International Journal of Biological Macromolecules 87 (2016) 290–294
Contents lists available at ScienceDirect
International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac
Simultaneous determination of neutral and uronic sugars based on UV–vis spectrometry combined with PLS Ci-Hai Zhang, Yong-Huan Yun, Zhi-Min Zhang, Yi-Zeng Liang ∗ College of Chemistry and Chemical engineering, Central South University, Changsha, 410083, China
a r t i c l e
i n f o
Article history: Received 15 October 2015 Received in revised form 24 February 2016 Accepted 25 February 2016 Available online 27 February 2016 Keywords: Partial least squares Neutral sugars Uronic sugars Simultaneous determination
a b s t r a c t A method using partial least squares (PLS) for simultaneous determination of neutral and uronic sugars was developed in this paper. This method is based on the development of the reaction between the analytes and anthrone. The calibration set was built with 25 binary solutions at the concentrations ranging from 20 to 100 g/mL for glucose and from 10 to 50 g/mL for glucuronic acid. An independent prediction set was utilized to check the robustness of the PLS calibration model. The root-mean-square error of prediction (RMSEP) values for neutral and uronic sugars are 1.2233 and 1.9367, respectively. The correlation coefficient for the prediction set (Rp 2 ) values for them are 0.9971 and 0.9767, respectively. Compared with the univariate method, the proposed method improves detection accuracy. In addition, it was also applied to commercial polysaccharides and Glycyrrhiza uralensis polysaccharides (GUPs), and the results indicated that the PLS model was suitable for simultaneous determination of neutral and uronic sugars. © 2016 Published by Elsevier B.V.
1. Introduction Polysaccharides isolated from natural sources (plants, microorganisms, algae, and animals) have emerged as an important group of bioactive natural products [1,2]. Previous studies indicated that the polysaccharides containing more uronic acid exhibited stronger antioxidant activities [3,4]. Therefore, to determine the neutral and uronic sugar contents in polysaccharides is an important step in characterizing the polysaccharides and is also essential to the understanding of their functions [5,6]. To measure neutral and uronic sugars in polysaccharides, a large number of methods including chromatography [7], mass spectrum [8,9] and capillary electrophoresis [10] have been developed. However, these techniques require expensive instruments, advanced analytical skills and a considerable amount of time. For general purposes, it is important to develop rapid and robust methods that are directly applicable to crude or simply pretreated polysaccharides. UV–vis spectrometry is widely employed to determine sugars in polysaccharides as a typical method. The theory of the UV–vis spectrometric method for sugar detection is established on the reaction between hydrolyzed carbohydrate solutions and
∗ Corresponding author. Fax: +86 731 88830831. E-mail address: yizeng [email protected] (Y.-Z. Liang). http://dx.doi.org/10.1016/j.ijbiomac.2016.02.066 0141-8130/© 2016 Published by Elsevier B.V.
a coloring reagent that can be detected in the UV–vis range of the electromagnetic spectrum. The commonly used reagents for color development include phenol [11], anthrone [12], carbazole [13] and meta-hydroxydiphenyl (MHDP) [14]. But the traditional UV–vis spectrometry method had some potential problems. For example, it cannot simultaneously detect neutral and uronic sugars. In addition, neutral sugars and uronic acid from acidic polysaccharides interfere with each other during the colorimetric reaction. As a consequence, the analytical accuracy of this method is reduced. To improve the accuracy of the UV–vis method, Caroline et al. have reported an anthrone method based on double absorbance reading [15]. However, Caroline’s method is complicated, which requires of four linear regression equations and combination of the slopes obtained from the four equations. Recently, a quantitative chemometric method, partial least squares (PLS) has particularly attracted researchers’ attention at a rapid pace. In many articles, the applications of spectrometry combined with PLS to the determination of multi-component substances have been reported. This approach has also been evaluated by multiple researchers as a convenient technique [16–20]. The aim of this paper is to establish a method, UV–vis spectrometry combined with PLS, for simultaneous determination of neutral and uronic sugars.
C.-H. Zhang et al. / International Journal of Biological Macromolecules 87 (2016) 290–294
2. Experimental and methods 2.1. Chemicals Anthrone, meta-hydroxydiphenyl (MHDP), glucose and glucuronic acid (GlcA) were supplied by Sigma-Aldrich (Sigma, St. Louis, MO, USA). Glycyrrhiza uralensis polysaccharides (GUPs) were prepared in our laboratory, and other reagents including H2 SO4 and phenol were of analytical grade from Peking Chemical Co. (Peking, China). The water used in all tests was treated in a Milli-Q water purification system (Millipore, Bedford, MA, USA). 2.2. Chemical method The reference method was established on anthrone [12] with some modifications. The anthrone reagent of 2 g/L was prepared by dissolving anthrone in concentrated sulphuric acid prior to use. A volume of 4 mL anthrone reagent was added to 1 mL of the sample in the tube, and the mixture was agitated by vortex. Then, the tube was immersed in a boiling water bath for 7 min, followed by an icewater bath until room temperature was reached. The absorption spectrum of the mixture was then scanned by UV–vis spectroscopy. 2.3. Standard solutions Stock solutions of glucose (0.5 g/L) and GlcA (0.5 g/L) were prepared. Standard series were tested for the glucose concentrations between 20 g/mL and 100 g/mL and the GlcA concentrations within the range of 10–50 g/mL. Binary standard solutions (GlcA with glucose) were also tested at the concentrations between 20 g/mL and 100 g/mL for glucose and between 10 g/mL and 50 g/mL for GlcA.
