Microchim Acta 151, 231–239 (2005)

DOI 10.1007/s00604-005-0404-x

Original Paper

Screening Inorganic Arsenic in Rice by Visibleand Near-Infrared Spectroscopy

Rafael Font1;�, Dinoraz Velez2, Mercedes Del Rıo-Celestino1,

Antonio De Haro-Bailon1, and Rosa Montoro2

1 Instituto de Agricultura Sostenible (CSIC), Alameda del Obispo s=n., E-14080 C�oordoba, Spain2 Instituto de Agroquımica y Tecnologıa de Alimentos (CSIC), Apartado 73, E-46100, Burjassot (Valencia), Spain

Received June 30, 2004; accepted May 24, 2005; published online September 12, 2005

# Springer-Verlag 2005

Abstract. The potential of near-infrared spectroscopy

(NIRS) for screening the inorganic arsenic (i-As) con-

tent in commercial rice was assessed. Forty samples of

rice were freeze-dried and scanned by NIRS. The i-As

contents of the samples were obtained by acid diges-

tion-solvent extraction followed by hydride generation

atomic absorption spectrometry, and were regressed

against different spectral transformations by modified

partial least square (MPLS) regression. The second

derivative transformation equation of the raw optical

data, previously standardized by applying standard

normal variate (SNV) and De-trending (DT) algo-

rithms, resulted in a coefficient of determination in

the cross-validation (1-VR) of 0.65, indicative of equa-

tions useful for correct separation of the samples in

low, medium and high groups. The standard deviation

(SD) to standard error of cross-validation (SECV)

ratio, expressed in the second derivative equation,

was similar to those obtained for other trace metal

calibrations reported in NIRS reflectance. Spectral in-

formation relating to starch, lipids and fiber in the rice

grain, and also pigments in the caryopsis, were the

main components used by MPLS for modeling the

selected prediction equation. This pioneering use of

NIRS to predict the i-As content in rice represents an

important reduction in labor input and cost of analysis.

Key words: Near-infrared spectroscopy (NIRS); inorganic arsenic;

brown rice; milled rice.

Rice is the dominant staple food crop in developing

countries, particularly in the humid tropics across the

globe [1]. Almost 96% of the world’s rice is produced

and consumed in developing countries [1], making up

over 70% of the daily energy intake [2]. The protein

component in rice (7–9% by weight) is relatively low

[3], but it forms a major source of protein (50%) in

these countries [2].

With food that is consumed in such large amounts it

is crucial to have information about its toxic trace

levels so that potential effects on human health can

be established. Arsenic (As) and its chemical species

As(III) and As(V), collectively known as inorganic As

(i-As), are the contaminants of interest in this study.

Total diet studies indicate that As concentrations in

rice are higher than those in other products of vege-

table origin [4, 5]. Natural processes and human activ-

ities are the two principal factors responsible for the

introduction of arsenic into the rice-growing environ-

ment. Natural processes of introduction involve soil=water chemistry and climate. Farm management activ-

ities such as fertilization practices, crop rotation and� Author for correspondence. E-mail: font@cica.es

herbicidal=insecticidal uses also act to introduce

arsenic. For inorganic arsenic, soil properties and

the use of pesticides are assumed to be the most

important interactive influences determining its final

concentration [6].

Data on As contents in samples of rice collected

in arsenic-endemic areas such as Taiwan, West

Bengal and Bangladesh show that the As content

ranges between 0.04 and 0.76mg g�1 [7–9]. In non-

arsenic-endemic areas, the highest value reported is

0.776mg g�1 [2, 10, 11]. The very few studies on

i-As contents in rice show concentrations varying

between 0.021 and 0.560 mg g�1 [5–7, 10, 12]. Eval-

uating the contribution of rice to i-As intake is, in

our view, a necessary task in obtaining a more realistic

assessment of the risk of exposure to this toxin, es-

pecially in arsenic-endemic areas and developing

countries.

