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Quantitative determination of some food dyes using digital processing of images obtained by thin-layer chromatography
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Journal of Chromatography A, 1188 (2008) 295–300
Quantitative determination of some food dyes using digital processing of images obtained by thin-layer chromatography Florin Soponar, Augustin C˘at˘alin Mot¸, Costel Sˆarbu ∗ Babe¸s-Bolyai University, Faculty of Chemistry and Chemical Engineering, Arany Janos Street 11, RO-400028 Cluj Napoca, Romania Received 13 December 2007; received in revised form 10 February 2008; accepted 12 February 2008 Available online 29 February 2008
Abstract A high-performance thin-layer chromatographic method combined with image processing of scanned chromatograms was developed for the determination of some food dyes (tartrazine, azorubine and Sunset Yellow) in different products. Porous silica gel with 3-aminopropyl functional groups attached to the matrix was used as stationary phase and a mixture of isopropanol, diethyl ether and ammonia (2:2:1, v/v/v) formed the mobile phase. Quantitative evaluation was performed using special-purpose software. The linearity of the analytical procedure was sustained by the numerical parameters such as correlation coefficient (0.9952–0.9980) and standard error of determination (0.03–0.20). The limits of detection were found to be within the range of 5.21–9.34 ng/spot, and the limits of quantification between 10.21 and 18.09 ng/spot. Recovery studies performed on two levels of concentration gave values between 96.39 and 102.76%. These results show that the regression approach provides rigorous and realistic detection and quantification limits and as a consequence can be routinely applied to other analytical systems. This method does not require expensive analytical instruments compared with classical densitometry and provides a reliable quantitative evaluation with minimum of time. © 2008 Elsevier B.V. All rights reserved. Keywords: HP-TLC; Food-dyes; Quantitative evaluation; Image processing; Validation
1. Introduction Thin-layer chromatography is one of the cheapest, quickest and most efficient separation methods for many classes of chemical compounds. It has some important advantages over the other chromatographic techniques like low cost of instrumentation, evaluation of the whole sample because of the spatial separation, the ability of making simultaneous separations (even 20 samples) and, of course, shortened time required for analysis. All these make thin-layer chromatography a convenient choice for many applications including analytical, bio-medical and pharmaceutical field. Scanning densitometry is the classical method for quantitative evaluation of chromatographic plates [1–3]. It is based on measuring the absorbance or fluorescense of different zones on the plate exposed to monochromatic source of light. Many techniques have been reported in literatures [4–8], but all of
∗
Corresponding author. Tel.: +40 264 593833; fax: +40 264 590818. E-mail address: [email protected] (C. Sˆarbu).
0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.02.077
them involved expensive apparatus. The most common difficulties that are encountered here are increased time of scanning (from 30 min to 2 h) and low resolution. An alternative way of quantitative evaluation is represented by the charged coupled devices (CCDs) systems [9–10] that are bidimensional detectors containing sensors capable of recording an image of a surface in a matter of seconds. Still, the main limitation is the unequal illumination of the recorded surface, which leads to errors [11]. Another solution for obtaining chromatographic images is to use a flatbed scanner. Owing to the uniform lighting of surfaces, short time of scanning (approximately 10–20 s) and high optical resolution, it is suitable for recording chromatographic plates. These systems based on digital processing of chromatographic images are reported in literature as an important quantitative method in thin-layer chromatography [12–15]. Also, it is possible to save the images in different file formats (tiff, bmp, jpeg) to store them for a long time without affecting their quality and access them instantly for eventual revaluation. In general, the color of food is associated with certain flavors, thus influencing the perception of the flavor from sweets to wines
296
F. Soponar et al. / J. Chromatogr. A 1188 (2008) 295–300
Fig. 1. The structures of the studied compounds.