291
Table 1 Parameters of the linear univariate calibration equations. Parameter
Glucose
GlcA
max (nm) Linear range (g/mL) Intercept Slope Correlation coefficient (R2 )
620 20–100 0.0924 0.0076 0.9984
560 10–50 0.1128 0.0021 0.9958
Table 2 The 25 designed sample solutions by using full factor design (52 ). No. of solution
Glucose (g/mL)
GlcA (g/mL)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
20 20 20 20 20 40 40 40 40 40 60 60 60 60 60 80 80 80 80 80 100 100 100 100 100
10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50
2.4. Procedures 2.4.1. Univariate calibration To identify the dynamic linear concentration range of each compound, univariate calibration was performed. Standard series of glucose and GlcA were detected by the anthrone method, and the results are shown in Table 1.
Fig. 1. Scanning curves of (a) 50 g/mL of GlcA, (b) 50 g/mL of glucose and (c) mixture of GlcA and glucose subjected to anthrone reaction. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)
2.4.2. Mixture design The solutions involving a mixture of analytes were used in the PLS model, the performance of which is dependent on the standard mixture solutions as well as the presence or absence of outliers. The following important factors should be examined in preparing these standard solutions: (1) The concentration of each compound must be bound to their respective linear dynamic ranges. (2) To give the most information from the analytical system, the concentrations of the compounds in the calibration samples must be orthogonal. (3) The absorbance of the compounds in a given mixture must not exceed the maximum absorbance of the instrument. (4) The concentrations of the prediction mixtures should span the same space as that of the calibration mixtures [21]. With these principles considered, the linear dynamic ranges obtained by univariate calibration were used to prepare binary standard mixtures. The composition of the calibration set was constructed according to a five-level full factor design. The specific data are listed in Table 2, where the spectrum was recorded between 200 nm and 800 nm (at 0.5 nm intervals). The region of 470–700 nm, which implies working with 460 experimental data points per spectrum, was selected for analysis because this region contains suitable spectral information from the interesting component mixtures. For model assessment, 20 random test mixtures were used within the linear dynamic ranges. 2.4.3. Calibration and prediction Principal component analysis (PCA) was used to check the homogeneity between calibration and prediction sets. The calibration model was built by the PLS algorithm. The optimum number of PLS factors was determined by a 10-fold cross-validation proce-
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Table 3 Parameters of PLS models. Parameters
Neutral sugars
Uronic sugars
RMSECV Factors
1.1997 8
3.0116 9
Calibration Rc 2 RMSEC
0.9997 0.5207
0.9981 0.6240
Prediction Rp 2 RMSEP
0.9971 1.2233
0.9767 1.9367
dure [22]. Then, an external prediction step was performed after the aforementioned calibration procedure. 2.4.4. Comparison with conventional UV–vis spectrometry methods The prediction samples were detected by conventional spectrometry methods as well. The phenol method [11] was used to detect neutral sugars, while the MHDP method [14] was used to detect uronic acid. 2.4.5. Evaluation of the model performance The best calibration model was selected for each analysis regarding the root-mean-square error of cross-validation (RMSECV), root-mean-square error (RMSE), and correlation coefficient for the calibration set (Rc 2 ) and correlation coefficient for the prediction set (Rp 2 ). By using the sample sets, the RMSE were expressed in details as the root-mean-square error of calibration (RMSEC) for the calibration set and root-mean-square error of prediction (RMSEP) for the prediction set, respectively [23]. Good models should have low RMSEC, RMSEP and RMSECV and high correlation coefficients. Besides, the differences between RMSEP and RMSEC should be small. 2.5. Partial least squares (PLS) theory PLS regression modeling is one of the most powerful multivariate statistical tools for quantitative analysis [24]. The main principle of this method is briefly given below. PLS is a quantitative spectral
decomposition technique that is closely related to principal component regression. However, the decomposition is performed in a slightly different fashion for PLS. Instead of decomposing lonely the spectral matrix into a set of eigenvectors (loadings) and then regressing using the eigenvectors obtained (as done in PCR), PLS actually uses the concentration information during every step in the decomposition process. Thus, the eigenvectors calculated by PLS, including the loadings and scores, are slightly different from those by PCR. The key idea of PLS is to obtain as much concentration information as possible into the first few loading and score vectors. There are actually two types of PLS algorithms, which are PLS 1 and PLS 2. Their differences are subtle but have very important effects on the results. In PLS 1, a separate set of score and loading vectors is calculated from each constituent of the interests. In this case, a separate set of score and loading vectors is specifically tuned for each constituent and therefore gives better prediction than PCR and PLS 2 [25]. Here we used PLS 1 and denoted it as PLS throughout this paper. 2.6. Apparatus and software The spectra were obtained on a Shimadzu UV-2450 spectrometer using a 10 mm quartz cell. The data were processed with MATLAB (The MathWorks, Natick, USA). 3. Results and discussion 3.1. Absorbance spectra of the system The ability of anthrone to react with the neutral and uronic sugars commonly existed in polysaccharide samples was evaluated by submitting pure standard solutions of glucose and GlcA to the anthrone method as described in the “Experimental and methods” section. Scanning of these solutions was monitored at the wavelengths ranging from 470 nm to 700 nm (Fig. 1). The spectral analysis suggested that the maximum absorption amounts of glucose and GlcA were at 620 nm and 560 nm, respectively. It could also be viewed that the mixture’s spectrum was very similar to the glucose’s. This hinders simultaneous determination of neutral and uronic sugars. To overcome this problem, PLS was used to establish a multivariate calibration model.
