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Simultaneous determination of pyruvate and acetate levels in xanthan biopolymer by infrared spectroscopy: effect of spectral pre-processing for solid-state analysis
Food Chemistry 124 (2011) 1124–1130
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Analytical Methods
Simultaneous determination of pyruvate and acetate levels in xanthan biopolymer by infrared spectroscopy: effect of spectral pre-processing for solid-state analysis Roya Tavallaie a, Zahra Talebpour a,*, Jila Azad a, Mohammad Reza Soudi b a b
Department of Chemistry, Faculty of Science, Alzahra University, Vanak, Tehran, Iran Department of Microbiology, Faculty of Science, Alzahra University, Vanak, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 9 January 2009 Received in revised form 13 May 2010 Accepted 7 July 2010
Keywords: Infrared spectroscopy Pyruvate Acetate Xanthan Partial least square regression Spectral pre-processing methods
a b s t r a c t A rapid, direct, and reagent-free procedure based on solid-state Fourier transform infrared spectroscopy (FT-IR) coupled with partial least squares (PLS) data analysis has been developed for simultaneous determination of pyruvate and acetate levels in a microbial xanthan biopolymer. The influences of various spectral pre-processing procedures were studied in order to eliminate effects caused by sample preparation. It was determined that the combination of first derivative and orthogonal signal correction pre-processing contributes to a significant increase in the predictive performance of PLS-1 regression models. By employing the wavenumber region 1320–1350 cm1 for pyruvate determination and 1500–1600 cm1 for acetate determination, the root mean square error of cross-validation (RMSECV) for pyruvate and acetate contents were obtained 0.13% and 0.29% w/w, respectively. Results of the proposed procedure for different real samples and those obtained by their reference methods were compared. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Xanthan is a heteropolysaccharide produced industrially by fermentation with the bacteria Xanthomonas campestris (Garcia-Ochoa, Santos, Casas, & Gomez, 2000). It has widespread commercial applications in food (Hernandez, Delegido, Alfaro, & Munoz, 2007; Mirhosseini, Tan, Hamid, & Yusof, 2008; Zhao, Zhao, Yang, & Cu, 2009), pharmaceutical (Pongjanyakul & Puttipipatkhachorn, 2007), and petrochemical (Baba Hamed & Belhadri, 2009) industries. This biopolymer contains pyruvate and acetate groups within its structure, and the content of these groups varies due to the condition of the post-fermentation process. Measurements of pyruvate and acetate contents are important because these two parameters have a significant effect on the rheological behaviour of xanthan solutions (Cheetham & Norma, 1989; Shatwell, Sutherland, & Ross-Murphy, 1990; Smith, Symes, Lawson, & Morris, 1984). Although several wet procedures have been described for the analysis of either acetate or pyruvate in xanthan (Taylor & Nasr-El-Din, 1993), reports on the simultaneous determination of these two groups in xanthan are limited to two analytical methods based on high performance liquid chromatography (HPLC) (Cheetham & Punruckvong, 1985) and nuclear magnetic resonance spectroscopy (NMR) (Cheetham & Mashimba, 1992). These meth-
* Corresponding author. Tel./fax: +98 21 88041344. E-mail addresses: [email protected], [email protected] (Z. Talebpour). 0308-8146/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2010.07.016
ods are capable of simpler assays than wet procedures but they are destructive, laborious, costly and some what unsatisfactory for routine analysis. As a result, the most common methods for the determination of pyruvate and acetate are still based on wet chemistry procedures namely the lactate dehydrogenase (Duckworth & Yaphe, 1970) and hydroxamic acid (Hestrin, 1949) methods, respectively. As a fast, accurate and easy technique, Fourier transfer infrared spectroscopy (FT-IR) is widely used in the analysis of carbohydrate polymers (Boulet, Williams, & Doco, 2007; Copikova et al., 2006; Kacurakova & Wilson, 2001; Yuen, Choi, Phillips, & Ma, 2009). The advantages of FT-IR could be made good used of deriving information from complex samples because of the development of chemometrics (Koca, Kocaoglu-Vurma, Harper, & Rodriguez-Saona, 2010; Xie, Ye, Liu, & Ying, 2009). Partial least squares regression (PLS) (Wold, Sjostrom, & Eriksson, 2001) is one of the most popular chemometric algorithms for calibration model creation owing to its simplicity and small volume of calculations, which can be performed with easily accessible statically software. In recent years, as growing interest is taken in addressing complex problems by FT– IR, the combination of spectral pre-processing methods with multivariate calibration techniques has drawn great attention and shown good prospects in constructing robust and parsimonious FT-IR calibration models. Due to the extremely low solubility of xanthan in common organic solvents employed in FT-IR spectrometry and the high viscosity of aqueous solutions of xanthan, this biopolymer cannot