[16]. Based on this, the producers add different substances in foods in order to simulate the natural color. The variation of the color of a certain product from one season to another, the effects of food processing and storage make the addition of artificial dyes a valuable technique, in this way maintaining the “natural” hue or the one preferred by the consumer. In this paper, three different dyes have been analyzed, which are encountered in many products on the Romanian market and not only. Synthetic azo-dyes represent about 60–70% of dyes used in food and textile industry; they are more stable than natural dyes, are cheap and are highly soluble in water. The only disadvantage could be the fact that they are insoluble in oils or organic solvents. As they are highly soluble in water, they do not remain in the organism (being metabolized in the liver), and they are eliminated through urine. A high-performance liquid chromatography method using a short column with photodiode array detection has been used in dye analysis (including the studied compounds in this paper), and it is recommended when the mixture contains many different colorants [17]. In this case the recoveries ranged from 76.6 to 115.0%. Another chromatographic method using high-performance liquid chromatography with UV-diode array detection (DAD) [18] has been successfully applied for colorant analysis including tartrazine and Sunset Yellow in several commercial food products with better recoveries (98%). UV molecular absorption spectrometric techniques based on multivariate calibration methods have also been reported [19], proving to be very useful in the determination of tartrazine, Sunset Yellow and Ponceau 4R with 94.5–105.3% recovery values. A method that combines high-performance thin-layer chromatography with digital processing is proposed for the determination of three food dyes. The structures are given in Fig. 1.
2. Experimental 2.1. Reagents The dyes used in this study (tartrazine, azorubine and Sunset Yellow) were obtained from Fluka (Buchs, Switzerland). Diethyl ether and ammonia (concentrated solution) were from Reactivul (Bucharest, Romania), while isopropanol was obtained from Silal Trading (Bucharest, Romania). All these solvents had analytical grade purity. Distilled water was the solvent used for the dyes. 2.2. Apparatus and software For quantitative determination a flatbed HP ScanJet 3970 was used at an optical resolution of 200 dpi. The software for digital processing (Macherey-Nagel TLC Scanner software) [20] was developed by Wieczorrek (Macherey-Nagel). The calculation of limits of detection and quantification was realized with SMAC (statistical methods in analytical chemistry) [21]. For statistical data treatment, STATISTICA was used. Data processing can be made on any computer with a medium configuration (500 MHz processor, 128 Mb RAM), but for image processing a configuration with a high video memory and high frequency processor (3 GHz, 512 Mb RAM) is recommended. Sample application was done using a 10 L Shimadzu microsyringe. 2.3. Sample and standard preparation The analysis of synthetic dyes was performed from five products, namely five dried concentrated juices, commercially available in different formats, according to the producer. These
F. Soponar et al. / J. Chromatogr. A 1188 (2008) 295–300
297
Table 1 Food products which contain the studied dyes Sample number
Commercial name
Tartrazine
Sunset Yellow
Azorubine
Producer
1 2 3 4 5
Frutti Orange Fruit Orange Bolero Cherry Bolero Orange Bolero Peach
Present Present Absent Present Present
Present Present Absent Present Absent
Absent Absent Present Absent Absent
Kendy (Bulgaria) Alta Vista (Romania) Eurostock (Bulgaria) Eurostock (Bulgaria) Eurostock (Bulgaria)
products can be bought from different food-stores. An overview is available in Table 1. Three stock solutions of tartrazine, Sunset Yellow and azorubine were prepared by dissolving 2.0019 g, 1.0098 g, and 2.1337 g, respectively in 1000 mL distilled water. These solutions were next used in preparing the standards. For each dye, six standards were made with a concentration between 30 and 180 mg L−1 for Sunset Yellow, 40.04 and 190.19 mg L−1 for tartrazine and 42.68 and 202.73 mg L−1 for azorubine. To determine the dyes from the products, 2–3 g of sample from each product was diluted to a volume of 100 mL and analyzed without any additional treatment. 2.4. HP-TLC procedure For separation of dyes Nano-SIL NH2 /UV 254 chromatographic plates were used with amino-modified layer (10 cm × 10 cm) from Macherey-Nagel. Precise volumes of 1 L were applied as spots in duplicates at 1 cm above the base and the edges of the plate using the 10 L microsyringe. After the plates have been dried in air for 15 min to eliminate any trace of water, they have been developed by ascending chromatography in a developing chamber saturated with vapors of mobile phase. The developing took place at room temperature. Mobile phase was a mixture of isopropanol, diethyl ether and ammonia (solution) at a ratio of 2:2:1 (v/v/v). A volume of 25 mL of mobile phase was enough for each elution. The developing distance was 5 cm; after the elution the plates were dried at 60 ◦ C and prepared for scanning process. 2.5. Obtaining the chromatographic images and storage The chromatographic plates were scanned using TrueColor setting of the HP ScanJet Driver, and the images were saved as TIFF files without any compression to avoid the loss of image quality. 3. Results and discussion