Table 4 Comparison of neutral and uronic sugars determination by PLS calibration and univariate for the prediction mixtures. Mixture
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ARE
Actual value
Found value (PLS)
Found value (univariate)
NS(g/mL)
US(g/mL)
NS(g/mL)
RE(%)
US(g/mL)
RE(%)
NS(g/mL)
RE(%)
US(g/mL)
RE(%)
25 25 30 30 35 40 50 50 55 55 60 70 70 75 75 85 85 90 90 95
15 45 10 15 40 35 25 45 45 15 25 30 40 20 10 35 45 15 30 45
24.99 24.28 31.97 30.59 35.17 41.56 50.07 50.44 54.15 55.22 60.41 70.95 71.03 75.21 74.42 84.66 85.71 88.38 89.16 91.09
0.04 2.88 6.57 1.97 0.49 3.90 0.14 0.88 1.55 0.40 0.68 1.36 1.47 0.28 0.77 0.40 0.84 1.80 0.93 4.12 1.57
17.59 43.72 13.93 13.58 42.16 33.28 25.92 42.05 46.72 16.94 26.67 32.35 38.74 21.23 10.35 37.21 46.83 14.67 31.42 46.85
17.27 2.84 39.30 9.47 5.40 4.91 3.68 6.56 3.82 12.93 6.68 7.83 3.15 6.15 3.50 6.31 4.07 2.20 4.73 4.11 7.75
34.57 36.43 36.12 38.16 47.28 52.35 62.43 71.4 65.52 63.66 68.77 80.09 85.41 91.25 84.67 96.82 99.78 98.43 104.56 107.94
38.28 45.72 20.40 27.20 35.09 30.88 24.86 42.80 19.13 15.75 14.62 14.41 22.01 21.67 12.89 13.91 17.39 9.37 16.18 13.62 22.81
17.6 46.23 12.34 17.69 42.43 37.86 27.13 46.46 46.52 16.57 27.13 32.38 47.21 22.46 12.45 37.52 46.98 17.07 32.64 46.32
17.33 2.73 23.40 17.93 6.08 8.17 8.52 3.24 3.38 10.47 8.52 7.93 18.03 12.30 24.50 7.20 4.40 13.80 8.80 2.93 10.48
NS: neutral sugars; US: uronic sugars; RE: relative error; ARE: average relative error
C.-H. Zhang et al. / International Journal of Biological Macromolecules 87 (2016) 290–294
293
Table 5 Application of the PLS method for the estimation of neutral sugars and uronic acid in polysaccharide samples. PLS Neutral sugars Xanthan gum Gellan gum GUPs
71.3 ± 1.2 72.1 ± 1.6 63.5 ± 2.3
Real value Uronic acid 21.2 ± 1.8 23.8 ± 2.5 33.9 ± 1.3
Neutral sugars
Uronic acid
80 75 65
20 25 35
GUPs: glycyrrhiza uralensis polysaccharides. The values are % w/w.
prediction sets were present in the factor space of the calibration sets and also corresponded to the trends of the calibration sets. 3.3. PLS model building
Fig. 2. Score plots for the absorbance data matrices of the calibration and prediction sets.