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be readily analysed in solution. As such, it is necessary to obtain the infrared spectra directly from the solid samples. The different physical characteristics and inhomogenities of solid samples are responsible for the spectral variability present in FT-IR measurements. Thus, spectra can be pre-processed in order to improve multivariate calibration (Blanco, Castillo, Peinado, & Beneyto, 2007). This is achieved by traditional filtering methods for FT-IR spectra, including first and second derivative (D1, D2) (Savitzky & Golay, 1964), standard normal variate (SNV) (Barnes, Dhanoa, & Lister, 1989), and multiplicative signal correction (MSC) (Geladi, MacDougall, & Martens, 1985) methods that are used in order to eliminate effects caused by sample preparation. These methods are all applied to the spectral matrix. Therefore, it is difficult to exclude the irrelevant information whilst not removing the spectral variations related to system components. Orthogonal signal correction (OSC) was introduced by Wold and coworkers (Wold, Antii, Lindgren, & Ohman, 1998) to remove systematic variation from response matrix X that is unrelated or orthogonal to the property matrix Y. This treatment has been used to correct systematic spectral alterations such as baseline changes and multiplicative effects, which are independent of concentration. In the current work, a new method for the direct and simultaneous determination of pyruvate and acetate contents of microbial xanthan solid samples based on FT-IR measurements using a partial least squares regression model is proposed. The effect of different spectral pre-processing methods and spectral regions in improving the performance of the PLS-1 models are studied. The predictive ability of the final PLS models is also compared with those obtained by reference methods for real samples of xanthan. 2. Materials and methods 2.1. Materials Sodium pyruvate, sodium acetate and KBr (IR spectroscopy grade) were obtained from Merck (Darmstadt, Germany). Xanthan standard (free from pyruvate and acetate groups) was prepared in the laboratory as described in Section 2.3. Commercial xanthan samples, including food grade xanthan (CHEMCOLLOIDS, Cheshire, UK), drilling mud xanthan (IOOC, Tehran, Iran), and industrial grade xanthan (CPKelco, Leatherhead, UK), were used as received. 2.2. Instrumentation Spectra were recorded using a Bruker FT-IR spectrometer model Tensor 27 (Bremen, Germany) equipped with a TGS detector having a standard device for pellet preparation. The OPUS software (version 4.1, Bruker) was employed throughout the work. For each spectrum, 16 scans were performed at a resolution of 4 cm1 over the mid-IR range (4000–400 cm1). All of the spectra were recorded at the transmittance mode and collected spectra were rationed against air as a background. Prior to data analysis, the region corresponding to the absorption of atmospheric CO2 (2410–2295 cm1) was eliminated. Weight measurements were performed on a Sartorius balance (Goettingen, Germany) with a precision of ±0.01 mg. 2.3. Biosynthesis of xanthan and preparation of standard samples Xanthan was produced in a 2-L bench-top fermenter (BiostatB, B-Braun, Germany) using Xanthomonas campestris strain b82, a native isolate that was previously introduced (Soudi, Ebrahimi, & Sharyat Panahi, 2006). Preculture was prepared in yeast extract, malt extract, and glucose broth, and a synthetic medium containing sucrose, citric acid, and mineral salts was used as the standard
production medium. To separate bacterial cells, the product was diluted and centrifuged several times, and xanthan gum was extracted using isopropyl alcohol. To prepare the xanthan standard (free of pyruvate and acetate groups), pyruvate and acetate groups were removed by heating a solution of xanthan (5 g/L, pH 8) for 3 h at 95 °C (alkaline treatment) and then reneutralising. Preparation of the b82 and 1706 laboratory samples (S1 and S2) were performed with the same procedure described for the xanthan standard by using the Xanthomonas campes b82 and X. campes 1706DSMZ strains, respectively, without an alkaline treatment. 2.4. Pellet preparation All pellets were prepared by mixing KBr and sample in a 95–5% w/w proportion. The sample is defined as a mixture of different amounts of xanthan standard, sodium pyruvate, and sodium acetate. The method described in reference Dijiba, Zhang, and Niemczyk (2005) was adapted to ensure the reproducibility of the method and the homogeneity of samples. Forty milligrams of the final prepared mixture were used for each pellet, which was then poured into the sample holder of the FT-IR apparatus. The top surface of the sample was smoothed by careful pulling and pressed up to 6 tons of pressure for 1 min. Triplicate spectra for each pellet were averaged. A mixture containing 95% w/w KBr and 5% w/w xanthan standard was used as the blank. 