NIRS CRAYFISH (Revised Ac035377c) Font Et Al (2)
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Transcript of NIRS CRAYFISH (Revised Ac035377c) Font Et Al (2)
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TITLE AND AUTHORSHIP
VISIBLE AND NEAR INFRARED SPECTROSCOPY AS A TECHNIQUE FOR
SCREENING THE INORGANIC ARSENIC CONTENT IN THE RED CRAYFISH
(Procambarus clarkii Girard)
Rafael Font,1* Mercedes Del Río-Celestino,1 Dinoraz Vélez,2 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:
2
ABSTRACT
The potential of near infrared spectroscopy (NIRS) for screening the inorganic arsenic (i-As)
content in the red crayfish (Procambarus clarkii Girad 1852) was assessed. Sixty-two
samples belonging to this species 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 equations 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 0.84, indicative of equations of good
quantitative information. The standard error of cross-validation (SECV) to standard deviation
(SD) 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
chromophores and lipids of the red crayfish tissues, and also the plant matter contained in
their stomachs, were the main organic components used by MPLS for modeling the selected
prediction equation. This pioneering use of NIRS to predict the i-As content in red crayfish
represents an important saving in time and cost of analysis.
3
INTRODUCTION
Among the metals and metalloids present in the environment, arsenic stands out because of its
toxicological potential. As is known, total arsenic (t-As) can be found in food in various
chemical forms that differ in their degree of toxicity and pathologies associated with them.
The most toxic forms are the inorganic ones, As(III) and As(V), and the sum of both forms,
denoted inorganic arsenic (i-As), is considered a human carcinogen.1 i-As contents in some
foods are subject to regulation in a small number of countries: fish and fish products in
Australia and New Zealand; seaweed in Australia, New Zealand, and France.2,3 Most existing
legislation, however, still bases its limits on the total As content, an ineffective criterion from
the viewpoint of food safety. The availability of fast methodologies to quantify i-As levels in
different kinds of foods would contribute to the drawing up of legislation to guarantee the
healthiness of foods with respect to this metalloid.
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. NIRS combines applied spectroscopy and statistics. 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.4 After calibration,
the regression equation permits accurate analysis of many other samples by prediction of
results on the basis of the spectra. NIRS can be used to analyze some specific elements
(indirectly, e.g. N as protein), well-defined compounds (e.g. starch), or more complex, poorly
defined attributes of substances (e.g. fiber, animal food intake).5
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
4
the analysis of heavy metals in lake sediments,6,7 studies concerning the chemical
characterization of soils8,9,10 and the determination of heavy metals and arsenic by NIRS in
plant tissues.11,12,13 Recently, in the agro-food field the feasibility of this technique for
measuring K, Na, Mg, and Ca in white wines14 was demonstrated. In the speciation field,
NIRS has been used for predicting mercurial species in the membrane constituents of living
bacterial cells.15 So far, however, no reports have been published on the use of NIRS for
predicting arsenic species.
The red crayfish (Procambarus clarkii Girard), an exotic species of crustacean from
Louisiana (USA), was introduced in the wetlands of the Bajo Guadalquivir, Seville (Spain), in
1974. Since then, this species has proliferated rapidly, leading to notable alterations in the
aquatic ecosystems. At the same time, however, it has become a socio-economic resource of
great importance in Doñana and the surrounding area. Commercial exploitation of this
crustacean in the region has generated a food industry that sells live or processed red crayfish
in Spain, other European countries, and the USA.16 As the crayfish can become a vector of
metal contamination to higher levels of the food chain, including humans,17 the present work
proposes large-scale monitoring of this species, using the NIRS technique. The objectives of
this work were: (i) to test the potential of NIRS for predicting the i-As content in red crayfish,
and (ii) to provide a mechanism to explain why NIRS is capable of predicting i-As in
crayfish.
EXPERIMENTAL SECTION
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
5
absorbance of the ceramic is very consistent 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. Performance of the instrument is checked
by measuring of photometric repeatability and wavelength accuracy.
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).
Collection and preparation of samples. Sixty-two samples of crayfish were collected during
various periods in the year 2000 (February, May, and June) from sampling stations situated in
different aquatic ecosystems, some of them polluted and others not affected by contamination.
The specimens of crayfish were caught by using Dutch traps or pots. The pots were baited to
maximize the catch and checked every 24 h, remaining in place for 1 or 2 days. The crayfish
were washed on site and taken to the laboratory, where they were sexed. The entire organism
was used for the determination of i-As. At some of the sampling points, the sample to be
analyzed consisted of several individual specimens. Each sample was frozen at –20 ºC and
freeze-dried, and then crushed to a fine powder in a mill. The resulting powder was stored at
4ºC until analysis.
