg 0.253±0.061 0.263±0.06 0.271±0.008 -0.78 a All results are given as a mean of three parallel measurements. b Zeta –score values are

calculated for EDXRF method.

30 

 

Table 12. Results of analysis NCS DC 73348 Branches and leaves. All results presented in %

Element/analyte EDXRFa ICP-OESa Certified Zeta-scoreb

Ca 2.23±0.36 2.22±0.27 2.22±0.07 0.07

K 0.75±0.18 0.78±0,08 0.85±0.03 -1.05

P 0.100±0.015 0.076±0.010 0.083±0.003 1.10

Mg 0.279±0.067 0.259±0.060 0.287±0.011 -0.34 a All results are given as a mean of three parallel measurements. b Zeta –score values are

calculated for EDXRF method.

Characteristic Zeta-scores, calculated according to equation 6 was used for the evaluation of

the measured value to the certified value. All calculated Zeta-score values for all elements

studied for both CRM were found to be less than 2 which means the results measured with

EDXRF are acceptable according to Table 2. Thus the present results of the standard

reference materials show accuracies which are well within the estimated combined standard

uncertainties of our measurements. These results confirm that the present analyses were

correctly performed and the uncertainty of the measurements realistically assessed. Small

deviations between the measured and certified values can also be explained by spectrum

fitting uncertainties and/or counting statistics. These results also form the basis of claiming

traceability for future results obtained with this method.

Uncertainties were evaluated by measuring three independent replicate pellets of 21 validation

set samples and three replicates of two certified reference materials. The estimated relative

expanded uncertainties (U) at k = 2 level for results obtained by ICP-OES (10-23%) were

better than those by XRF (14 -24%). The accuracy for analysis of Ca is approximately 14%

for XRF and for the ICP-OES technique not significantly different (12%). For K the accuracy

by ICP-OES is approximately 10% and for XRF 24%, the results of EDXRF method for Mg

and P show estimated uncertainty 17% and 15%, respectively that seem to be even better

compared to ICP method. This might be due to the sample preparation step in the ICP-OES

method. Crucial steps in sample preparation are weighing, dissolving and dilution of the

sample. These procedures may cause the discrepancies of the values compared to the EDXRF

sample preparation, where there are no preparative procedures mentioned above. Results

obtained by the EDXRF technique are on the other hand much more dependent on the

homogeneity of the sample. Comparison of silage samples with CRM is therefore important

31 

 

in assessment of the homogeneity of the material prepared for analysis and its influence on the

results. In this work the sample homogenization and grinding was done with ball mills. Based

on the results of RSD between three independent pellets analyzed and the Zeta-scores, the

particle size and homogenate of the forage samples is satisfactory for the EDXRF method

developed.

Also the matrices of the CRM-s (apple leaves and branches and leaves) do not match 100%

the actual forage samples (made of grass) from what the calibration samples are made off.

This small difference in matrix material can also cause deviations between the EDXRF and

actual certified reference material results.

Traceability of the developed EDXRF method for the determination of major nutritional

elements in forage samples was established through the analysis of two certified reference

materials and the results are presented in Table 11 and 12 with stated uncertainty. Results

obtained with EDXRF are traceable to the SI units.

It should also be mentioned that the time between pelletizing and analysis should be as short

as possible in order to avoid changes in surface of the pellet that could alter the measures

intensities of some elements and therefore the analyte concentrations. The surface of the pellet

is strongly influenced by the sample particle size. Also analysis of a large number of parallel

sub-samples helps to achieve a statistically more meaningful average concentration for the

elements of interest in inhomogeneous samples (e.g. plant material, soil etc.)

Based on the results described above, the developed EDXRF method for the determination of

Ca, K, P and Mg in forages could be successfully implemented for a rapid determination of

the major nutrient elements in forages in Estonia after further internal quality control for the

method is implemented.

32 

 

Determination of Calcium, Potassium, Phosphorus and

Magnesium in Forages by Energy Dispersive X-ray Fluorescence

spectrometry

Liina Kruus

5 Summary  

In this study the usefulness of a non-destructive methodology based on EDXRF for the

simultaneous determination and monitoring of some major elements in silages made of grass

has been tested and an EDXRF method for determination of several major nutritional

elements (Ca, K, P and Mg) in forages has been developed. The results of this study highlight

the suitability of EDXRF analysis for the determination of several major nutritional elements

(Ca, K, P and Mg) in forages.

