Screening Inorganic Arsenic in Rice by Visible and Near-Infrared Spectroscopy

Rafael Font,1* Dinoraz Vélez,2 Mercedes Del Río-Celestino,1 Antonio De Haro-Bailón,1

Rosa Montoro2

1Instituto de Agricultura Sostenible (CSIC). Alameda del Obispo s/n. 14080, Córdoba,

Spain.

2Instituto de Agroquímica y Tecnología de Alimentos (CSIC), Apartado 73, 46100,

Burjassot (Valencia), Spain.

*Corresponding author: telephone (+34) 957499211; fax (+34) 957499252; e-mail:

font@cica.es.

/tt/file_convert/577cce5a1a28ab9e788dd64e/document.doc

1

Abstract

The potential of near infrared spectroscopy (NIRS) for screening the inorganic arsenic

(i-As) content 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

digestion-solvent extraction followed by hydride generation atomic absorption

spectrometry, and were regressed against different spectral transformations by modified

partial least square (MPLS) regression. Second derivative transformation equation of

the raw optical data, previously standardized by applying standard normal variate

(SNV) and De-trending (DT) algorithms, resulted in a coefficient of determination in

the cross-validation (1-VR) of 0.65, indicative of equations useful for a correct

separation of the samples in low, medium and high groups. The standard deviation

(S.D.) to standard error of cross-validation (SECV) ratio, shown by the second

derivative equation, was similar to those obtained for other trace metal calibrations

reported in NIRS reflectance. Spectral information related to starch, lipids and fiber of

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

2

Introduction

Rice is the dominant staple food crop in developing countries, particularly for the humid

tropics across the globe [1]. Almost 96% of rice is produced and consumed in in

developing countries [1] and contributing over 70% of energy to the daily intake [2].

The component of protein in rice, at 7 – 9% by weight, is relatively low [3], but it is a

major source of protein (50%) for these rice-consuming people [2].

For a food with such a high consumption it is crucial to have information about toxic

trace levels, in order to establish the potential effects on human health. 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 work. Total diet studies indicate that As

concentrations in rice are higher than those in other products of vegetable origin [4,5].

Natural processes and human activities are the two principal factors responsible for the

introduction of arsenic into the rice-growing environment. Natural processes of

introduction involve soil/water chemistry and climate. Farm management activities such

as fertilization practices, crop rotation and herbicidal/insecticidal uses also act to

introduce arsenic. For inorganic arsenic, soil properties and uses of pesticides are

assumed to be the most important interactive influences that determine 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 0.76 µg/g [7-9]. In non-arsenic-endemic areas, the highest value reported is 0.776

µg/g [2,10,11]. The very few studies on i-As contents in rice show concentrations that

vary between 0.021 and 0.560 µg/g [5,6,7,10,12]. Evaluating the contribution of rice to

i-As intake is, in our view, a necessary task in order to make a more realistic assessment

3

of the risk of exposure to this toxin, especially in arsenic-endemic areas and developing

countries.

The standard methodologies for trace metal determination 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 hazardous chemicals. In contrast, Near Infrared

Spectroscopy (NIRS) is a valuable technique that offers speed and low cost of analysis,

and also the sample is analyzed without using chemicals. The spectral information can

be used for simultaneous prediction of numerous constituents and parameters of 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 analysis of metal content 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 concerning

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 work were: (i) to test the potential of NIRS for predicting the i-As

content in rice samples, and (ii) to provide a mechanism to explain why NIRS is capable

of predicting i-As in this species.

4

Experimental

Samples. Samples of commercial rice were selected at different markets in Valencia

(Spain) on the basis of the type of rice (brown or milled, long or medium grain). This

criterium was based on the fact that rice is marketed usually without regard to

geographic origin and specific cultivar type. In addition, previous studies demonstrated

that any differences in concentrations of arsenic are not anticipated to be distinctive

enough to establish geographic origin, rice variety, or other source attributes produced

under normal growing circumstances [20]. Rice samples were ground and freeze-dried

before determination of the i-As content by the reference method, and NIRS analysis.

Determination of inorganic arsenic. The methodology applied was developed

previously by Muñoz 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 overnight. After

reduction by HBr and hydrazine sulfate, the inorganic arsenic was extracted into

chloroform, and back-extracted into 1 mol/L HCl. The back-extraction phase was dry-

ashed and the i-As was quantified by flow injection-hydride generation atomic

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

3300). The analytical characteristics of the method were: detection limit = 0.013 μg/g

dry weight (dw); precision = 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

reflectance 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 standard is placed in the radiant beam, and the diffusely reflected energy is

measured at each wavelength. The actual absorbance of the ceramic is very consistent

5

across wavelengths. In this work, each spectrum was recorded once from each sample,

and was obtained as an average of 32 scans over the sample, plus 16 scans over 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 whole time of analysis

took about 2 min., approximately. Mathematical transformations of the spectra and

regressions performed on the spectral and laboratory data were obtained by using the

GLOBAL v. 1.50 program (WINISI II, Infrasoft International, LLC, Port Matilda, PA,

USA).