Determination of inorganic arsenic. The methodology applied was developed previously by
Muñoz et al.18 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-1
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
6
Perkin Elmer Model 3300). The analytical characteristics of the method were: detection limit
= 0.013 µg g-1 dry weight (dw); precision = 3-5%; recovery As(III) 99% and As(V) 96%.
NIRS procedure: recording of spectra and processing of data. Freeze-dried, ground
samples of crayfish were placed in the NIRS sample holder (3 cm diameter) until it was ¾ 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 red crayfish were recorded as an NIR file, and were checked for spectral outliers
[spectra with a standardized distance from the mean (H) > 3 (Mahalonobis 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.
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.19 A first-order derivative of log (1/R) 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.20 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.
7
Standard normal variate and De-trending (SNV-DT) transformations21 were used to correct
baseline offset due to scattering effects (differences in particle size among samples).
Cross-validation. The performances of the different calibration equations obtained were
determined from cross-validation. Thus, the prediction ability of the equations obtained was
determined on the basis of their coefficient of determination in the cross-validation (1-VR),22
and standard error of cross-validation (SECV) to standard deviation (SD) ratio (SECV/SD).23
Cross-validation is an internal validation method, first described by Stone,24 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.5 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.25
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 and to identify
chemical (T) or spectral (H) outliers. T outliers are samples with a relationship between the
reference value and the value predicted from the spectrum that is different from the same
relationship of other samples in the population, and with large residuals (T values > 2.5). An
H outlier identifies a sample that is spectrally different from other samples in the population
and has a standardized H value > 3.0. The outlier elimination pass was set to allow the
software to remove outliers twice before completing the final calibration.26
RESULTS AND DISCUSSION
Individuals of crayfish whose sex was differentiated at the time of capture and could therefore
be determined consisted of males and females in a ratio of 1:1, approximately. Crayfish
samples consisted of juvenile and mature individuals. i-As contents found in the crayfish
samples (n = 62) used to carry out this work (Table 1) were in the range of the contents
previously reported for this species in the same area.27
Red crayfish reflectance spectrum. Fig. 1 shows the second derivative average spectrum
[(2, 5, 5, 2; SNV+DT) (order of derivative, gap, first smooth, second smooth)] of the samples
of P. clarkii used to carry out this study (n = 62). The (2, 5, 5, 2; SNV+DT) and (2, 5, 5, 2)
average spectra matched all the absorption bands, and no shift of absorption maxima was
observed between them. In assigning wavelength absorbances to specific absorbers care has to
be taken, because theoretically, the second derivative transformation reproduces the log 1/R
space only when no additional transformations such as SNV+ DT are used.
In the visible region (400-700 nm) of the spectrum, light absorption by pigments dominates
the reflectance spectrum, these absorptions being due to electronic transitions taking place in
the photoactive part of the molecule (chromophore). The absorption band displayed at 474 nm
is related to astaxanthin (3,3´-dihydroxy- ββ -carotene-4-4´-dione), as it has been reported to
present typical absorption maxima (λ max) at 472, 476 or 490 nm depending on the solvent
used.28,29 Astaxanthin is the predominant carotenoid in the carapace of most crustacean
species, including P. clarkii,28,30 accounting for 86-98% of the total carotenoids.31 Although
astaxanthin appears as a red pigment, when complexed with proteins it produces a shift of
light absorbance to longer wavelengths (bathochromic shift).32 Thus, the actual color
exhibited by P. clarkii is the result of complexes formed between astaxanthin (prosthetic
group) and proteins located in the carapace. The absorption band at 474 nm shown in Fig. 1
supports the idea that free astaxanthin is present in the freeze-dried tissues of P. clarkii. The
presence of ketoastaxanthin molecules not complexed with proteins could be due to the
instability (not covalent binding) of the astaxanthin-protein complexes during the freeze-
drying treatment of the samples.28,33 The second derivative (2, 5, 5, 2; SNV+DT) spectrum
showed two absorption bands, at 550 and 600 nm (Fig. 1), which could be related to
8
absorptions due to carotenes other than astaxanthin,34 as these have been reported to be
present in red crayfish.30
A weak absorption band at 668 nm and a conspicuous absorption band at 712 nm were
displayed by the P. clarkii second derivative average spectrum (Fig. 1). These bands could be
related to absorptions due to matter of plant origin contained in the digestive tract of the
crayfish individuals, as this was not removed from the carcass before analysis. Thus, plant
phytochromes have been reported to present λ max at 666 nm, and the N-terminal domain
(phycocyanobilin) of the phytochrome has shown a λ max at 714 nm in some freshwater