Precision and accuracy of the proposed method were checked by analyzing a reference plant

material SRM 1515, elements in apple leaves and NCS DC 73348, elements in branches and

leaves. Good agreement was achieved between certified values and data obtained with the

developed EDXRF method.

The relative expanded uncertainties and limit of detections for the developed method were

calculated. Relative expanded uncertainties (at k = 2 level) for the studied elements are: Ca

14%, K 24%, P 15% and Mg 17% and the limits of detection are: Ca 0.3%, K 0.9%, P 0.36%,

Mg 0.36%.

The simple sample preparation, versatility and relatively fast determinations make EDXRF an

attractive alternative for the routine analysis of forage material in screening studies.

33 

 

6 References 1. Buchanan-Smith J., Leeson, S., Chase, L C.F.M. de Lange,Potassium in Animal

Nutrition, Better crops, 1998, vol. 82.,No. 3

2. V.Sikk, Loomade mineraalne toitumine, Tartu, 2005

3. Buchanan-Smith J., Leeson, S., Chase, L C.F.M. de Lange, Phosphorus in animal

nutrition, Better Crops, 1999, Vol. 83 No. 1

4. Boss, C., Fredeen, K. J. Concepts, instrumentation, and techniques in inductively

coupled plasma optical emission spectrometry, 2 nd Edition, Perkin Elmer, USA, 1997

5. Optima 2000 DV users guide, Perkin Elmer Instruments, Perkin Elmer LLC,

Germany,2000

6. M. Radojevic´, V.N. Bashkin, Practical Environmental Analysis, The Royal Society of

Chemistry, London, UK, 1999.

7. M. Hoenig, H. Beaten, S. Vanhentenrijk, E. Vassileva, Ph. Quevauviller, Anal. Chim.

Acta 358,1998, 85.

8. C.C. Nascentes, M. Korn, M.A.Z. Arruda, Microchem. J. 69, 2001, 37.

9. E. Wieteska, A. Kiolek, A. Drzewin´ ska, Chem. Anal. 42, 1997, 837.

10. I. Lavilla, A.V. Figueiras, C. Bendicho, J. Agric. Food Chem. 47,1999, 5072.

11. J.S. Alvarado, T.J. Neal, L.L. Smith, M.D. Erickson, Anal. Chim. Acta 322, 1996, 11.

12. G.D. Laing, F.M.G. Tack, M.G. Verloo, Anal. Chim. Acta 497, 2003, 191.

13. C. Baffi, M. Bettinelli, G.M. Beone, S. Spezia, Chemosphere 48, 2002, 299.

14. H. Polkowska-Motrenko, B. Danko, R. Dybczyn´ ski, A. Koster- Ammerlaan, P. Bode,

Anal. Chim. Acta 408, 2000, 89.

15. K. Lamble, S.J. Hill, Analyst, 120, 1995, 413.

16. A. Sahuquillo, R. Rubio, G. Rauret, Analyst, 124, 1999.

17. F.E. Smith, E.A. Arsenault, Talanta 43, 1996, 1207.

34 

 

18. Instruction manual Multiwave 3000, Anton Paar, Graz, Austria, 2004

19. Somenath Mitra, Sample preparation techniques in analytical chemistry, Volume 162,

John Wiley & Sons Inc. Hoboken, New Jersey, 2003

20. P.J. Potts, A.T. Ellis, P. Kregsamer, C. Streli, C. Vanhoof, M. West, P. Wobrauschek, J.

Anal. At. Spectrom. 10, 2006, 1076.

21. M. West, A.T. Ellis, P. Kregsaner, P.J. Potts, C. Streli, C. Vanhoof, P. Wobrauschek, J. Anal. At. Spectrom. 10, 2007, 1304.

22. R.E. Van Grieken, A.A.Merkowicz, Handbook of X-ray fluorescence spectrometry,

Marcel Dekker Inc., New York, 2002

23. E.V. Chuparina, T.N. Gunicheva, J. Anal. Chem. 58, 2003, 856.

24. J. Ivanova, R. Djingova, I. Kuleff, J. Radioanal. Nucl. Chem. 238, 1998, 29.

25. C. Anderson, F. Moreno, F. Geurts, C. Wreesmann, M. Ghomshei, J. Meech,

Microchem. J. 81, 2005, 81.