of the red (R), green (G) and blue (B) channels [22]. The color value is calculated by the additive subtraction of RGB intensities from white. In Fig. 2a the chromatogram of a mixture of Sunset Yellow (yellow–orange), azorubine (red–violet) and tartrazine (yellow) can be observed. As said before, the chromatogram was obtained by additive subtraction of RGB intensities from white. Fig. 2c shows again the same chromatogram, but this time, adjusted to its three RGB channels. Therefore, it can be seen that only certain channels are responsible for the color value of a given point from the chromatographic plate. For example, in Fig. 2c it can be noticed that in case of tartrazine only the blue channel contributes to the final chromatographic peak [23], when in the case of azorubine the blue channel together with the green one can be taken into consideration. To Sunset Yellow the blue channel has a major influence with a small contribution from green channel, as expected, because Sunset Yellow and tartrazine have a similar color. In all cases the red channel has a low influence on the final chromatogram, being responsible for eventual impurities on the plate or for the background noise. The ability to decompose a given peak corresponding to a colored spot into three components, each one with its contribution of information, can provide the possibility to choose the component that is more suitable for analysis. Therefore, the blue channel was selected for Sunset Yellow because the red and green channels due to their low content of information were influenced by the background noise. In this way the final results are less influenced by errors. This option can be also useful in cases of compounds that present low resolution. For instance, the calibration for azorubine was done using only the green channel because the blue one is partly influenced by tartrazine. For tartrazine there is no option left than to work on the blue channel, also with good results. Chromatographic analysis through Macherey-Nagel TLC Scanner software implies the detection of the spots by referring to a clean zone from the developed plate, the selection of appropriate channel (or combination of channels) corresponding to each dye, and the integration of the peaks. Further the concentration of the spot vs. integral is represented, the linear regression equation is obtained and by interpolation the unknown concentration is determined.
3.1. Chromatograms processing 3.2. Linearity, limits of detection and quantification It is common knowledge that the digitalization of images obtained by flatbed scanners or digital (photo or video) cameras result in color data being represented by a set of RGB (red, green, blue) values. These parameters are the color intensities
The linear domains as well as the calibration curve have been studied in this paper. In this way 1 L of each standard was applied in duplicates and analyzed using the method
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Fig. 2. The position of the spots from the solvent front on the chromatographic plate (b) and the chromatograms disposed on all three channels (a) and on separate channels (c). 1 = Sunset Yellow; 2 = azorubine; 3 = tartrazine. Table 2 The linear domain, statistic parameters, limits of detection and quantification
(mg L−1 )
Linear domain Regression equationa Correlation coefficient, r Standard error (SE) F Limit of detection (ng/spot) Limit of quantification (ng/spot)
Sunset Yellow
Tartrazine
Azorubine
30.03–180.18 y = 0.602(±0.012)x − 2.300(±1.408) 0.9980 0.20 2503 5.21 10.21
40.04–190.19 y = 0.291(±0.008)x + 6.457(±1.060) 0.9958 0.03 1194 8.13 15.78
42.68–202.73 y = 0.286(±0.009)x + 3.176(±1.200) 0.9952 0.03 1028 9.34 18.09
x: sample concentration. a y: peak integral.
described previously. Linear relation between the applied concentrations and the integrals of the peaks is described in Table 2. There is a relatively large linear domain, where the correlation coefficients have values in the range of 0.9952–0.9980. Limits of detection and quantification have been calculated using SMAC software, based on confidence bands generated from calibration experiments using ordinary least squares method [24,25].
3.3. Precision and accuracy In order to determine the precision of six identical spots, each one containing 100.1 ng/spot of Sunset Yellow, 106.7 ng/spot of azorubine and 130.10 ng/spot of tartrazine, were applied. The chromatographic plate was developed and scanned as described before. The data are presented in Table 3, the precision being estimated in terms of standard deviation.