NIRS procedure: recording of spectra and processing of data. Freeze-dried, ground

samples of rice were placed in the NIRS sample holder (3 cm diameter) until it was full

(weight 3.50 g), and were then scanned. Their NIR spectra were acquired at 2 nm

intervals over a wavelength range from 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), by

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 calibration is a way of solving problems associated with

overlapping peaks and baseline correction [23]. A first-order derivative of log (1/R)

6

results in a curve containing peaks and valleys that correspond 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 make 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 from 400 to 2500 nm every

8 nm. Standard normal variate and De-trending (SNV-DT) transformations [25] were

used to correct 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 on independent test data, but

it does not waste data for testing only, as occurs in external validation. This procedure is

useful because all available chemical analyses for all individuals 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 work, 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-validation (r2) [27] (eq. 1) and standard

deviation (S.D.) to standard error of cross-validation (SECV) ratio (RPD) [28] (eq. 2).

7

r2= eq. 1

where: = NIR measured value; = mean “y” value for all samples; = lab reference

value for the ith sample.

RPD = eq. 2

where: = lab reference value for the ith sample; = NIR measured value; N=

number of samples, K= number of wavelengths used in an equation; S.D.= standard

deviation.

The statistics shown in eq. 1 and eq. 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 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 work, cross-validation was computed on the calibration set for determining the

optimum number of terms to be used in building the calibration equations.

Results and Discussion

8

Population boundaries and identification of spectral outliers for rice samples.

Population boundaries for spectra of samples of rice were determined by PCA

performed over the entire population (Figure 1). By using twelve PCs, calculated on the

second derivative (2, 5, 5, 2; SNV+DT) of the raw spectra, the 98.54 % of the whole

spectral variability in the data was explained. The global H (GH) of the sample

population 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 carefully examination of the

commercial description of the product, it was decided to eliminate it from the

calibration set as product composition was in doubt.

Inorganic arsenic contents in the rice samples. Samples of rice used to conduct this

work showed mean content and S.D. of 110.37 and 49.80 ng/g dw, respectively (Table

1). The range of i-As found in the samples extended form 13.0 to 268.0 ng/g dw, these

values being similar to those contents previously reported in white rice from the United

States of America [6]. Inorganic arsenic contents were normally distributed in the

occurrence range (Figure 2).

Spectral data pre-treatments and equation performances. The application of the

second derivative and SNV+DT algorithms to the raw spectra (Log 1/R) (Figure 3),

resulted in substantial correction (Figure 4) of the baseline shift caused by differences in

particle size and path length variation. Peaks and troughs in Figure 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 the complexity of the derivative spectra

resulted in a clear separation between peaks which overlap in the raw spectra.

The use of the second derivative transformation (2, 5, 5, 2; SNV+DT) 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

9

treatments used. MPLS regression resulted in an equation that presented four terms and

showed a low standard error of calibration (SEC = 20.19 ng/g dw) and high coefficient

of determination in the calibration (R2 = 0.80) (Table 1). In cross-validation the selected

equation showed an r2 of 0.65 (meaning that the 65% of the chemical variability in the

data was explained), which was indicative of equations useful for a correct separation of

samples with low, medium and high contents [27] (Figure 5). In accordance with the

RPD value (1.67) shown by the highest prediction ability equation obtained, and

considering the limits for RPD recommended by Chang et al. [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 elements 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 products, probably needs to be

revised for element analysis. On the other hand, while much effort has been applied 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 and mineral species in these

products. By this reason, those studies reported on mineral composition of soils [30,31]

show a special relevance at the time of establishing suitable limits of RPD.

But in spite of the above considerations, authors currently researching NIRS for

environmental analysis and food safety still base their decisions on these statistics for

rapid field and laboratory measurements [19,33,34], to relate chemistry and apparent

absorption of NIR spectra.

Brown and milled rice reflectance spectra. The average second derivative (2, 5, 5, 2;

SNV+DT) spectra of those samples that were clearly identified as brown (n= 16) and

milled rice (n= 14) were obtained. As it can be observed in Figure 6, milled rice showed

10

higher absorptions than brown rice at wavelengths 914 and 984 nm, which have been

assigned to C-H stretching third overtone of CH2 groups and O-H stretching second

overtone of starch [35], respectively. The relative higher starch content of milled rice

(78%) in comparison to that of brown rice (66%) [36], as a consequence of removing

the bran and embryo fractions in the abrasive milling, explain these differences in

apparent 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 first overtone of cellulose, and CH2 symmetric

stretching plus =CH2 deformation [35,37] groups of oil and fiber (Figure 6). Most non-

starch constituents are removed during milling, with fiber showing the most dramatic

drop, followed by other nutrients except protein [36]. Results reported on distribution of

nutrients in brown rice, supports the idea that only a 27% of the total cellulose, a 21% of

the lignin and about a 20% of the non-starch lipids (ether-soluble) are present in the

milled rice [38], being the rest of them removed during milling.