2.5. Multivariate calibration method 2.5.1. Calibration and prediction sets A calibration set of 35 pellets was prepared by the addition of known aliquots of sodium pyruvate and sodium acetate to a xanthan standard covering an appropriate % (w/w) concentration level to provide sample characteristics similar to real samples of xanthan. This set was divided into factorial and random designs. In the factorial design, 30 calibration pellets were prepared using six concentration levels for pyruvate and five concentration levels for acetate spanning a concentration range of 1.0–3.7% and 1.5– 5.0% w/w, respectively (Nos. 1–30). The second group of calibration pellets (Nos. 31–35) was prepared by using random design with pyruvate and acetate concentrations range between 4.0–6.0% and 5.5–7.5% w/w, respectively. The prediction set with six pellets was built up to ensure a good reference range for the validation. The compositions of the calibration and prediction sets are shown in Table 1. To evaluate the precision of the proposed method, the pellet preparation step for one sample (2:3.5:95.5% w/w pyruvate:acetate:xanthan standard) was repeated 10 times. The proposed method was evaluated for the determination of the pyruvate and acetate contents in three commercial samples of Table 1 The concentration of pyruvate and acetate groups in the calibration (Nos. 1–35) and prediction (Nos. 1p–6p) sets. No. Pyruvate Acetate No. Pyruvate Acetate No. Pyruvate Acetate (% w/w) (% w/w) (% w/w) (% w/w) (% w/w) (% w/w) 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1.0 1.3 1.6 2.1 2.8 3.7 1.0 1.3 1.6 2.1 2.8 3.7 1.0 1.3
1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7 3.8
15 16 17 18 19 20 21 22 23 24 25 26 27 28
1.6 2.1 2.8 3.7 1.0 1.3 1.6 2.1 2.8 3.7 1.0 1.3 1.6 2.1
5.0 1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7
29 30 31 32 33 34 35
2.8 3.7 4.0 4.5 5.0 5.5 6.0
3.8 5.0 6.5 5.5 7.5 7.0 5.5
1p 2p 3p 4p 5p 6p
1.2 4.7 2.2 1.7 3.4 4.0
1.8 4.0 3.0 2.5 4.9 3.6
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xanthan (food, drilling mud, and industrial grade) and two synthetic laboratory xanthan samples (S1 and S2). The results were compared with the results of the reference methods. 2.5.2. Data analysis Triplicate spectra for each pellet were averaged, and the averaged spectra were subtracted from the spectra of the blank sample. In order to remove or reduce noise, offset, and/or bias, spectra were smoothed according to the procedure of Savitzky and Golay (1964) by means of a 23-point smoothing filter. Several spectral pre-processing methods and their combinations were also employed, giving rise to a total of seven pre-processing methods, including the raw data. These included: first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), orthogonal signal correction (OSC), D1– OSC, and D2–OSC. For investigation of the different spectral preprocessing methods, the full range of spectra was used. After choosing the optimised pre-processing method, several data intervals were analysed in order to establish the best models, including 1320–1350 and 1700–1800 cm1 for pyruvate, and 1380–1420 and 1500–1600 cm1 for acetate. The partial least squares approach (PLS-1) was used for building calibration models. The parameters of different spectral pre-processing methods (the number of OSC components), the spectral regions, and the number of latent variables were all optimised by leave-one-out (LOO) cross-validation on the calibration set (through RMSECV). The predictive ability of the optimum calibration model was evaluated by the root mean square error and correlation coefficient of prediction sets (RMSEP and R2pre , respectively). The parameters of RMSECV and RMSEP are defined as:
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 ^ i¼1 ðyni yi Þ RMSECV ¼ n sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn ^ 2 i¼1 ðyi yi Þ RMSEP ¼ n
where n is the number of samples in the calibration and prediction sets, and yi is the reference measurement result for the sample i in ^ni is the estimated result for sample i in the calibration set each set. y ^ is the estiwhen the model is constructed without sample i and y mated result of sample i in the prediction set. 2.5.3. Software The data pre-processing included SNV, MSC, and OSC and also PLS modelling was carried out using MATLAB V.6.1. (Math works, Natick, USA) and PLS-Toolbox 3.5 (Eigenvector Research, Inc., Manson, WA), as implemented in Matlab 6.1. 