Visible and Near-Infrared Spectroscopy as a Technique for Screening

Anal. Chem. 2004, 76, 3893-3898

Visible and Near-Infrared Spectroscopy as a Technique for Screening the Inorganic Arsenic Content in the Red Crayfish (Procambarus clarkii Girard) Rafael Font,*,† Mercedes Del Rıó-Celestino,† Dinoraz Ve´lez,‡ Antonio De Haro-Bailoń,† and Rosa Montoro‡

Instituto de Agricultura Sostenible (CSIC), Alameda del Obispo s/n, 14080 Co´ rdoba, Spain, and Instituto de Agroquı´mica y Tecnologıá de Alimentos (CSIC), Apartado 73, 46100 Burjassot, Valencia, Spain

The potential of near-infrared spectroscopy (NIRS) for screening the inorganic arsenic (i-As) content in the red crayfish (Procambarus clarkii Girard 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-squares (MPLS) regression. Second derivative transformation equations of the raw optical data, previously standardized by applying standard normal variate and de-trending 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 to standard deviation 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 savings in time and cost of analysis. 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 * Corresponding author. Telephone: (+34) 957499211. Fax: (+34) 957499252. E-mail: [email protected]. † Instituto de Agricultura Sostenible (CSIC). ‡ Instituto de Agroquı´mica y Tecnologıá de Alimentos (CSIC). (1) Tsuda, T.; Babazono, A.; Ogawa, T.; Hamad, H.; Mino, Y.; Aoyama, H.; Kuramatani, N.; Nagira, T.; Hotta, N.; Harada, M.; Inomata, S. Appl. Organomet. Chem. 1995, 6, 309-322. 10.1021/ac035377c CCC: $27.50 Published on Web 05/28/2004

© 2004 American Chemical Society

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 and 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 the analysis of heavy metals in lake sediments,6,7 studies concerning the chemical characterization of soils,8-10 and the determination (2) Australian New Zealand Food Authority (ANZFA). Food Standards Code, Issue 41, 1997. (3) Mabeau, S.; Fleurence, J. Trends Food Sci. Technol. 1993, 4, 103-107. (4) Malley, D. F.; Williams, P. C.; Hauser, B.; Hall, J. In Near Infrared Spectroscopy: The Future Waves; Davies, A. M. C., Williams, P. C., Eds.; NIR Publications: Chichester, 1996; pp 691-699. (5) Foley, W. J.; McIlwe, A.; Lawler, I.; Aragones, L.; Woolnough, A. P.; Berding, N. Oecologia 1998, 116, 293-305. (6) Nilsson, M. B.; Dabakk, E.; Korsman, T.; Renberg, I. Environ. Sci. Technol. 1996, 30, 2586-2592. (7) Malley, D. F.; Williams, P. C. Environ. Sci. Technol. 1997, 31, 3461-3467. (8) Krischenko, V. P.; Samokhvalov, S. G.; Fomina, L. G.; Novikova, G. A. In Making Light Work: Advances in Near Infrared Spectroscopy; Murray, I., Cowe, A., Eds.; VCH: Weinheim, 1992; pp 239-249.

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of heavy metals and arsenic by NIRS in plant tissues.11-13 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 (United States), 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 socioeconomic resource of great importance in Don ãna 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 United States.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 largescale monitoring of this species, using the NIRS technique. The objectives of this work were as follows: (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) 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 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 entire time of analysis took about 2 min, approximately. Performance of the instrument is checked by measurement of photometric repeatability and wavelength accuracy. Mathematical transformations of the spectra and regressions performed on the spectral and laboratory data were obtained by (9) Confalonieri, M.; Fornasier, F.; Ursino, A.; Boccardi, F.; Pintus, B.; Odoardi, M. J. Near Infrared Spectrosc. 2001, 9, 123-131. (10) Font, R.; del Rıó, M.; Simoń, M.; Aguilar, M.; de Haro, A. Fresenius Environ. Bull. 2004, 13, in press. (11) Clark, D. H.; Cary, E. E.; Mayland, H. F. Agron. J. 1989, 81, 91-95. (12) Font, R.; del Rıó, M.; de Haro, A. Fresenius Environ. Bull. 2002, 11, 777781. (13) Moroń, A.; Cozzolino, D. J. Agric. Sci. 2002, 139, 413-423. (14) Sauvage, L.; Frank, D.; Stearne, J.; Millikan, M. B. Anal. Chim. Acta 2002, 458, 223-230. (15) Feo, J. C.; Aller, A. J. J. Anal. At. Spectrom. 2001, 16, 146-151. (16) Gutie´rrez-Yurrita, P. J.; Martıńez, J. M.; Ilheú, M.; Bravo-Utrera, M. A.; Bernardo, J. M.; Montes, C. In Crayfish in Europe as Alien Species: How to Make the Best of a Bad Situation; Holdich D. M., Gheardi, F., Eds.; A. A. Balkema: The Netherlands, 1999; pp 161-192. (17) Sańchez-Lo´pez, F. J.; Gil Garcıá, M. D.; Sańchez Morito, N. P.; Martıńez Vidal, J. L. Ecotox. Environ. Safety 2003, 54, 223-228.