26. E. Margui, I. Queralt, M.Hidalgo Application of X-ray fluorescence spectrometry to

determination and quantitation of metals in vegetal material, Trends in Analytical

Chemistry, Vol. 28, No. 3, 2009

27. R. Padilla A lvarez, A. Markowicz, D. Wegrzynek, E. Chinea Cano, S. A. Bamford, D.

Herna ndez Torres, X-Ray Spectrom.; 36, 2007, 27–34

28. EURACHEM/CITAC Guide for Quantifying uncertainty in analytical measurement,

Edison S.L.R, Rosslein M., Williams A., Second Edition, 2000

29. International Organization for Standardization, ISO 27085:2009 Animal feeding stuffs –

Determination of calcium, sodium, phosphorus, magnesium, potassium, iron, zinc,

copper, manganese, cobalt, molybdenum, arsenic, lead and cadmium by ICP-OES

30. Stikans M., Boman J., Lindgren E.S., X-Ray Spectrom, 27, 1998 ,367-372

31. R.H. Obenauf, R.Bostwick, W. Fithian, M. DeStefano, A., Torres, Handbook of Sample

Preparation and Handling, SPEX CertiPrep Inc., New York, 2005

32. http://tera.chem.ut.ee/~ivo/Spec/XR/X_Rays_XRF_Spec.pdf last updated Spring 2010.

35 

 

33. International Organization for Standardization, The Vocabulary of Basic and General

Terms in Metrology, 3rd Edition, 2004

34. International Organization for Standardization, ISO/IEC 17025:2005 General

requirements for the competence of testing and calibration laboratories

35. International Organization for Standardization, ISO 13528:2005 Statistical methods for

use in proficiency testing by interlaboratory comparisons

36. Twin – X Operator manual, Version 3, Oxford Instruments Analytical, High Wycombe,

United Kingdom

37. L. Perring, J. Blanc EDXRF determination of iron during infant cereals production and

its fitness for purpose, International Journal of Food Science and Technology, 2007, 42,

551-555

36 

 

Kaltsiumi, kaaliumi, fosfori ja magneesiumi määramine silos

energiadispersiivse röntgenfluorestsentsispektromeetria meetodil

Liina Kruus

7 Kokkuvõte  

Käesoleva töö eesmärgiks oli välja töötada energiadispersiivne röntgenfluoresentsi-

spektromeetriline meetod kaltsiumi, kaaliumi, fosfori ja magneesiumi määramiseks silos. Töö

eesmärgiks oli välja arendada kiirmeetod, mis oleks võimalikult lihtne, kiire ja odav ning

võimaldaks määrata nimetatud elemente piisava täpsusega ning oleks alternatiiviks ICP-OES

meetodile. Sellise meetodi vajalikkus tuleneb Põllumajandusuuringute keskuse üha

suurenevast silode analüüsimahust.

Töö esimese osas antakse kirjanduslik lühiülevaade K, Ca, P ja Mg tähtsusest loomade

tervisele ning kirjeldatakse erinevaid määramiseks ja proovide ettevalmistamiseks

kasutatavaid meetodeid ning nende eripärasid.

Töö teine, eksperimentaalne osa viidi läbi Põllumajandusuuringute keskuses. Töö teises osas

töötati välja energiadispersiivne röntgenfluoresentsil baseeruv meetod Ca, K, P, Mg

määramiseks silodes. Töös on välja toodud läbiviidud eksperimentide tulemused ja antud

hinnang nendele tulemustele. Arvutatud on meetodi määramispiirid: Ca 0.3%, K 0.9%, P

0.36% Mg 0.36% ja suhtelised laiendmääramatused k = 2 usaldusnivool: Ca 14%, K 24%, P

15%, Mg 17%.

Väljatöötatud meetodiga saadud tulemusi võrreldi referentsmeetodiks olnud ICP-OES

meetodiga ning sertifitseeritud referentsmaterjalidega. Loodud meetodiga saadud tulemused

langesid hästi kokku nii referentsmeetodi (ICP-OES) tulemustega kui ka referentsmaterjalide

sertifitseeritud väärtustega. Saadud tulemustest selgus, et väljatöötatud meetod on piisava

täpsusega ning sobib kasutamiseks alternatiivse meetodina Ca, K, P ja Mg sisalduste

määramiseks silodes.