2.6. Reference methods 2.6.1. Lactate dehydrogenase method for pyruvate determination The lactate dehydrogenase method for the determination of pyruvate in polysaccharides (Duckworth & Yaphe, 1970) is known to be one of the most accurate methods for the determination of pyruvate in xanthan, and is commonly used for this purpose. In this method, the pyruvate group in xanthan is first hydrolysed using oxalic acid or dilute HCl, and then the amount of nicotinamide adenine dinucleotide (NAD)+ released is measured at 340 nm using lactate dehydrogenase (LDH). 2.6.2. Hydroxamic acid method for acetate determination In the hydroxamic acid method (Hestrin, 1949), the acetate ester in xanthan reacts with hydroxamic acid to produce an acetohydroxamic acid. This acid forms a water-soluble complex with iron that absorbs light at 520 nm. The method is applicable for the determination of 2–10 micromoles of acetate group per mL. 3. Results and discussion 3.1. FT-IR spectra Fig. 1 shows the spectra of xanthan, sodium pyruvate, and the sodium acetate standard in the full range of the mid-IR region with an air background. As is evident, intense absorption bands
Fig. 1. The spectrum of (a) xanthan, (b) sodium pyruvate, and (c) sodium acetate standards in the full range of mid-IR with air background. Each pellet consisted of KBr and sample with a 95–5% w/w proportion.
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corresponding to the pyruvate and acetate functional groups are observed in the range of 1000–1800 cm1. The spectra of pyruvate and acetate have similar band positions owing to the similarity of these functional groups. The obvious overlap between the pyruvate and acetate bands in this region allows for the use of multivariate regression for the simultaneous determination of pyruvate and acetate content in xanthan sample. 3.2. Multivariate calibration method 3.2.1. Rationale behind modelling approach Prior to developing models, it is essential to consider some other factors, as it is preferable to develop models with a clear analytical basis relating to each analyte. The difference between the molecular environment of synthetic calibration mixtures and real samples can influence the position of the characteristics bonds in their IR spectra. For this reason, a feasibility study (spiking experiment) was conducted using a set of spiked synthetic samples ranging from 0% to 11% w/w for each analyte (acetate and pyruvate) in order to comparing the position of the characteristic bands in synthetic and real samples. It was evident from this study that though there are some little differences between the spectral feature of either calibration mixtures with real samples or different real samples, which was produced by using different strains of X. campes, with each other, but all of them have a same pattern in the region of 1300–2000 cm1. 3.2.2. Effects of spectral pre-processing methods Fig. 2a indicated the FT-IR spectra of the calibration set (Table 1) after subtraction of the blank sample spectrum. Additionally, pretreatment average-smoothing and mean centreing spectral data were employed to remove noise and eliminate common spectral information. Consistent baseline offsets and biases were also present in the spectra due to differences in the scatter profile of the solid samples. These offsets and biases typically produce calibration models that require a larger number of factors or that bear lower predictive abilities (higher RMSECV). In order to minimise these features and eliminate sources of non-linearity, different spectral data pre-processing methods (D1, D2, SNV, MSC and OSC) were applied using the calibration set. The number of OSC components was changed from 1 to 4, and two components sufficed to remove the majority of the spectral variance not related to the analyte concentration. D1 and D2 effectively reduced the bias originally present in the spectra. However, as the derivatives are numerically calculated as differences in intensity in intervals of wavenumbers they have the detrimental effect of reducing the signal-to-noise ratio, especially when applied to noisy spectra (Roychoudhury, Harvey, & McNeil, 2006). This effect can be seen in Fig. 2b, which shows the D1 spectra of the calibration set. The OSC processed spectra are shown in Fig. 2c. It is evident that prior to OSC pre-treatment, all spectra overlapped, whilst after OSC correction, the difference between the spectra was more obvious. The multivariate statistical method of partial least squares (PLS) was employed to create calibration models to relate the spectra to the known content of pyruvate and acetate groups in xanthan. In order to investigate spectral pre-processing methods, data were analysed in the range of 400–4000 cm1 and full cross-validation was employed. Plots of the RMSECV versus latent variable (LV) for the pyruvate and acetate groups were obtained for the spectral pre-processing methods of D1, D2, SNV, MSC, OSC and their combinations. Table 2, part A displays the extracted results from RMSECV plot determined using different spectral pre-processing methods. The calibration models for pyruvate and acetate presented the lowest values for RMSECV for the spectra set when the D1, D2, and OSC were applied. These methods were more effective than the classi-
