Post on 28-Dec-2015
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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