Table 3 Precision determined by measuring six replicates Food dye Sunset Yellow Azorubine Tartrazine
Mean signal (arbitrary units) 103.33 70 89.5
Standard deviation (SD)
Standard error of the mean (SEM)
1.63 1.67 1.87
0.67 0.68 0.76
F. Soponar et al. / J. Chromatogr. A 1188 (2008) 295–300
299
Table 4 Recovery assessed with the method of standard additions at two concentration levels Food dye
Initial (ng/spot)
Added (ng/spot)
Observed (ng/spot)
Recovery (%)
Tartrazine
0 21.29
120.12 100.10
115.78 118.68
96.39 97.76
Sunset Yellow
0 22.54
150.15 50.05
154.30 71.54
102.76 98.55
Azorubine
0 34.59
128.04 51.45
130.70 86.69
102.08 101.27
Table 5 Quantitative estimation of the three synthetic azo-dyes Sample
Net weight (g/product)
Amount of dye (mg/product) Tartrazine
1 2 3 4 5
5 4 8 8 8
19.04 ± 11.94 ± – 16.32 ± 26.39 ±
0.92 0.82 1.77 1.04
Sunset Yellow 15.40 ± 0.54 10.76 ± 0.87 – 8.22 ± 1.96 –
The accuracy of the proposed method was determined by internal standard addition. There have been analyzed solutions with zero initial concentration, as well as solutions with a known initial concentration. Precise quantities of pure dyes were added to 1 g of product sample. For making a solution with zero initial concentration, the pure dye was diluted with distilled water. Table 4 contains the data that demonstrate a good recovery of all the studied dyes. 3.4. Quantitative evaluation The method being validated through precision, linearity and accuracy, the next step was the quantitative determination of the dyes from the commercial product, which is the purpose of this study. The results of the validation attest the fitness of the proposed analytical procedure for the identification and quantitative determination of the three dyes in mixture. Five different commercial products that contained at least one of the three studied dyes were analyzed in order to assign the values of their contents. The dyes were simultaneously evaluated, on the same plate, reducing the time and the amount of the materials required for the analysis. The obtained results and their confidence intervals are listed in Table 5. As the net weight of the products is different depending upon the producer, the amount of the dyes are listed in weight percentages too in order to facilitate the comparison of their amounts. The contents of the food dyes are well correlated with the desirable color of the product that stands on the natural tint of the fruit (Table 1). 4. Conclusion In this paper a method for the determination of some synthetic azo-dyes is proposed. Compared with other methods that obtained similar results but with more expensive and sophisticated apparatus, the instrumentation used in this report is more
Amount of dye (%) Azorubine
Tartrazine
Sunset Yellow
Azorubine
– – 36.52 ± 1.49 – –
0.38 ± 0.3 ± – 0.20 ± 0.33 ±
0.31 ± 0.01 0.27 ± 0.02 – 0.10 ± 0.02 –
– – 0.46 ± 0.02 – –
0.02 0.02 0.02 0.01
simple and easy to handle and acquire. This method combines high-performance thin-layer chromatography with digital processing of images, a domain that is still growing and open to further improvements. The small cost of the materials, instrumentation and a short time of scanning required (compared with classical densitometry) make this method an optimal choice for rapid quantitative evaluation of different samples. The validation of this method demonstrates once again that using appropriate resources, thin-layer chromatography combined with image processing can be a powerful tool in quantitative determination of dyes in commercial products. Also, this technique can be applied to other classes of compounds, colored or colorless, in the last instance making necessary the use of a UV lamp connected to a digital camera. References [1] V. Pollak, J. Schulze-Clewing, J. Planar Chromatogr. 3 (1990) 104. [2] R. Vijayakannan, M. Karan, S. Dutt, V. Jain, K. Vasisht, Chromatographia 63 (2006) 277. [3] S.A. Coran, V. Giannellini, M. Bambagiotto-Alberti, J. Chromatogr. A 1045 (2004) 217. [4] P. Trivedi, K. Pundarikakshudu, Chromatographia 65 (2007) 239. [5] A. Witek, H. Hopkala, G. Matzsik, Chromatographia 50 (1999) 41. [6] T. Ohno, E. Mikami, H. Oka, J. Nat. Med. 60 (2006) 141. [7] N.P. Singh, A.P. Gupta, A.K. Sinha, P.S. Ahuja, J. Chromatogr. A 1077 (2005) 202. [8] A.S. Stepanov, Pharm. Chem. J. 37 (2003) 54. [9] J.V. Sweedler, R.B. Bilhorn, P.M. Epperson, G.R. Sims, M.B. Denton, Anal. Chem. 60 (1988) 282. [10] J.V. Sweedler, R.B. Bilhorn, P.M. Epperson, G.R. Sims, M.B. Denton, Anal. Chem. 60 (1988) 372. [11] I. Vovk, M. Prosek, J. Chromatogr. A 768 (1997) 329. [12] C.F. Poole, J. Chromatogr. A 856 (1999) 399. [13] A.V. Gerasimov, Zh. Anal. Khim. 55 (2000) 1292. [14] A.V. Gerasimov, Zh. Anal. Khim. 59 (2004) 392. [15] L. Zhang, X. Lin, J. Chromaotgr. A 1109 (2006) 273. [16] J. Delwiche, Food Qual. Prefer. 15 (2004) 137. [17] N. Yoshioka, K. Ichihashi, Talanta 74 (2008) 1408.