The standard methodologies for trace metal deter-

mination offer a high level of precision but have some

handicaps, such as high cost of analysis, slowness of

operation, destruction of the sample, and use of hazard-

ous chemicals. In contrast, Near Infrared Spectro-

scopy (NIRS) is a valuable technique that offers

speed and low cost of analysis, as well as sample

analysis without the use of chemicals. The spectral

information can be used for simultaneous prediction

of numerous constituents and parameters relating to

the samples, once appropriate calibration equations

have been prepared from sets of samples analyzed

by both NIRS and conventional analytical techniques.

After calibration, the regression equation permits

accurate analysis of many other samples by prediction

of results on the basis of the spectra.

NIRS has been applied to the analysis of metal

contents mostly in the environmental field, and to a

lesser extent in the agro-food fields. In environmental

studies various authors have reported the analysis of

heavy metals in lake sediments [13], studies concern-

ing the chemical characterization of soils [14], and the

determination of heavy metals and arsenic by NIRS in

plant tissues [15, 16]. Recently, in the agro-food field

the feasibility of this technique for measuring K, Na,

Mg, and Ca in white wines was demonstrated [17]. In

the speciation field, NIRS has been used for predicting

mercurial species in the membrane constituents of

living bacterial [18] cells, and i-As in crustaceans of

commercial interest [19]. So far, however, no reports

have been published on the use of NIRS for predicting

arsenic species in rice.

The objectives of this study are (i) to test the poten-

tial of NIRS for predicting the i-As content in rice

samples, and (ii) to provide a mechanism for explaining

why NIRS is capable of predicting i-As in this species.

Experimental

Samples

Samples of commercial rice were selected from different markets in

Valencia (Spain) according to the type of rice (brown or milled,

long, medium or short grain). This criterion was based on the fact

that rice is usually marketed regardless of geographic origin or

specific cultivar type. In addition, previous studies demonstrated

that differences in arsenic concentration are not anticipated to be

distinctive enough to establish geographic origin, rice variety, or

other source attributes occurring under normal growing circum-

stances [20]. Rice samples were ground and freeze-dried before

determination of the i-As content by the reference method and by

NIRS analysis.

Determination of Inorganic Arsenic

The methodology applied was developed by Mu~nnoz et al. [21].

Deionized water (4.1 mL) and concentrated HCl (18.4 mL) were

added to 0.5 g of freeze-dried sample. The mixture was left over-

night. After reduction by HBr and hydrazine sulfate, the inorganic

arsenic was extracted into chloroform, and back-extracted into

1 mol L�1 HCl. The back-extraction phase was dry-ashed and i-As

quantified by flow injection (FI) hydride generation (HG) atomic

absorption spectrometry (AAS) (FI-HG Perkin Elmer FIAS-400;

AAS Perkin Elmer Model 3300). The analytical characteristics of

the method were as follows: detection limit¼ 0.013mg g�1 dry

weight (dw); precision¼ 3–5%; recovery As(III) 99% and As(V)

96%.

NIRS Equipment and Software

Near infrared spectra were recorded on an NIRS spectrometer model

6500 (Foss-NIRSystems, Inc., Silver Spring, MD, USA) in reflec-

tance mode equipped with a transport module. The monochromator

6500 consists of a tungsten bulb and a rapid scanning holographic

grating with detectors positioned for transmission or reflectance

measurements. To produce a reflectance spectrum, a ceramic stan-

dard is placed in the radiant beam, and the diffusely reflected energy

is measured at each wavelength. The actual absorbance of the ceram-

ic is very consistent across wavelengths. In this study, each spec-

trum was recorded once from each sample, and obtained as an

average of 32 scans of the sample, plus 16 scans of the standard

ceramic before and after scanning the sample. The ceramic and the

sample spectra are used to generate the final Log (1=R) spectrum.

The total time of analysis was about 2 min. Mathematical transfor-

mations of the spectra and regressions performed on the spectral and

laboratory data were obtained by using the GLOBAL v. 1.50 pro-

gram (WINISI II, Infrasoft International, LLC, Port Matilda, PA,

USA).