3.2. Principal component analysis PCA was used to observe the homogeneity between the calibration and prediction sets. To implement this method, the UV spectrum data matrices (470–700 nm) of the calibration (red dots) and prediction (blue dots) sets were separately subjected to PCA. Fig. 2 shows the plot of the first principal component (PC 1) against the second one (PC 2) for both of the calibration and prediction sets. As shown in the figure, the calibration sets were evenly distributed without obvious clusters. Therefore, all of the calibration samples can be used to build a PLS model. In addition, the calibration sets have two trends in PCA. Firstly, with the same glucose concentration, PC 2 decreases with the increase of the GlcA concentration. Secondly, with the same GlcA concentration, PC 1 increases with the increase of the glucose concentration. By observing and comparing the calibration and prediction sets, it was found that the
3.3.1. Selection of the optimum factors For the PLS algorithm, to select appropriate factors is of great importance, which determine the prediction effect of the calibration model. If the number of factors used in this model is small, it will cause under-fitting. If the number of selected factors is excessively large, however, over-fitting will occur [26]. In this paper, a 10-fold cross-validation procedure was used to select the number of factors [22]. Fig. 3 shows the RMSECV obtained by optimizing the calibration sets of the absorbance data with the PLS method. Here, the optimal numbers of selected factors for the calibration models were 8 for neutral sugars and 9 for uronic sugars. 3.3.2. Calibration results With the optimized factors, the PLS models for neutral and uronic sugars were developed. The parameters of the PLS calibration models are shown in Table 3. As it can be seen, the Rc 2 for the neutral sugar and uronic sugar models were 0.9997 and 0.9981 and the RMSEC were 0.5207 and 0.6240, indicating that the calibration performance of the models was quite satisfactory. 3.3.3. Validation results To check the robustness of the PLS calibration models, the models were applied to an independent prediction set (nine test
Fig. 3. Plot of RMSECV vs. number of factors. (a) neutral sugars; (b) uronic sugars.
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mixtures), which was different from those upon which the calibration models were built. The assessment results are presented in Table 3. The RMSEP values are 1.2233 and 1.9367 for neutral and uronic sugars, respectively. The Rp 2 values are 0.9971 and 0.9767 for neutral and uronic sugars, respectively. The results demonstrate that the proposed procedure allows simultaneous determination of neutral sugars and uronic acid. 3.4. Comparison with conventional UV–vis spectrometry methods To evaluate the effects of the PLS models, independent prediction sets were also detected by univariate methods. The results are shown in Table 4. Comparing the values and average relative errors (ARE) of the two methods, it can be seen that better results were obtained by the proposed PLS method. Especially, the detection values of neutral sugars show great improvement. These satisfactory results revealed that the proposed method would not only be suitable for simultaneous determination of neutral and uronic sugars, but also improve the accuracy of the detection results. 3.5. Applications of the proposed method to commercial samples and GUPs The proposed PLS method was also applied to the determination of neutral and uronic sugars in real samples. Table 5 lists the values of neutral and uronic sugars obtained by using the proposed method and the corresponding values reported by scientific literatures for xanthan gum, gellan gum [27] and GUPs [28]. As we can observe, the two types of results are comparable even though there are few differences, indicating that the PLS method could be used in carbohydrates studies. 4. Conclusion The method of UV–vis spectrometry combined with partial least squares (PLS) for simultaneous determination of neutral sugars and uronic acid was established in this paper. Compared with the univariate method, the proposed method can improve the detection accuracy. As a rapid and relatively inexpensive method, it greatly facilitates simultaneous determination of neutral and uronic sugars in polysaccharides.