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[18] S. Pereira Alves, D.M. Brum, E.C. Branco de Andrade, A.D. Pereira Netto, Food Chem. 107 (2008) 489. [19] J.J. Berzas Nevado, J. Rodriguez Flores, M.J. Villasenor Llerena, J. Anal. Chem. 361 (1998) 465. [20] C. Wieczorrek, J. Planar Chromatogr. 18 (2005) 181. [21] P.C. Meier, R.E. Zund, Statistical Methods in Analytical Chemistry, Wiley, New York, 1993.
[22] E.I. Giorgianni, T.E. Madden, Digital Color Management: Encoding Solutions, Addison Wesley, Reading, MA, 1998. [23] A.V. Gerasomiv, Zh. Anal. Khim. 58 (2002) 241. [24] C. Sarbu, L. Soran, M. Curtui, Rev. Roum. Chim. 55 (2004) 137. [25] A. Hubaux, G. Vos, Anal. Chem. 42 (1970) 849.
Journal of Chromatography A, 1188 (2008) 295–300
Quantitative determination of some food dyes using digital processing of images obtained by thin-layer chromatography Florin Soponar, Augustin C˘at˘alin Mot¸, Costel Sˆarbu ∗ Babe¸s-Bolyai University, Faculty of Chemistry and Chemical Engineering, Arany Janos Street 11, RO-400028 Cluj Napoca, Romania Received 13 December 2007; received in revised form 10 February 2008; accepted 12 February 2008 Available online 29 February 2008
Abstract A high-performance thin-layer chromatographic method combined with image processing of scanned chromatograms was developed for the determination of some food dyes (tartrazine, azorubine and Sunset Yellow) in different products. Porous silica gel with 3-aminopropyl functional groups attached to the matrix was used as stationary phase and a mixture of isopropanol, diethyl ether and ammonia (2:2:1, v/v/v) formed the mobile phase. Quantitative evaluation was performed using special-purpose software. The linearity of the analytical procedure was sustained by the numerical parameters such as correlation coefficient (0.9952–0.9980) and standard error of determination (0.03–0.20). The limits of detection were found to be within the range of 5.21–9.34 ng/spot, and the limits of quantification between 10.21 and 18.09 ng/spot. Recovery studies performed on two levels of concentration gave values between 96.39 and 102.76%. These results show that the regression approach provides rigorous and realistic detection and quantification limits and as a consequence can be routinely applied to other analytical systems. This method does not require expensive analytical instruments compared with classical densitometry and provides a reliable quantitative evaluation with minimum of time. © 2008 Elsevier B.V. All rights reserved. Keywords: HP-TLC; Food-dyes; Quantitative evaluation; Image processing; Validation
1. Introduction Thin-layer chromatography is one of the cheapest, quickest and most efficient separation methods for many classes of chemical compounds. It has some important advantages over the other chromatographic techniques like low cost of instrumentation, evaluation of the whole sample because of the spatial separation, the ability of making simultaneous separations (even 20 samples) and, of course, shortened time required for analysis. All these make thin-layer chromatography a convenient choice for many applications including analytical, bio-medical and pharmaceutical field. Scanning densitometry is the classical method for quantitative evaluation of chromatographic plates [1–3]. It is based on measuring the absorbance or fluorescense of different zones on the plate exposed to monochromatic source of light. Many techniques have been reported in literatures [4–8], but all of
∗
Corresponding author. Tel.: +40 264 593833; fax: +40 264 590818. E-mail address: [email protected] (C. Sˆarbu).