NIRS Procedure: Recording Spectra and Processing Data

Ground samples of rice were placed in the NIRS sample

holder (3 cm diameter) until it was full (weightffi 3.50 g) and

232 R. Font et al.

then scanned. Their NIR spectra were obtained at 2 nm intervals

over a wavelength range of 400 to 2500 nm (visible plus near

infrared regions).

Samples of rice were recorded as an NIR file, and were checked

for spectral outliers [spectra with a standardized distance from the

mean (H)>3 (Mahalanobis distance)], using principal component

analysis (PCA). The objective of this procedure was to detect and, if

necessary, remove possible samples whose spectra differed from the

other spectra in the set [22].

In the second step, laboratory reference values for i-As, as

obtained from the reference method, were added to the NIR spectra

file. Calibration equations were computed in the new file by

using the raw optical data (log 1=R, where R is reflectance), or first

or second derivatives of the log 1=R data, with several combinations

of segment (smoothing) and derivative (gap) sizes. The use of

derivative spectra instead of the raw optical data to perform cali-

bration is a way of solving problems associated with overlapping

peaks and baseline correction [23]. A first-order derivative of

log (1=R) results in a curve featuring peaks and valleys that corre-

spond to the point of inflection on either side of the log (1=R)

peak, while the second-order derivative calculation results in a

spectral pattern display of absorption peaks pointing down rather

than up, with an apparent band resolution taking place [24]. In

addition, the gap size and amount of smoothing used to enable

the transformation will affect the number of apparent absorption

peaks.

To correlate the spectral information (raw optical data or derived

spectra) of the samples and the i-As content determined by the

reference method, modified partial least squares (MPLS) was used

as regression method, using wavelengths between 400 and 2500 nm

every 8 nm. Standard normal variate and De-trending (SNV-DT)

transformations [25] were used to correct the baseline offset due

to scattering effects (differences in particle size and path length

variation among samples).

Cross-Validation

Cross-validation is an internal validation method that, like the

external validation approach, seeks to validate the calibration

model based on independent test data, but it does not waste data

on testing only, as is the case with external validation. This pro-

cedure is useful because all available chemical analyses for all

individual species can be used to determine the calibration model

without the need to maintain separate validation and calibration

sets. The method is carried out by splitting the calibration set

into M segments and then calibrating M times, each time testing

about a (1=M) part of the calibration set [26]. In this study, the

different calibration equations were validated with 7 cross-validation

segments, as this was the optimum number of groups automatically

selected by the software as a function of the number of samples

employed.

The prediction ability of the equations obtained was determined

on the basis of their coefficient of determination in the cross-valida-

tion (r2) [27] (Eq. (1)) and standard deviation (SD) to standard error

of cross-validation (SECV) ratio (RPD) [28] (Eq. (2)).

r2 ¼�Xn

i¼1

ðyy� �yyÞ2

��Xni¼1

ðyi � �yyÞ2

��1

ð1Þ

where yy¼NIR measured value; �yy¼mean ‘‘y’’ value for all samples;

yi ¼ lab reference value for the ith sample.

RPD ¼ SD

���Xni¼1

ðyi � yyiÞ2

�ðN � K � 1Þ�1

�1=2��1

ð2Þ

where yi ¼ lab reference value for the ith sample; yy¼NIR measured

value; N¼ number of samples; K¼ number of wavelengths used in

an equation; SD¼ standard deviation.

The statistics shown in Eqs. (1) and (2) give a more realistic

estimate of the applicability of NIRS to the analysis than those of

the external validation, as cross-validation avoids the bias produced

when a low number of samples representing the full range are

selected as validation set [27, 28]. The SECV method is based on

an iterative algorithm which selects samples from a sample set

population to develop the calibration equation and then predicts

based on the remaining unselected samples. This statistic indicates

an estimate of the standard error of prediction (SEP) that may have

been found in an external validation [29], and as occurred with SEP

is calculated as the square root of the mean square of the residuals

for N-1 degrees of freedom, where the residual equals the actual

minus the predicted value.