Acknowledgement This work is financially supported by the National Nature Foundation Committee of P.R. China (grants nos. 21275164, 21075138). References [1] J.M. Francois, Nat. Protoc. 1 (2006) 2995–3000. [2] L.Q. Yang, L.M. Zhang, Carbohydr. Polym. 76 (2009) 349–361. [3] J.W. Li, Y.F. Liu, L.P. Fan, L.Z. Ai, L.A. Shan, Carbohydr. Polymr 84 (2011) 390–394. [4] Y.X. Sun, J.C. Liu, J.F. Kennedy, Carbohydr. Polym. 82 (2010) 299–304. [5] X.L. Wei, M.A. Chen, J.B. Xiao, Y. Liu, L. Yu, H. Zhang, Y.F. Wang, Carbohydr. Polym 79 (2010) 418–422. [6] N. Volpi, F. Maccari, Glycobiology 19 (2009) 356–367. [7] H.X. Chen, M. Zhang, B.J. Xie, J. Agric. Food Chem. 52 (2004) 3333–3336. [8] Y. Zhang, P. Zhang, Z.F. Wang, L.J. Huang, J. Liq. Chromatogr. R T 34 (2011) 1754–1771. [9] Z.W. Sun, C.H. Song, L. Xia, X.Y. Wang, Y.R. Suo, J.M. You, Chromatographia 71 (2010) 789–797. [10] S. Cortacero-Ramirez, A. Segura-Carretero, C. Cruces-Blanco, M.H.B. de Castro, A. Fernandez-Gutierrez, Food Chem. 87 (2004) 471–476. [11] M. Dubois, K.A. Gilles, J.K. Hamilton, P.A. Rebers, F. Smith, Anal. Chem. (1956) 350–356. [12] R. Dreywood, Ind. Eng. Chem. Anal. Ed. 18 (1946) 499. [13] Z. Dische, J. Biol. Chem. 167 (1947) 189–198. [14] N. Blumenkrantz, G. Asboe-Hansen, Anal. Biochem. 54 (1973) 484–489. [15] C. Rondel, C.E. Marcato-Romain, E. Girbal-Neuhauser, Water Res. 47 (2013) 2901–2908. [16] J.L. Aleixandre-Tudo, H. Nieuwoudt, J.L. Aleixandre, W.J. Du Toit, J. Agric. Food Chem. 63 (2015) 1088–1098. [17] F. Bourdon, M. Lecoeur, P. Odou, C. Vaccher, C. Foulon, Talanta 120 (2014) 274–282. [18] D. Xu, W. Fan, H.Y. Lv, Y.Z. Liang, Y. Shan, G.Y. Li, Z.Y. Yang, L. Yu, Spectrochim. Acta A 123 (2014) 430–435. [19] A.P. do Nascimento, M.G. Trevisan, E.R.M. Kedor-Hackmann, R.J. Poppi, Anal. Lett. 40 (2007) 975–986. [20] J. Ghasemi, A. Niazi, Microchem. J. 68 (2001) 1–11. [21] B. Hemmateenejad, M. Akhond, F. Samari, Spectrochim. Acta A 67 (2007) 958–965. [22] Q.S. Xu, Y.Z. Liang, Chemometr. Intell. Lab. 56 (2001) 1–11. [23] J.Y. Shi, X.B. Zou, J.W. Zhao, M. Holmes, K.L. Wang, X. Wang, H. Chen, Spectrochim. Acta A 94 (2012) 271–276. [24] S. Wold, K. Esbensen, P. Geladi, Chemometr. Intell. Lab. 2 (1987) 37–52. [25] R. Manne, Chemometr. Intell. Lab. 2 (1987) 187–197. [26] S. Wold, Technometrics 20 (1978) 397–405. [27] L.W. Doner, Chromatographia 53 (2001) 579–581. [28] M. Tomoda, N. Shimizu, M. Kanari, R. Gonda, S. Arai, Y. Okuda, Chem. Pharm. Bull. 38 (1990) 1667–1671.
Contents lists available at ScienceDirect
International Journal of Biological Macromolecules journal homepage: www.elsevier.com/locate/ijbiomac
Simultaneous determination of neutral and uronic sugars based on UV–vis spectrometry combined with PLS Ci-Hai Zhang, Yong-Huan Yun, Zhi-Min Zhang, Yi-Zeng Liang ∗ College of Chemistry and Chemical engineering, Central South University, Changsha, 410083, China
a r t i c l e
i n f o
Article history: Received 15 October 2015 Received in revised form 24 February 2016 Accepted 25 February 2016 Available online 27 February 2016 Keywords: Partial least squares Neutral sugars Uronic sugars Simultaneous determination
a b s t r a c t A method using partial least squares (PLS) for simultaneous determination of neutral and uronic sugars was developed in this paper. This method is based on the development of the reaction between the analytes and anthrone. The calibration set was built with 25 binary solutions at the concentrations ranging from 20 to 100 g/mL for glucose and from 10 to 50 g/mL for glucuronic acid. An independent prediction set was utilized to check the robustness of the PLS calibration model. The root-mean-square error of prediction (RMSEP) values for neutral and uronic sugars are 1.2233 and 1.9367, respectively. The correlation coefficient for the prediction set (Rp 2 ) values for them are 0.9971 and 0.9767, respectively. Compared with the univariate method, the proposed method improves detection accuracy. In addition, it was also applied to commercial polysaccharides and Glycyrrhiza uralensis polysaccharides (GUPs), and the results indicated that the PLS model was suitable for simultaneous determination of neutral and uronic sugars. © 2016 Published by Elsevier B.V.
1. Introduction Polysaccharides isolated from natural sources (plants, microorganisms, algae, and animals) have emerged as an important group of bioactive natural products [1,2]. Previous studies indicated that the polysaccharides containing more uronic acid exhibited stronger antioxidant activities [3,4]. Therefore, to determine the neutral and uronic sugar contents in polysaccharides is an important step in characterizing the polysaccharides and is also essential to the understanding of their functions [5,6]. To measure neutral and uronic sugars in polysaccharides, a large number of methods including chromatography [7], mass spectrum [8,9] and capillary electrophoresis [10] have been developed. However, these techniques require expensive instruments, advanced analytical skills and a considerable amount of time. For general purposes, it is important to develop rapid and robust methods that are directly applicable to crude or simply pretreated polysaccharides. UV–vis spectrometry is widely employed to determine sugars in polysaccharides as a typical method. The theory of the UV–vis spectrometric method for sugar detection is established on the reaction between hydrolyzed carbohydrate solutions and
∗ Corresponding author. Fax: +86 731 88830831. E-mail address: yizeng [email protected] (Y.-Z. Liang). http://dx.doi.org/10.1016/j.ijbiomac.2016.02.066 0141-8130/© 2016 Published by Elsevier B.V.