0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.02.077
them involved expensive apparatus. The most common difficulties that are encountered here are increased time of scanning (from 30 min to 2 h) and low resolution. An alternative way of quantitative evaluation is represented by the charged coupled devices (CCDs) systems [9–10] that are bidimensional detectors containing sensors capable of recording an image of a surface in a matter of seconds. Still, the main limitation is the unequal illumination of the recorded surface, which leads to errors [11]. Another solution for obtaining chromatographic images is to use a flatbed scanner. Owing to the uniform lighting of surfaces, short time of scanning (approximately 10–20 s) and high optical resolution, it is suitable for recording chromatographic plates. These systems based on digital processing of chromatographic images are reported in literature as an important quantitative method in thin-layer chromatography [12–15]. Also, it is possible to save the images in different file formats (tiff, bmp, jpeg) to store them for a long time without affecting their quality and access them instantly for eventual revaluation. In general, the color of food is associated with certain flavors, thus influencing the perception of the flavor from sweets to wines
296
F. Soponar et al. / J. Chromatogr. A 1188 (2008) 295–300
Fig. 1. The structures of the studied compounds.
[16]. Based on this, the producers add different substances in foods in order to simulate the natural color. The variation of the color of a certain product from one season to another, the effects of food processing and storage make the addition of artificial dyes a valuable technique, in this way maintaining the “natural” hue or the one preferred by the consumer. In this paper, three different dyes have been analyzed, which are encountered in many products on the Romanian market and not only. Synthetic azo-dyes represent about 60–70% of dyes used in food and textile industry; they are more stable than natural dyes, are cheap and are highly soluble in water. The only disadvantage could be the fact that they are insoluble in oils or organic solvents. As they are highly soluble in water, they do not remain in the organism (being metabolized in the liver), and they are eliminated through urine. A high-performance liquid chromatography method using a short column with photodiode array detection has been used in dye analysis (including the studied compounds in this paper), and it is recommended when the mixture contains many different colorants [17]. In this case the recoveries ranged from 76.6 to 115.0%. Another chromatographic method using high-performance liquid chromatography with UV-diode array detection (DAD) [18] has been successfully applied for colorant analysis including tartrazine and Sunset Yellow in several commercial food products with better recoveries (98%). UV molecular absorption spectrometric techniques based on multivariate calibration methods have also been reported [19], proving to be very useful in the determination of tartrazine, Sunset Yellow and Ponceau 4R with 94.5–105.3% recovery values. A method that combines high-performance thin-layer chromatography with digital processing is proposed for the determination of three food dyes. The structures are given in Fig. 1.
2. Experimental 2.1. Reagents The dyes used in this study (tartrazine, azorubine and Sunset Yellow) were obtained from Fluka (Buchs, Switzerland). Diethyl ether and ammonia (concentrated solution) were from Reactivul (Bucharest, Romania), while isopropanol was obtained from Silal Trading (Bucharest, Romania). All these solvents had analytical grade purity. Distilled water was the solvent used for the dyes. 2.2. Apparatus and software For quantitative determination a flatbed HP ScanJet 3970 was used at an optical resolution of 200 dpi. The software for digital processing (Macherey-Nagel TLC Scanner software) [20] was developed by Wieczorrek (Macherey-Nagel). The calculation of limits of detection and quantification was realized with SMAC (statistical methods in analytical chemistry) [21]. For statistical data treatment, STATISTICA was used. Data processing can be made on any computer with a medium configuration (500 MHz processor, 128 Mb RAM), but for image processing a configuration with a high video memory and high frequency processor (3 GHz, 512 Mb RAM) is recommended. Sample application was done using a 10 L Shimadzu microsyringe. 2.3. Sample and standard preparation The analysis of synthetic dyes was performed from five products, namely five dried concentrated juices, commercially available in different formats, according to the producer. These