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using the GLOBAL v. 1.50 program (WINISI II, Infrasoft International, LLC, Port Matilda, PA). 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 °C, freeze-dried, and then crushed to a fine powder in a mill. The resulting powder was stored at 4 °C until analysis. Determination of Inorganic Arsenic. The methodology applied was developed previously by Mun õz 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 Perkin-Elmer Model 3300). The analytical characteristics of the method were as follows: 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 threequarters 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 a 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 (18) Mun õz, O.; Ve´lez, D.; Montoro, R. Analyst 1999, 124, 601-607. (19) Hruschka, W. R. In Near-Infrared Technology in the Agricultural and Food Industries; Williams, P. C., Norris, K., Eds.; American Association of Cereal Chemists, Inc.: St. Paul, MN, 1987; pp 35-55.

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 the regression method, using wavelengths from 400 to 2500 nm every 8 nm. 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 (20) Shenk, J. S.; Workman, J. J.; Westerhaus, M. O. In Handbook of Near Infrared Analysis; Burns, D. A., Ciurczak, E. W., Eds.; Marcel Dekker: New York, 1992; pp 383-431. (21) Barnes, R. J.; Dhanoa, M. S.; Lister, S. J. Appl. Spectrosc. 1989, 43, 772777. (22) Shenk, J. S.; Westerhaus, M. O. In Near infrared spectroscopy: The future waves; Davies, A. M. C., Williams, P. C., Eds.; NIR Publications: Chichester, 1996; pp 198-202. (23) Williams, P. C.; Sobering, D. C. In Near infrared spectroscopy: The future waves; Davies, A. M. C., Williams, P. C., Eds.; NIR Publications: Chichester, 1996; pp 185-188. (24) Stone, M. J. R. Stat. Soc. 1974, B, 111-133. (25) Martens, H.; Naes, T. Multivariate calibration; John Wiley & Sons: New York, 1989. (26) NIRSystems. NIRS 2, Routine Analysis Manual; NIRSystems Infrasoft International: Port Matilda, 1995.

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 in the Range from 400 to 2500 nma calibration

cross-validation

n

range

mean

SD

SEC

R2

62

0.15-2.82

1.26

0.75

0.19

0.93

SECV/SD

1-VR

nt

0.38

0.84

5

a 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 crossvalidation 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.

1) were in the range of the contents previously reported for this species in the same area.27 Red Crayfish Reflectance Spectrum. Figure 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. When wavelength absorbances are being assigned 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′-dihydroxyββ-carotene-4-4′-dione), as has been reported to present typical absorption maxima (λmax) 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 Figure 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 (27) Devesa, V.; Suń ˜er, M. A.; Lai, V. W.-M.; Granchinho, S. C. R.; Martıńez, J. M.; Ve´lez, D.; Cullen, W. R.; Montoro, R. Appl. Organomet. Chem. 2002, 16, 123-132. (28) Garate, A. M.; Milicua, J. C. G.; Go´mez, R.; Macarulla, J. M.; Britton, G. Biochim. Biophys. Acta 1986, 881, 446-455. (29) Howell, B. K.; Matthews, A. D. Comp. Biochem. Physiol. 1991, 98B, 375379. (30) Cremades, O.; Parrado, J.; Alvarez-Osorio, M. C.; Jover, M.; Collantes de Terań, L.; Gutierrez, J. F.; Bautista J. Food Chem. 2003, 82, 559-566. (31) Tanaka Y. H.; Matsuguchi, T.; Katayama, T.; Simpson, K. L.; Chichester, C. O. Comp. Biochem. Physiol. 1976, 54B, 391-393. (32) Britton G. In Carotenoids; Britton, G., Liaaen-Jensen, S., Fander, H. P., Eds.; Birkhaüser Verlag: Basel, 1995; pp 13-62.


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Figure 1. Second 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.

Figure 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).