a coloring reagent that can be detected in the UV–vis range of the electromagnetic spectrum. The commonly used reagents for color development include phenol [11], anthrone [12], carbazole [13] and meta-hydroxydiphenyl (MHDP) [14]. But the traditional UV–vis spectrometry method had some potential problems. For example, it cannot simultaneously detect neutral and uronic sugars. In addition, neutral sugars and uronic acid from acidic polysaccharides interfere with each other during the colorimetric reaction. As a consequence, the analytical accuracy of this method is reduced. To improve the accuracy of the UV–vis method, Caroline et al. have reported an anthrone method based on double absorbance reading [15]. However, Caroline’s method is complicated, which requires of four linear regression equations and combination of the slopes obtained from the four equations. Recently, a quantitative chemometric method, partial least squares (PLS) has particularly attracted researchers’ attention at a rapid pace. In many articles, the applications of spectrometry combined with PLS to the determination of multi-component substances have been reported. This approach has also been evaluated by multiple researchers as a convenient technique [16–20]. The aim of this paper is to establish a method, UV–vis spectrometry combined with PLS, for simultaneous determination of neutral and uronic sugars.
C.-H. Zhang et al. / International Journal of Biological Macromolecules 87 (2016) 290–294
2. Experimental and methods 2.1. Chemicals Anthrone, meta-hydroxydiphenyl (MHDP), glucose and glucuronic acid (GlcA) were supplied by Sigma-Aldrich (Sigma, St. Louis, MO, USA). Glycyrrhiza uralensis polysaccharides (GUPs) were prepared in our laboratory, and other reagents including H2 SO4 and phenol were of analytical grade from Peking Chemical Co. (Peking, China). The water used in all tests was treated in a Milli-Q water purification system (Millipore, Bedford, MA, USA). 2.2. Chemical method The reference method was established on anthrone [12] with some modifications. The anthrone reagent of 2 g/L was prepared by dissolving anthrone in concentrated sulphuric acid prior to use. A volume of 4 mL anthrone reagent was added to 1 mL of the sample in the tube, and the mixture was agitated by vortex. Then, the tube was immersed in a boiling water bath for 7 min, followed by an icewater bath until room temperature was reached. The absorption spectrum of the mixture was then scanned by UV–vis spectroscopy. 2.3. Standard solutions Stock solutions of glucose (0.5 g/L) and GlcA (0.5 g/L) were prepared. Standard series were tested for the glucose concentrations between 20 g/mL and 100 g/mL and the GlcA concentrations within the range of 10–50 g/mL. Binary standard solutions (GlcA with glucose) were also tested at the concentrations between 20 g/mL and 100 g/mL for glucose and between 10 g/mL and 50 g/mL for GlcA.
291
Table 1 Parameters of the linear univariate calibration equations. Parameter
Glucose
GlcA
max (nm) Linear range (g/mL) Intercept Slope Correlation coefficient (R2 )
620 20–100 0.0924 0.0076 0.9984
560 10–50 0.1128 0.0021 0.9958
Table 2 The 25 designed sample solutions by using full factor design (52 ). No. of solution
Glucose (g/mL)
GlcA (g/mL)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
20 20 20 20 20 40 40 40 40 40 60 60 60 60 60 80 80 80 80 80 100 100 100 100 100
10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50
2.4. Procedures 2.4.1. Univariate calibration To identify the dynamic linear concentration range of each compound, univariate calibration was performed. Standard series of glucose and GlcA were detected by the anthrone method, and the results are shown in Table 1.
Fig. 1. Scanning curves of (a) 50 g/mL of GlcA, (b) 50 g/mL of glucose and (c) mixture of GlcA and glucose subjected to anthrone reaction. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)
2.4.2. Mixture design The solutions involving a mixture of analytes were used in the PLS model, the performance of which is dependent on the standard mixture solutions as well as the presence or absence of outliers. The following important factors should be examined in preparing these standard solutions: (1) The concentration of each compound must be bound to their respective linear dynamic ranges. (2) To give the most information from the analytical system, the concentrations of the compounds in the calibration samples must be orthogonal. (3) The absorbance of the compounds in a given mixture must not exceed the maximum absorbance of the instrument. (4) The concentrations of the prediction mixtures should span the same space as that of the calibration mixtures [21]. With these principles considered, the linear dynamic ranges obtained by univariate calibration were used to prepare binary standard mixtures. The composition of the calibration set was constructed according to a five-level full factor design. The specific data are listed in Table 2, where the spectrum was recorded between 200 nm and 800 nm (at 0.5 nm intervals). The region of 470–700 nm, which implies working with 460 experimental data points per spectrum, was selected for analysis because this region contains suitable spectral information from the interesting component mixtures. For model assessment, 20 random test mixtures were used within the linear dynamic ranges. 2.4.3. Calibration and prediction Principal component analysis (PCA) was used to check the homogeneity between calibration and prediction sets. The calibration model was built by the PLS algorithm. The optimum number of PLS factors was determined by a 10-fold cross-validation proce-