The visible segment of the spectrum similarly showed absorption bands that differed in

intensity between brown and milled rice. The fact that pigments in coloured rices are

located in the pericarp or the seed coat, which are removed during milling, explains

these differences shown by spectra (Figure 6). The conspicuous band at 668 nm is

displayed by both types of rice, but with little higher intensity in brown than in milled

rice. This band, which has been related previously to some bran component [39] is

difficult to explain here as being caused only by the outer layers of the grain because its

ubiquity in the different types of rice.

Correlation plot of i-As vs wavelength. The correlation plot for i-As vs wavelength

absorbance for the standardised (SNV+DT) optical data in displayed in Figure 7. Most

relevant features shown by the correlation plot were the negative correlation between i-

11

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 element distribution in rice demonstrated a higher i-As

concentration in the brown rice than in milled rice [36]. A considerable portion of the

rice caryopsis ash is accounted for by phosphorus. Thus, milling results in loss of

different essential elements. Although several studies have been reported concerning the

element distribution in the milling fractions of rice [40], data available on arsenic

concentration in this species are mainly referred to milled rice [6].

However, because the similar biochemistry of arsenic with that of phosphorus [41,42], it

is logical to think that caryopsis account also for most arsenic in the grain. This fact

would explain by itself the negative correlation with starch shown by i-As, i.e., milled

rice has a lower concentration of i-As and a relative higher percentage of starch, and the

opposite for brown rice.

More difficult is to explain the relative high negative correlation of i-As with those

wavelengths related to protein absorption. The low difference in protein concentration

between brown (7.1-8.3 %) and milled rice (6.3-7.1%) [36] does not justify this fact. It

is probably that the multiple factors controlling the final protein content in rice, or

geographic location and farm management activities [6] relate both, protein and i-As

contents.

Positive correlations were found between i-As and absorption in wavelengths regions

related to fiber 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 to be found.

Modified partial least square loadings. MPLS regression reduces the spectral

information of the samples by creating a much smaller number of new orthogonal

12

variables (factors), which are combinations of the original data, and which retain the

essential information needed to predict the composition. 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 (loading plots). These loading plots show the regression 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 element 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 molecules [15]. At the very low concentrations in which i-

As is found in the rice samples used in this work (mean= 110 ng/g dw), any 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

(Figure 8) in this work for the selected equation for i-As. It can be concluded from

Figure 8, that C-H (912 nm) and also O-H (984 nm) groups of starch highly influenced

the first three MPLS loadings for this element. In addition, C-H groups of oil and fiber

(2308 and 2348 nm) also participated to model, mainly, the first term of the equation.

In the visible region of the spectrum, cromophores located in the caryopsis (absorption

at 672 nm and shorter wavelengths) also participated actively in constructing the first

terms. In spite of the low r value shown by the band at 912 nm (Figure 7), this band was

selected to highly participate in the first three terms of the equation for i-As, due to the

high variability in absorbance displayed by it (Figure 4).

13

Prediction results obtained from cross-validation showed for the first time that NIRS

can be employed with 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 purposes in spite of the low i-As levels shown in this work. Thus, NIRS can

be used for identifying those samples having low, medium and high i-As contents. In

the second step, the exact value of i-As of 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.

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19

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20

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

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

2; SNV+DT), performed on the range from 400 to 2500 nm.

Calibration Cross-

validation

n range mean S.D. 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; S.D.=

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.

21

Figure Captions

Figure 1. First two principal component plot (PC1 vs PC2) for rice samples (n= 40)

used in this work.

Figure 2. Frequency distribution of inorganic arsenic (ng/g dw) in the samples used in

the study (n= 40).

Figure 3. Raw spectra (Log 1/R) of the rice samples used in this work (n= 40), in the

range from 400 to 2500 nm.

Figure 4. Second derivative spectra (2, 5, 5, 2; SNV+DT) of the raw optical data in the

range from 400 to 2500 nm.

Figure 5. Cross-validation scatter plot of laboratory vs. predicted values by NIRS for

inorganic arsenic in rice samples (n= 40) (ng/g dw).

Figure 6. Second derivative (2, 5, 5, 2; SNV+DT) spectra of a) brown and b) milled

rice samples used in this study.

Figure 7. Correlation plot for inorganic arsenic reference values vs. wavelength

absorbance by using SNV+DT algorithms, in the range from 400 to 2498 nm (n= 40).

Figure 8. MPLS loading spectra for inorganic arsenic in the second derivative (2, 5, 5,

2; SNV+DT) transformation. From the top to bottom, panels represent loadings for

factors 1, 2 and 3, respectively.

22

Figure 1.

23

Figure 2.

24

Figure 3.

25

Figure 4.

26

Figure 5.

0 50 100 150 200 250 300 350

50

100

150

200

250

inor

gani

c ar

seni

c (p

redi

cted

)

inorganic arsenic (laboratory)

27

Figure 6.

28

Figure 7.

29

Figure 8.

30