In this study, cross-validation was computed based on the cali-

bration set for determining the optimum number of terms to be used

in building the calibration equations.

Results and Discussion

Population Boundaries and Identification

of Spectral Outliers for Rice Samples

Population boundaries for spectra of rice samples

were determined by PCA performed over the entire

population (Fig. 1). By using twelve PCs, calcula-

ting on the basis of the second derivative (2, 5,

5, 2; SNVþDT) of the raw spectra, 98.54% of

the entire spectral variability in the data was

explained. The global H (GH) of the sample popula-

tion extended from 0.25 to 2.12 with a mean distance

of 0.96.

One sample was shown to be a GH outlier in PCA.

After careful examination of the commercial descrip-

tion of the product, we decided to eliminate it from

the calibration set as the product composition was

doubtful.

Fig. 1. First two principal component plots (PC1 vs. PC2) for rice

samples (n¼ 40) used in this study

Screening Inorganic Arsenic in Rice by Visible and NIRS 233

Inorganic Arsenic Contents

in the Rice Samples

Samples of rice used to conduct these experiments

showed a mean content and a SD of 110.37 and

49.80 ng g�1 dry weight (dw), respectively (Table 1).

The range of i-As found in the samples extended from

13.0 to 268.0 ng g�1 dw, these values being similar to

those previously reported for white rice from the

United States of America [6]. Inorganic arsenic con-

tents were distributed normally in the occurrence

range (Fig. 2).

Spectral Data Pre-Treatments and Equation

Performances

The application of the second derivative and

SNVþDT algorithms to the raw spectra (Log 1=R)

(Fig. 3) resulted in substantial correction (Fig. 4) of

the baseline shift caused by differences in particle size

and path length variation. Peaks and troughs in Fig. 4

correspond to the points of maximum curvature in the

raw spectrum, and it has a trough corresponding to

each peak in the original. The increase in complexity

of the derivative spectra resulted in a clear separation

of peaks which overlap in the raw spectra.

The use of the second derivative transformation (2,

5, 5, 2; SNVþDT) of the raw optical data performed

over the entire segment (400–2500 nm) yielded a

higher prediction ability equation in cross-validation

than any other of the various mathematical treatments

used. MPLS regression resulted in an equation that

presented four terms and showed a low standard error

of calibration (SEC¼ 20.19 ng g�1 dw) and high coef-

ficient of determination in the calibration (R2¼ 0.80)

(Table 1). In cross-validation the selected equation

showed an r2 of 0.65 (meaning that 65% of the che-

mical variability in the data was explained), which

was indicative of equations useful for correct separa-

tion of samples with low, medium and high contents

[27] (Fig. 5). In accordance with the RPD value (1.67)Fig. 2. Frequency distribution of inorganic arsenic (ng g�1 dry

weight) in the rice samples used in this study (n¼ 40)

Table 1. Calibration and cross-validation statistics (ng g�1, dry

weight) for inorganic arsenic for the selected equations (2, 5, 5,

2; SNVþDT), performed in the range of 400 to 2500 nm

Calibration Cross-

validation

n Range Mean SD SEC R2 RPD r2

40 13.0–268.0 110.37 49.80 20.19 0.80 1.67 0.65

n Number of samples in the calibration file; range minimum and

maximum reference values in the calibration file; SD standard

deviation of the calibration file; SEC standard error of calibration;

R2 coefficient of determination in the calibration; RPD standard

deviation to standard error of cross-validation ratio; r2 coefficient of

determination in the cross-validation

Fig. 3. Raw spectra (Log 1=R) of the rice samples (n¼ 40), in the

range of 400 to 2500 nm

Fig. 4. Second derivative spectra (2, 5, 5, 2; SNVþDT) of the raw

optical data of rice samples in the range of 400 to 2500 nm

234 R. Font et al.

shown by the highest prediction ability equation

obtained, and considering the limits for RPD recom-

mended by Chang and Lairdet [30], and Dunn et al.

[31], this equation was acceptable for i-As prediction

in rice.