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Table 3 Parameters of PLS models. Parameters
Neutral sugars
Uronic sugars
RMSECV Factors
1.1997 8
3.0116 9
Calibration Rc 2 RMSEC
0.9997 0.5207
0.9981 0.6240
Prediction Rp 2 RMSEP
0.9971 1.2233
0.9767 1.9367
dure [22]. Then, an external prediction step was performed after the aforementioned calibration procedure. 2.4.4. Comparison with conventional UV–vis spectrometry methods The prediction samples were detected by conventional spectrometry methods as well. The phenol method [11] was used to detect neutral sugars, while the MHDP method [14] was used to detect uronic acid. 2.4.5. Evaluation of the model performance The best calibration model was selected for each analysis regarding the root-mean-square error of cross-validation (RMSECV), root-mean-square error (RMSE), and correlation coefficient for the calibration set (Rc 2 ) and correlation coefficient for the prediction set (Rp 2 ). By using the sample sets, the RMSE were expressed in details as the root-mean-square error of calibration (RMSEC) for the calibration set and root-mean-square error of prediction (RMSEP) for the prediction set, respectively [23]. Good models should have low RMSEC, RMSEP and RMSECV and high correlation coefficients. Besides, the differences between RMSEP and RMSEC should be small. 2.5. Partial least squares (PLS) theory PLS regression modeling is one of the most powerful multivariate statistical tools for quantitative analysis [24]. The main principle of this method is briefly given below. PLS is a quantitative spectral
decomposition technique that is closely related to principal component regression. However, the decomposition is performed in a slightly different fashion for PLS. Instead of decomposing lonely the spectral matrix into a set of eigenvectors (loadings) and then regressing using the eigenvectors obtained (as done in PCR), PLS actually uses the concentration information during every step in the decomposition process. Thus, the eigenvectors calculated by PLS, including the loadings and scores, are slightly different from those by PCR. The key idea of PLS is to obtain as much concentration information as possible into the first few loading and score vectors. There are actually two types of PLS algorithms, which are PLS 1 and PLS 2. Their differences are subtle but have very important effects on the results. In PLS 1, a separate set of score and loading vectors is calculated from each constituent of the interests. In this case, a separate set of score and loading vectors is specifically tuned for each constituent and therefore gives better prediction than PCR and PLS 2 [25]. Here we used PLS 1 and denoted it as PLS throughout this paper. 2.6. Apparatus and software The spectra were obtained on a Shimadzu UV-2450 spectrometer using a 10 mm quartz cell. The data were processed with MATLAB (The MathWorks, Natick, USA). 3. Results and discussion 3.1. Absorbance spectra of the system The ability of anthrone to react with the neutral and uronic sugars commonly existed in polysaccharide samples was evaluated by submitting pure standard solutions of glucose and GlcA to the anthrone method as described in the “Experimental and methods” section. Scanning of these solutions was monitored at the wavelengths ranging from 470 nm to 700 nm (Fig. 1). The spectral analysis suggested that the maximum absorption amounts of glucose and GlcA were at 620 nm and 560 nm, respectively. It could also be viewed that the mixture’s spectrum was very similar to the glucose’s. This hinders simultaneous determination of neutral and uronic sugars. To overcome this problem, PLS was used to establish a multivariate calibration model.
Table 4 Comparison of neutral and uronic sugars determination by PLS calibration and univariate for the prediction mixtures. Mixture
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ARE
Actual value
Found value (PLS)
Found value (univariate)
NS(g/mL)
US(g/mL)
NS(g/mL)
RE(%)
US(g/mL)
RE(%)
NS(g/mL)
RE(%)
US(g/mL)
RE(%)
25 25 30 30 35 40 50 50 55 55 60 70 70 75 75 85 85 90 90 95
15 45 10 15 40 35 25 45 45 15 25 30 40 20 10 35 45 15 30 45
24.99 24.28 31.97 30.59 35.17 41.56 50.07 50.44 54.15 55.22 60.41 70.95 71.03 75.21 74.42 84.66 85.71 88.38 89.16 91.09
0.04 2.88 6.57 1.97 0.49 3.90 0.14 0.88 1.55 0.40 0.68 1.36 1.47 0.28 0.77 0.40 0.84 1.80 0.93 4.12 1.57
17.59 43.72 13.93 13.58 42.16 33.28 25.92 42.05 46.72 16.94 26.67 32.35 38.74 21.23 10.35 37.21 46.83 14.67 31.42 46.85
17.27 2.84 39.30 9.47 5.40 4.91 3.68 6.56 3.82 12.93 6.68 7.83 3.15 6.15 3.50 6.31 4.07 2.20 4.73 4.11 7.75
34.57 36.43 36.12 38.16 47.28 52.35 62.43 71.4 65.52 63.66 68.77 80.09 85.41 91.25 84.67 96.82 99.78 98.43 104.56 107.94
38.28 45.72 20.40 27.20 35.09 30.88 24.86 42.80 19.13 15.75 14.62 14.41 22.01 21.67 12.89 13.91 17.39 9.37 16.18 13.62 22.81
17.6 46.23 12.34 17.69 42.43 37.86 27.13 46.46 46.52 16.57 27.13 32.38 47.21 22.46 12.45 37.52 46.98 17.07 32.64 46.32
17.33 2.73 23.40 17.93 6.08 8.17 8.52 3.24 3.38 10.47 8.52 7.93 18.03 12.30 24.50 7.20 4.40 13.80 8.80 2.93 10.48
NS: neutral sugars; US: uronic sugars; RE: relative error; ARE: average relative error
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Table 5 Application of the PLS method for the estimation of neutral sugars and uronic acid in polysaccharide samples. PLS Neutral sugars Xanthan gum Gellan gum GUPs
71.3 ± 1.2 72.1 ± 1.6 63.5 ± 2.3
Real value Uronic acid 21.2 ± 1.8 23.8 ± 2.5 33.9 ± 1.3
Neutral sugars
Uronic acid
80 75 65
20 25 35
GUPs: glycyrrhiza uralensis polysaccharides. The values are % w/w.