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 (Figure 1), which could be related to 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 (Figure 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 λmax at 666 nm, and the N-terminal domain (phycocyanobilin) of the phytochrome has shown a λmax 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 (33) Zagalsky, P. F. In Carotenoids; Britton, G., Liaaen-Jensen, S., Pfander, H., Eds.; Birkhau ¨ ser Verlag: Basel, 1995; pp 287-294. (34) Marder, S. R.; Torruellas, W. E.; Blanchard-Desce, M.; Ricci, V.; Stegeman, G. I.; Gilmour, S.; Bre´das, J. L.; Li, J.; Bublitz, G. U.; Boxer, S. G. Science 1997, 276, 1233-1236. (35) Jorissen, H. J.; Braslavsky, S. E.; Wagner, G.; Gartner, W. Photochem. Photobiol. 2002, 76 (4), 457-461. (36) Tkachuk R.; Kuzina F. D. Can. J. Plant Sci. 1982, 62, 875-884.

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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 to 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 Figure 2. The sign and value of the correlation at the wavelengths related to electronic transitions in the blue and green range (450500 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., r668 nm ) 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 region from 1100 to 2500 (37) Osborne, B. G.; Fearn, T.; Hindle, P. H. In Practical NIR spectroscopy with applications in food and beverage analysis; Browning, D., Ed.; Longman Scientific and Technical: Harlow, 1993; pp 13-35. (38) Murray, I. In Proceedings of the International NIR/NIT Conference; Hollo, J., Kaffka, K. J., Gonczy, J. L., Eds.; Akademiai Kiado: Budapest, 1986; pp 13-28.

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 to 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 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 equations that presented five 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 (Figure 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 estima(39) Murray, I.; Williams, P. C. In Near-Infrared Technology in the Agricultural and Food Industries; Williams, P. C., Norris, K., Ed.; American Association of Cereal Chemists, Inc.: St. Paul, MN, 1987; pp 17-34. (40) Gutie´rrez-Yurrita, P. J.; Montes, C. Comp. Biochem. Physiol., Part A: Mol. Integr. Physiol. 1998, 120, 713-721. (41) Gutie´rrez-Yurrita, P. J.; Montes, C. Comp. Biochem. Physiol., Part A: Mol. Integr. Physiol. 2001, 130, 29-38. (42) Jeckel, W. H.; Aizpun de Moreno, J. E.; Moreno, V. J. Comp. Biochem. Physiol., Part B: Biochem. Mol. Biol. 1991, 98, 261-266. (43) Mura, G.; Zarattini, P.; Delise, M.; Fabietti, F.; Bocca, A. Crustaceana 2000, 73 (4), 479-495. (44) Fernańdez, M. A. S.; Mendoç a, M. I. R.; Marques, J. C.; Madeira, V. M. C. Fresh Water Crayfish 1995, 10, 98-104.

Figure 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).

tion 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 the determination of total trace elements and macronutrients in other matrixes. Some authors have also reported SECV/SD data for mineral analysis in plants, similar to or even lower than those shown in this work. Va´zquez 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. Moroń 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, respectively. However, prediction errors related to micronutrients and trace metals are sometimes higher than those previously mentioned, depending on the element being predicted. Va´zquez 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 Moroń and Cozzolino,13 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 micronutrients in three 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 (45) Murray, I. In Sward Management Handbook; Davies, A., Baker, R. D., Grant, S. A., Laidlaw, A. S., Eds; British Grassland Society: UK, 1993; pp 285312. (46) Va´zquez de Aldana, B. R.; Garcıá-Criado, B.; Garcıá-Ciudad, A.; Pe´rez-Corona, M. E. Commun. Soil Sci. Plant Anal. 1995, 26 (9-10), 1383-1396. (47) Clark, D. H.; Mayland, H. F.; Lamb, R. C. Agron. J. 1987, 79, 485-490.


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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 (Figure 2). 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 (Figure 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 abovementioned 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.

Figure 4. MPLS loading plots for inorganic arsenic for the equation (2, 5, 5, 2; SNV + DT).

composition, MPLS regression was employed (Figure 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 Figure 4 that chromophores existing in the tissues of the crayfish greatly influenced the first three MPLS loadings of the second derivative (48) Mouneyrac, C.; Amiard, J. C.; Amiard-Triquet, C.; Cottier, A.; Rainbow, P. S.; Smith, B. D. Aquat. Toxicol. 2002, 57, 225-242. (49) Baudrimont, M.; Andres, S.; Durrieu, G.; Boudou, A. Aquat. Toxicol. 2003, 63, 89-102.

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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. ACKNOWLEDGMENT 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. Martıńez (Universidad Autońoma de Madrid, Spain) for providing the crayfish samples used in this work. This research was supported by the Ministerio de Ciencia y Tecnologıá, Project AGL 2001-1789, for which the authors are deeply indebted.

Received for review November 20, 2003. Accepted April 23, 2004. AC035377C

Visible and Near-Infrared Spectroscopy as a Technique for Screening

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