F. Soponar et al. / J. Chromatogr. A 1188 (2008) 295–300
297
Table 1 Food products which contain the studied dyes Sample number
Commercial name
Tartrazine
Sunset Yellow
Azorubine
Producer
1 2 3 4 5
Frutti Orange Fruit Orange Bolero Cherry Bolero Orange Bolero Peach
Present Present Absent Present Present
Present Present Absent Present Absent
Absent Absent Present Absent Absent
Kendy (Bulgaria) Alta Vista (Romania) Eurostock (Bulgaria) Eurostock (Bulgaria) Eurostock (Bulgaria)
products can be bought from different food-stores. An overview is available in Table 1. Three stock solutions of tartrazine, Sunset Yellow and azorubine were prepared by dissolving 2.0019 g, 1.0098 g, and 2.1337 g, respectively in 1000 mL distilled water. These solutions were next used in preparing the standards. For each dye, six standards were made with a concentration between 30 and 180 mg L−1 for Sunset Yellow, 40.04 and 190.19 mg L−1 for tartrazine and 42.68 and 202.73 mg L−1 for azorubine. To determine the dyes from the products, 2–3 g of sample from each product was diluted to a volume of 100 mL and analyzed without any additional treatment. 2.4. HP-TLC procedure For separation of dyes Nano-SIL NH2 /UV 254 chromatographic plates were used with amino-modified layer (10 cm × 10 cm) from Macherey-Nagel. Precise volumes of 1 L were applied as spots in duplicates at 1 cm above the base and the edges of the plate using the 10 L microsyringe. After the plates have been dried in air for 15 min to eliminate any trace of water, they have been developed by ascending chromatography in a developing chamber saturated with vapors of mobile phase. The developing took place at room temperature. Mobile phase was a mixture of isopropanol, diethyl ether and ammonia (solution) at a ratio of 2:2:1 (v/v/v). A volume of 25 mL of mobile phase was enough for each elution. The developing distance was 5 cm; after the elution the plates were dried at 60 ◦ C and prepared for scanning process. 2.5. Obtaining the chromatographic images and storage The chromatographic plates were scanned using TrueColor setting of the HP ScanJet Driver, and the images were saved as TIFF files without any compression to avoid the loss of image quality. 3. Results and discussion
of the red (R), green (G) and blue (B) channels [22]. The color value is calculated by the additive subtraction of RGB intensities from white. In Fig. 2a the chromatogram of a mixture of Sunset Yellow (yellow–orange), azorubine (red–violet) and tartrazine (yellow) can be observed. As said before, the chromatogram was obtained by additive subtraction of RGB intensities from white. Fig. 2c shows again the same chromatogram, but this time, adjusted to its three RGB channels. Therefore, it can be seen that only certain channels are responsible for the color value of a given point from the chromatographic plate. For example, in Fig. 2c it can be noticed that in case of tartrazine only the blue channel contributes to the final chromatographic peak [23], when in the case of azorubine the blue channel together with the green one can be taken into consideration. To Sunset Yellow the blue channel has a major influence with a small contribution from green channel, as expected, because Sunset Yellow and tartrazine have a similar color. In all cases the red channel has a low influence on the final chromatogram, being responsible for eventual impurities on the plate or for the background noise. The ability to decompose a given peak corresponding to a colored spot into three components, each one with its contribution of information, can provide the possibility to choose the component that is more suitable for analysis. Therefore, the blue channel was selected for Sunset Yellow because the red and green channels due to their low content of information were influenced by the background noise. In this way the final results are less influenced by errors. This option can be also useful in cases of compounds that present low resolution. For instance, the calibration for azorubine was done using only the green channel because the blue one is partly influenced by tartrazine. For tartrazine there is no option left than to work on the blue channel, also with good results. Chromatographic analysis through Macherey-Nagel TLC Scanner software implies the detection of the spots by referring to a clean zone from the developed plate, the selection of appropriate channel (or combination of channels) corresponding to each dye, and the integration of the peaks. Further the concentration of the spot vs. integral is represented, the linear regression equation is obtained and by interpolation the unknown concentration is determined.