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cal pre-treatment methods, such as SNV and MSC. The greatest advantage of OSC is that it provides simpler models with a reduced number of PLS factors. Additionally, the combination of the D1 and D2 pre-processing with OSC was applied for the construction of calibration models (Table 2, part B). Subsequent evaluation of RMSECV indicated that the first derived OSC-treated data provided substantially lower values than others. Thus, this combination was chosen as the optimal spectral pre-processing method for the prediction of pyruvate and acetate levels in solid samples of xanthan by FT-IR. 3.2.3. Effect of spectral regions The selection of relevant spectral regions improved the results by reducing the contribution to the overall noise from these regions in the absence of relevant information about analyte concentrations. In order to find the best spectral region, different spectral intervals upon the characteristic bands of pyruvate and acetate were analysed. However, only the most significant results are detailed herein. The optimum region was selected based on the RMSECV of the D1–OSC pre-processing method for the calibration model, and the results are shown in Table 3. As evident, the optimal results for pyruvate and acetate determination were obtained by using wavenumber regions 1320–1350 and 1500–1600 cm1, respectively. As such, these regions were applied for construction of the final models. 3.2.4. Characteristics of optimum models The optimal models using PLS-1 employed in the optimal preprocessing method (D1–OSC), and selected regions with the lowest RMSECV were used to predict the pyruvate and acetate levels in the separate prediction sets. The optimums LVs that lead to the minimum values for error belong to factor numbers 5 and 3 for pyruvate and acetate, respectively. The RMSECV and RMSEP produced for pyruvate and acetate levels were 0.13%, 0.22% w/w and 0.29%, 0.35% w/w, respectively. The square of the correlation coefficient for calibration and prediction sets (R2cal , R2pre ) obtained for pyruvate and acetate were obtained at (0.9986, 0.9910) and (0.9514, 0.9332), respectively. Evidently, the correlation coefficients of the calibration and prediction sets for acetate are not as good as for pyruvate due to the lower overall peak sensitivity. In order to evaluate the precision of the method, 10 pellets of a sample containing 2% w/w pyruvate and 3.5% w/w acetate were prepared to verify the reproducibility of the obtained spectra. The triplicate spectra for each pellet were averaged, and pyruvate and acetate content was predicted using the optimal models. The relative inter-pellet standard deviation was calculated to be 12.7% and 16.6% for pyruvate and acetate, respectively. Additionally, the lower–upper limits of pyruvate and acetate at the 95% confidence interval were obtained at 2.03–2.37 and 3.16–3.84, respectively. 3.3. Analysis of xanthan real samples In order to verify the accuracy of the constructed PLS models, three commercial xanthans (food, drilling mud, and industrial grade) and two synthetic laboratory xanthans (S1, S2) containing pyruvate and acetate were analysed. The results obtained by the proposed methods were compared with those found by the aforementioned lactate dehydrogenase method (for pyruvate) and hydroxamic acid method (for acetate) (Table 4). The correlation coefficients (R2) between the mid-IR prediction value and the reference measurement for pyruvate and acetate content were determined to be 0.9544 and 0.9441, respectively. The results obtained by the proposed method for pyruvate and acetate content for varying real samples were statistically compared with those
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Fig. 2. The spectra of the calibration set after: (a) subtraction of the blank sample spectrum (none), (b) first derivative (D1), and (c) orthogonal signal correction (OSC).
obtained by their reference methods using analysis of variance (ANOVA). Comparison between D1–OSC–PLS and these methods
shows that these results are reliable within a 95% of confidence interval.
R. Tavallaie et al. / Food Chemistry 124 (2011) 1124–1130 Table 2 Analytical features of different spectral pre-processing methods employed for PLS-1 calculation on pyruvate and acetate content in xanthan. Pyruvate Pre-processing method A None D1 D2 SNV MSC OSC B D1–OSC D2–OSC a b c
Acetate
LVa
RMSECVb (% w/w)
R2cal c
LVs
RMSECV (% w/w)
R2cal
3 8 10 8 10 1
0.65 0.48 0.54 0.63 0.70 0.30
0.7879 0.9749 0.9692 0.9234 0.9097 0.9579
4 7 4 7 10 1
0.28 0.35 0.33 0.31 0.49 0.24
0.9295 0.9457 0.9016 0.9740 0.9650 0.9690
4 2
0.03 0.20
0.9998 0.9161
5 4
0.01 0.25
0.9999 0.9826
Number of latent variables. Root mean square error of cross-validation. Correlation between the predicted and expected values for calibration set.