The use of the coefficient of determination in the

evaluation of an NIR equation involving trace ele-

ments and mineral species has received some criticism

[15, 32]. In addition, the interpretation of the value of

the coefficient of determination as it was first reported

by Shenk and Westerhaus [27] for agricultural pro-

ducts probably needs to be revised for element analy-

sis. On the other hand, while much effort has been

devoted to the development of calibration of quality

components in the agro-food field, no critical levels of

the RPD statistic have been set for trace elements or

mineral species in these products. Therefore, the stud-

ies reported on mineral composition of soils [30, 31]

have special relevance at the time of establishing sui-

table limits of RPD.

But in spite of the above considerations, authors

currently researching NIRS for environmental analy-

sis and food safety still base their decisions on these

statistics for rapid field and laboratory measurements

[19, 33, 34] to relate the chemistry and apparent

absorption of NIR spectra.

Brown and Milled Rice Reflectance Spectra

Average second derivative (2, 5, 5, 2; SNVþDT)

spectra of those samples that were clearly identified

as brown (n¼ 16) and milled rice (n¼ 14) were

obtained. As shown in Fig. 6, milled rice exhibits

higher absorption than brown rice at wavelengths

914 and 984 nm, which have been assigned to C–H

stretching of third overtone of CH2 groups and O–H

stretching of second overtone of starch [35], respec-

tively. The relative higher starch content of milled

rice (78%) as compared to that of brown rice (66%)

[36] as a consequence of removing the bran and em-

bryo fractions in the abrasive milling, explain these

apparent differences in absorption between both

spectra.

The same phenomenon, but of inverse sign, can be

observed at wavelengths 1778 and 2348 nm, related to

C–H stretching of first overtone of cellulose, and CH2

symmetric stretching plus ¼CH2 deformation [35, 37]

groups of oil and fibre (Fig. 6). Most non-starch

constituents are removed during milling, with fibre

showing the most dramatic drop, followed by other

nutrients except protein [36]. Results reported on the

distribution of nutrients in brown rice support the idea

that only 27% of the total cellulose, 21% of the lignin

and about 20% of the non-starch lipids (ether-soluble)

Fig. 5. Cross-validation scatter plot of laboratory vs. predicted

values by NIRS for inorganic arsenic in rice samples (n¼ 40)

(ng g�1 dry weight)

Fig. 6. Second derivative spectra (2, 5, 5, 2;

SNVþDT) of (a) brown and (b) milled rice

samples

Screening Inorganic Arsenic in Rice by Visible and NIRS 235

remain in milled rice [38], the remaining percentage

having been lost in the course of milling.

The visible segment of the spectrum similarly

showed absorption bands that differed in intensity

for brown and milled rice. The fact that pigments in

coloured rice are located in the pericarp or the seed

coat, which are removed during milling, explains

these differences shown by the spectra (Fig. 6). The

conspicuous band at 668 nm is displayed by both

types of rice, but with a slightly higher intensity in

brown than in milled rice. This band, which has been

previously related to some bran component [39], is

difficult to explain in this case as a result only of

the outer layers of the grain because of its ubiquity

in the different types of rice.

Correlation Plot of i-As vs. Wavelength

The correlation plot for i-As vs. wavelength absor-

bance for the standardised (SNVþDT) optical data

in displayed in Fig. 7. The most relevant features

shown by the correlation plot were the negative cor-

relation between i-As and absorption existing in those

wavelengths which have been assigned to starch

(around 984 nm, and also from 2200 to 2254 nm)

and protein (2052 nm) [35, 37].

Previous studies reporting the element distribution

in rice demonstrated a higher concentration in brown

rice than in milled rice [36]. A considerable portion

of the rice caryopsis ash is accounted for by phos-

phorus. Thus, milling results in the loss of different

essential elements. Although several studies have

been published concerning the element distribution

in milling fractions of rice [40], the data available

on i-As concentrations mainly refers to milled

rice [6].