plants.35 In addition, the latter wavelength has been reported as selected by stepwise
regression for predicting the chlorophyll content of plant material.36
The optical region corresponding to the near infrared spectrum showed absorbance bands
mainly at 1690 (C-H stretch first overtone of CH3 groups),37 1726 nm (C-H stretch first
overtone), 1926 nm (O-H stretch first overtone of residual moisture in the samples), 2054 nm
(N-H stretch of amides), and 2166, 2306, and 2346 nm, related with amide C-O stretch
combination tones or C-H combination tones, respectively.38
Correlation plot. The correlation plot for i-As vs. wavelength for the standardized
(SNV+DT) optical data is displayed in Fig. 2. The sign and value of the correlation at the
wavelengths related to electronic transitions in the blue and green range (450-500 nm)
indicated the existence of an inverse correlation (i.e., r460nm = − 0.58) between the i-As
content of the samples and their reddish appearance (the lower the i-As content in the sample,
the higher the reddish appearance of the sample). On the other hand, in the electromagnetic
region corresponding to electronic transitions in the red region (668 nm), a similar correlation
but of inverse sign (i.e., r668nm = 0.64) was found, indicating a high positive correlation
between the i-As reference values of the samples and the concentration of the chromophores
absorbing at this wavelength (the higher the bluish and greenish appearance, the higher the i-
As content in their tissues). Noteworthy features displayed by the correlation plot in the
9
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region from 1100 to 2500 nm were the positive correlation with i-As content exhibited by
water (r1936nm = 0.62) and protein (r2054nm = 0.48), and the negative correlation shown by the
wavelengths related with C-H combination tones38 (r2308nm = −0.52; r2348nm = −0.53) and C-H
cis unsaturations39 (r2178nm = −0.50). Furthermore, a high negative correlation (r = −0.71) was
found at 1684 nm, an absorption band related to the C-H stretch first overtone of CH3
groups.37
Since i-As enters the organism mainly through the diet, this factor has to be a major variable
influencing correlations between i-As and apparent absorption. Diet directly influences
differences in carapace and muscular color. These differences in apparent visible absorption,
highly correlated with the i-As content, are expected to be dependent on the astaxanthin and
other carotene concentrations in their tissues. Individuals mainly consuming food of plant
origin would increase the i-As content and astaxanthin concentration through the ingestion of
β-carotenes, one of the precursors of astaxanthin.30,31 On the other hand, individuals
consuming detritus with an additional ingestion of the mineral sediment of the river bottom
would increase their i-As uptake with a lower carotene ingestion. Studies performed on red
crayfish populations in the sampling area40,41 demonstrated notable differences in food habits
among individuals. For instance, juveniles of red crayfish consume a greater proportion of
food of animal origin than adults, while mature animals mainly consume plants and organic
sediment. The herbivorous diet may be the cause of the high proportion of i-As found in these
crayfish.27
Furthermore, many crustacean species have been shown to undergo seasonal variations in
biochemical composition and lipids of muscle and carapace,42,43 depending on available food
resources and environmental changes, and also on other factors such as sex and development
stage.44
11
NIRS analysis. Second derivative transformation (2, 5, 5, 2; SNV+DT) of the raw optical
data, performed on the entire range of the spectra (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 a calibration equation that presented 5 terms and showed a
low standard error of calibration (SEC = 0.19 µg g-1 dw) and high coefficient of determination
in the calibration (R2 = 0.93) (Table 1). In cross-validation the selected equation showed a
high coefficient of determination (1 − VR = 0.84; 84% of the chemical variability in the data
was explained in cross-validation), which was indicative of equations with good quantitative
information22 (Fig. 3).
The prediction ability of the NIR calibration equations is determined by many authors
according to the relationship between the error of the analysis (SECV) and the spread in
composition of agricultural products. Thus, if the error in estimation is large compared with
the spread (as SD) in composition, then regression has increasing difficulty in finding stable
calibrations.23,38,45 In accordance with these considerations, the low SECV/SD ratio (0.38)
found for the i-As equation in this work was suitable for screening purposes. This represents a
novel contribution, as arsenic speciation in foods can now be tackled by means of NIRS for
the first time.
Some examples of similar correlations between analyte concentration and apparent absorption
have been reported in relation to determination of total trace elements and macro nutrients in
other matrices. Some authors have also reported SECV/SD data for mineral analysis in plants,
similar to or even lower than those shown in this work. Vázquez de Aldana et al.46 reported a
standard error of prediction to SD ratio in external validation of 0.25, for the prediction of
nitrogen in grassland species. Sauvage et al.14 obtained SECV/SD ratios that ranged from 0.35
to 0.37 for PLS calibrations of Na, K, Mg, and Ca in white wines by using NIR transmission.