prediction sets were present in the factor space of the calibration sets and also corresponded to the trends of the calibration sets. 3.3. PLS model building
Fig. 2. Score plots for the absorbance data matrices of the calibration and prediction sets.
3.2. Principal component analysis PCA was used to observe the homogeneity between the calibration and prediction sets. To implement this method, the UV spectrum data matrices (470–700 nm) of the calibration (red dots) and prediction (blue dots) sets were separately subjected to PCA. Fig. 2 shows the plot of the first principal component (PC 1) against the second one (PC 2) for both of the calibration and prediction sets. As shown in the figure, the calibration sets were evenly distributed without obvious clusters. Therefore, all of the calibration samples can be used to build a PLS model. In addition, the calibration sets have two trends in PCA. Firstly, with the same glucose concentration, PC 2 decreases with the increase of the GlcA concentration. Secondly, with the same GlcA concentration, PC 1 increases with the increase of the glucose concentration. By observing and comparing the calibration and prediction sets, it was found that the
3.3.1. Selection of the optimum factors For the PLS algorithm, to select appropriate factors is of great importance, which determine the prediction effect of the calibration model. If the number of factors used in this model is small, it will cause under-fitting. If the number of selected factors is excessively large, however, over-fitting will occur [26]. In this paper, a 10-fold cross-validation procedure was used to select the number of factors [22]. Fig. 3 shows the RMSECV obtained by optimizing the calibration sets of the absorbance data with the PLS method. Here, the optimal numbers of selected factors for the calibration models were 8 for neutral sugars and 9 for uronic sugars. 3.3.2. Calibration results With the optimized factors, the PLS models for neutral and uronic sugars were developed. The parameters of the PLS calibration models are shown in Table 3. As it can be seen, the Rc 2 for the neutral sugar and uronic sugar models were 0.9997 and 0.9981 and the RMSEC were 0.5207 and 0.6240, indicating that the calibration performance of the models was quite satisfactory. 3.3.3. Validation results To check the robustness of the PLS calibration models, the models were applied to an independent prediction set (nine test
Fig. 3. Plot of RMSECV vs. number of factors. (a) neutral sugars; (b) uronic sugars.
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mixtures), which was different from those upon which the calibration models were built. The assessment results are presented in Table 3. The RMSEP values are 1.2233 and 1.9367 for neutral and uronic sugars, respectively. The Rp 2 values are 0.9971 and 0.9767 for neutral and uronic sugars, respectively. The results demonstrate that the proposed procedure allows simultaneous determination of neutral sugars and uronic acid. 3.4. Comparison with conventional UV–vis spectrometry methods To evaluate the effects of the PLS models, independent prediction sets were also detected by univariate methods. The results are shown in Table 4. Comparing the values and average relative errors (ARE) of the two methods, it can be seen that better results were obtained by the proposed PLS method. Especially, the detection values of neutral sugars show great improvement. These satisfactory results revealed that the proposed method would not only be suitable for simultaneous determination of neutral and uronic sugars, but also improve the accuracy of the detection results. 3.5. Applications of the proposed method to commercial samples and GUPs The proposed PLS method was also applied to the determination of neutral and uronic sugars in real samples. Table 5 lists the values of neutral and uronic sugars obtained by using the proposed method and the corresponding values reported by scientific literatures for xanthan gum, gellan gum [27] and GUPs [28]. As we can observe, the two types of results are comparable even though there are few differences, indicating that the PLS method could be used in carbohydrates studies. 4. Conclusion The method of UV–vis spectrometry combined with partial least squares (PLS) for simultaneous determination of neutral sugars and uronic acid was established in this paper. Compared with the univariate method, the proposed method can improve the detection accuracy. As a rapid and relatively inexpensive method, it greatly facilitates simultaneous determination of neutral and uronic sugars in polysaccharides.
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