However, because of the similarity between the bio-

chemistry of As(V) and that of phosphorus [41, 42], it

is logical to think that caryopsis also accounts for

most i-As in the grain. This fact would explain the

negative correlation with starch shown by i-As, i.e.,

milled rice having a lower concentration of i-As and a

relatively higher percentage of starch, and the oppo-

site applying to brown rice.

The relatively high negative correlation of i-As with

those wavelengths related to protein absorption are

more difficult to explain. The low difference in protein

concentration between brown (7.1–8.3%) and milled

rice (6.3–7.1%) [36] does not justify the phenomenon.

It is likely that multiple factors controlling the final

protein content in rice, or the particular geographic

location and farm management activities [6] affect

both protein and i-As contents.

Positive correlations were found between i-As

and absorption in wavelength regions related to fibre

and oil (1722 and 2310 nm) and also pigments (from

472 to 506 nm), which can be explained by the main

location of these components in the outer layers of

the grain, where higher concentrations of i-As are

supposed were found.

Modified Partial Least Square Loadings

MPLS regression reduces the spectral information of

the samples by creating a much smaller number of

new orthogonal variables (factors), which are combi-

nations of the original data, and which retain the

essential information needed to predict the composi-

tion. The role played by the NIR absorbers (organic

and inorganic molecules) present in the samples in

modelling the calibration equations for i-As can be

interpreted by studying the bands of the MPLS factors

Fig. 7. Correlation plot for inorganic arsenic

reference values vs. wavelength absorbance by

using SNVþDT algorithms, in the range of

400 to 2498 nm (rice samples; n¼ 40)

236 R. Font et al.

Fig. 8. MPLS loading spectra for inorganic arsenic in rice samples in the second derivative (2, 5, 5, 2; SNVþDT) transformation. From

top to bottom, panels represent loadings for factors 1, 2 and 3, respectively

Screening Inorganic Arsenic in Rice by Visible and NIRS 237

(loading plots). These loading plots show the regres-

sion coefficients of each wavelength related to the

element (i-As) being calibrated, for each factor of

the equation. The wavelengths represented in the

loading plots as participating more highly in the

development of each factor are those that have greater

spectral variation and better correlation with the ele-

ment in the calibration set.

It has been stated that the success of estimation via

NIRS of specific mineral elements in some grasses

and legumes is usually dependent on the occurrence

of those elements in either organic or hydrated mole-

cules [15]. At the very low concentrations in which

i-As is found in the rice samples used in this study

(mean¼ 110.37 ng g�1 dw), prediction of this element

has to be done on the basis of secondary correlations

with plant components [24, 34]. This phenomenon is

supported by data from MPLS loadings (Fig. 8) in this

study for the selected equation for i-As. It can be

concluded from Fig. 8 that the C–H (912 nm) and also

the O–H (984 nm) groups of starch strongly influ-

enced the first three MPLS loadings for this element.

In addition, the C–H groups of oil and fibre (2308 and

2348 nm) also participated in modelling mainly the

first term of the equation.

In the visible region of the spectrum, chromophores

located in the caryopsis (absorption at 672 nm and

shorter wavelengths) also participated actively in con-

structing the first terms. In spite of the low r value

shown by the band at 912 nm (Fig. 7), this band was

selected to greatly participate in the first three terms of

the equation for i-As, due to the high variability in

absorbance it displays (Fig. 4).

The prediction results obtained from cross-validation

showed for the first time that NIRS can be employed

for speciation purposes in rice, and that this technique

is able to predict the i-As concentration in samples of

this species with sufficient accuracy for screening pur-

poses despite the low i-As levels shown in this study.

Thus, NIRS can be used for identifying samples with

low, medium and high i-As contents. In the second

step, the exact value of i-As in the samples selected

by the researcher as being of interest can be obtained

by the reference method. NIRS can therefore decrease

the number of analyses in the laboratory needed for

monitoring the i-As content in screening programs.

Acknowledgements. This research work was supported by the Min-

isterio de Ciencia y Tecnologıa, Project AGL 2001-1789, for which

the authors are very grateful.

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