3.1. Chromatograms processing 3.2. Linearity, limits of detection and quantification It is common knowledge that the digitalization of images obtained by flatbed scanners or digital (photo or video) cameras result in color data being represented by a set of RGB (red, green, blue) values. These parameters are the color intensities
The linear domains as well as the calibration curve have been studied in this paper. In this way 1 L of each standard was applied in duplicates and analyzed using the method
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F. Soponar et al. / J. Chromatogr. A 1188 (2008) 295–300
Fig. 2. The position of the spots from the solvent front on the chromatographic plate (b) and the chromatograms disposed on all three channels (a) and on separate channels (c). 1 = Sunset Yellow; 2 = azorubine; 3 = tartrazine. Table 2 The linear domain, statistic parameters, limits of detection and quantification
(mg L−1 )
Linear domain Regression equationa Correlation coefficient, r Standard error (SE) F Limit of detection (ng/spot) Limit of quantification (ng/spot)
Sunset Yellow
Tartrazine
Azorubine
30.03–180.18 y = 0.602(±0.012)x − 2.300(±1.408) 0.9980 0.20 2503 5.21 10.21
40.04–190.19 y = 0.291(±0.008)x + 6.457(±1.060) 0.9958 0.03 1194 8.13 15.78
42.68–202.73 y = 0.286(±0.009)x + 3.176(±1.200) 0.9952 0.03 1028 9.34 18.09
x: sample concentration. a y: peak integral.
described previously. Linear relation between the applied concentrations and the integrals of the peaks is described in Table 2. There is a relatively large linear domain, where the correlation coefficients have values in the range of 0.9952–0.9980. Limits of detection and quantification have been calculated using SMAC software, based on confidence bands generated from calibration experiments using ordinary least squares method [24,25].
3.3. Precision and accuracy In order to determine the precision of six identical spots, each one containing 100.1 ng/spot of Sunset Yellow, 106.7 ng/spot of azorubine and 130.10 ng/spot of tartrazine, were applied. The chromatographic plate was developed and scanned as described before. The data are presented in Table 3, the precision being estimated in terms of standard deviation.
Table 3 Precision determined by measuring six replicates Food dye Sunset Yellow Azorubine Tartrazine
Mean signal (arbitrary units) 103.33 70 89.5
Standard deviation (SD)
Standard error of the mean (SEM)
1.63 1.67 1.87
0.67 0.68 0.76
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Table 4 Recovery assessed with the method of standard additions at two concentration levels Food dye
Initial (ng/spot)
Added (ng/spot)
Observed (ng/spot)
Recovery (%)
Tartrazine
0 21.29
120.12 100.10
115.78 118.68
96.39 97.76
Sunset Yellow
0 22.54
150.15 50.05
154.30 71.54
102.76 98.55
Azorubine
0 34.59
128.04 51.45
130.70 86.69
102.08 101.27
Table 5 Quantitative estimation of the three synthetic azo-dyes Sample
Net weight (g/product)
Amount of dye (mg/product) Tartrazine
1 2 3 4 5
5 4 8 8 8
19.04 ± 11.94 ± – 16.32 ± 26.39 ±
0.92 0.82 1.77 1.04
Sunset Yellow 15.40 ± 0.54 10.76 ± 0.87 – 8.22 ± 1.96 –
The accuracy of the proposed method was determined by internal standard addition. There have been analyzed solutions with zero initial concentration, as well as solutions with a known initial concentration. Precise quantities of pure dyes were added to 1 g of product sample. For making a solution with zero initial concentration, the pure dye was diluted with distilled water. Table 4 contains the data that demonstrate a good recovery of all the studied dyes. 3.4. Quantitative evaluation The method being validated through precision, linearity and accuracy, the next step was the quantitative determination of the dyes from the commercial product, which is the purpose of this study. The results of the validation attest the fitness of the proposed analytical procedure for the identification and quantitative determination of the three dyes in mixture. Five different commercial products that contained at least one of the three studied dyes were analyzed in order to assign the values of their contents. The dyes were simultaneously evaluated, on the same plate, reducing the time and the amount of the materials required for the analysis. The obtained results and their confidence intervals are listed in Table 5. As the net weight of the products is different depending upon the producer, the amount of the dyes are listed in weight percentages too in order to facilitate the comparison of their amounts. The contents of the food dyes are well correlated with the desirable color of the product that stands on the natural tint of the fruit (Table 1). 4. Conclusion In this paper a method for the determination of some synthetic azo-dyes is proposed. Compared with other methods that obtained similar results but with more expensive and sophisticated apparatus, the instrumentation used in this report is more
Amount of dye (%) Azorubine
Tartrazine
Sunset Yellow
Azorubine
– – 36.52 ± 1.49 – –
0.38 ± 0.3 ± – 0.20 ± 0.33 ±
0.31 ± 0.01 0.27 ± 0.02 – 0.10 ± 0.02 –
– – 0.46 ± 0.02 – –
0.02 0.02 0.02 0.01
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