Morón and Cozzolino13 developed successful equations predicting macro elements in forage
crops, which presented SECV/SD ratios of 0.27, 0.30, and 0.35 for Ca, N, and K,
12
respectively. However, prediction errors related to micronutrients and trace metals are
sometimes higher than those previously mentioned, depending on the element being
predicted. Vázquez de Aldana et al.46 found standard error of prediction (SEP) in an external
validation to SD ratios of 0.53 and 0.66 for zinc and manganese, respectively, in grasslands.
Font et al.12, reported SECV/SD ratios that ranged from 0.58 (Pb) to 0.74 (Zn) in Brassica
juncea plants grown in polluted soils. Similar ratios were reported for legume forage crops by
Morón and Cozzolino13, who found values that ranged from 0.43 to 0.59 for P, Mg, and S.
Much more diverse were the prediction data reported by Clark et al.47 for various macro and
micro nutrients in 3 forage species. The SEP/SD ratio found by these authors showed values
that ranged from 0.48 (potassium in alfalfa) to 1.40 (iron in alfalfa).
MPLS loadings. To reduce the spectral information of the samples by creating a much
smaller number of new orthogonal variables (factors) which are combinations of the original
data, and which retain the essential information needed to predict the composition, MPLS
regression was employed (Fig. 4). 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.46,47 Although it is
possible that NIR reflectance spectra of P. clarkii contain some information related to the
association of i-As with sulfhydryl groups of proteins,48,49 the low concentrations of i-As
present in the samples used to carry out this work (mean = 1.26 µg g-1 dw) make it difficult to
explain the high correlations obtained in this work only on the basis of such interactions
(Table 1). It can be concluded from Fig. 4 that chromophores existing in the tissues of the
crayfish greatly influenced the first three MPLS loadings of the second derivative
transformation (2, 5, 5, 2; SNV+DT). This is in agreement with the correlations existing
between i-As content and apparent absorption in our samples, in which high correlations were
shown in the visible region of the spectrum (Fig. 2).
13
Of the first three factors of the selected equation (2, 5, 5, 2; SNV+DT), the second MPLS
loading was the most highly correlated with i-As. It is worth noting the influence of the band
at 712 nm in modeling this second factor, which is related (Fig. 1) to the absorption by plant
matter contained in the digestive tract of the crayfish samples. Absorptions due to C-H
combination tones at 2308 and 2348 nm by lipids38 also highly influenced the two first factors
of the equation, and together with the above mentioned wavelengths were the most weighted
in the first three MPLS factors.
Use of NIRS for screening purposes. Australia and New Zealand have set the maximum
permissible content of i-As in fish, crustaceans, and mollusks2 at 1 mg kg-1 wet weight. This
limit represents 3.3 mg kg-1 dry weight, if we assume the mean moisture obtained in the
samples analyzed (70%). With the use of NIRS it would be possible quickly to establish the
samples that lie below this limit. NIRS analysis would also be much more economical than
the AAS reference method.
CONCLUSIONS
Prediction results obtained from cross-validation showed for the first time that NIRS can be
employed with speciation purposes, and that this technique is able to predict the i-As
concentration in freeze-dried samples of red crayfish with sufficient accuracy for screening
purposes. 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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ACKNOWLEDGMENTS
The authors wish to thank Dr. Ian Murray (Scottish Agricultural College, Aberdeen, Scotland)
for the critical review of the manuscript. The authors are also grateful to Dr. J. M. Martínez
(Universidad Autónoma de Madrid, Spain) for providing the crayfish samples used in this
work. This research was supported by the Ministerio de Ciencia y Tecnología, Project AGL
2001-1789, for which the authors are deeply indebted.
20
Table 1. Calibration and cross-validation statistics (µg g-1, dry weight) for inorganic arsenic for the selected equation (2, 5, 5, 2; SNV+DT), performed on the range from 400 to 2500 nm.
Calibration Cross-validation
n range mean SD SEC R2 SECV/SD 1-VR nt
62 0.15-2.82 1.26 0.75 0.19 0.93 0.38 0.84 5
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; SECV/SD= standard error of cross-validation to SD ratio; 1-VR= percentage of variation in the reference values explained by NIRS in the cross-validation; nt= number of terms in the selected equation.
21
FIGURE CAPTIONS Fig. 1. 2nd derivative (2, 5, 5, 2; SNV+DT) average NIR spectrum of freeze-dried and
ground crayfish samples (n= 62) in the range from 414 to 2498 nm.
Fig. 2. Correlation plot for inorganic arsenic reference values vs. wavelength absorbance by
using SNV+DT algorithms, in the range from 400 to 2498 nm (n= 62).
Fig. 3. Cross-validation scatter plot (laboratory vs. predicted by NIRS) for inorganic arsenic (µg g-1
dw) for the equation (2, 5, 5, 2; SNV+DT).
Fig. 4. MPLS loading plots for inorganic arsenic for the equation (2, 5, 5, 2; SNV+DT).