Table 3 Analytical features of different spectral regions employed for PLS-1 calculation on pyruvate and acetate content in xanthan.
a b c
Region
Wavenumber (cm1)
LVsa
RMSECVb (% w/w)
R2cal c
Pyruvate 1 2 3
1320–1350 1700–1800 Combination of 1 and 2
5 6 6
0.13 0.24 0.14
0.9986 0.9761 0.9906
Acetate 1 2 3
1380–1420 1500–1600 Combination of 1 and 2
5 3 6
0.45 0.29 0.40
0.7878 0.9514 0.8434
Number of latent variables. Root mean square error of cross-validation. Correlation between the predicted and expected values for calibration set.
Table 4 Pyruvate and acetate levels in commercial and laboratory made xanthan samples using the optimum models. Real samples
Xanthan food grade Xanthan drilling mud Xanthan industrial grade S1 S2 a
Pyruvate (% w/w)
Acetate (% w/w)
D1–OSC– PLS method
Reference method
D1–OSC– PLS method
Reference method
3.42 ± 0.27a 2.98 ± 0.11 1.69 ± 0.41
3.75 ± 0.17 2.90 ± 0.24 2.07 ± 0.31
1.29 ± 0.59 1.66 ± 0.17 2.02 ± 0.63
1.98 ± 0.32 2.19 ± 0.19 3.07 ± 0.42
1.11 ± 0.15 1.21 ± 0.34
1.40 ± 0.86 1.80 ± 0.98
1.06 ± 0.42 1.20 ± 0.25
1.28 ± 0.19 1.50 ± 0.28
Mean ± standard deviation (n = 3).
4. Conclusion The results of the present study indicate that solid-state FT-IR measurements can be employed for direct determination of both pyruvate and acetate in xanthan biopolymer samples, both typically present as very minor components. The simplicity of the PLS procedure in combination with its low cost are significant advantages over related reference methods. This procedure is also rapid, efficient, and environment friendly, as the amount of organic solvents used during its quantitative determination process is sig-
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nificantly reduced. As such, this procedure might serve as an application for xanthan quality control in industry. We have further demonstrated that when care is taken to produce homogeneous calibration samples and optimised pre-processing methods are carried out, good results can be obtained by FT-IR measurements on KBr disks for quantitative purposes, a variation of the method that offers an alternative for analysis of poorly water-soluble biopolymers such as xanthan.
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Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130. Xie, L., Ye, X., Liu, D., & Ying, Y. (2009). Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS. Food Chemistry, 114, 1135–1140.
Yuen, S. N., Choi, S. M., Phillips, D. L., & Ma, C. Y. (2009). Raman and FTIR spectroscopic study of carboxymethylated non-starch polysaccharides. Food Chemistry, 114, 1091–1098. Zhao, Q., Zhao, M., Yang, B., & Cu, C. (2009). Effect of xanthan gum on the physical properties and textural characteristics of whipped cream. Food Chemistry, 116, 624–628.
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Analytical Methods
Simultaneous determination of pyruvate and acetate levels in xanthan biopolymer by infrared spectroscopy: effect of spectral pre-processing for solid-state analysis Roya Tavallaie a, Zahra Talebpour a,*, Jila Azad a, Mohammad Reza Soudi b a b
Department of Chemistry, Faculty of Science, Alzahra University, Vanak, Tehran, Iran Department of Microbiology, Faculty of Science, Alzahra University, Vanak, Tehran, Iran
a r t i c l e
i n f o
Article history: Received 9 January 2009 Received in revised form 13 May 2010 Accepted 7 July 2010
Keywords: Infrared spectroscopy Pyruvate Acetate Xanthan Partial least square regression Spectral pre-processing methods
a b s t r a c t A rapid, direct, and reagent-free procedure based on solid-state Fourier transform infrared spectroscopy (FT-IR) coupled with partial least squares (PLS) data analysis has been developed for simultaneous determination of pyruvate and acetate levels in a microbial xanthan biopolymer. The influences of various spectral pre-processing procedures were studied in order to eliminate effects caused by sample preparation. It was determined that the combination of first derivative and orthogonal signal correction pre-processing contributes to a significant increase in the predictive performance of PLS-1 regression models. By employing the wavenumber region 1320–1350 cm1 for pyruvate determination and 1500–1600 cm1 for acetate determination, the root mean square error of cross-validation (RMSECV) for pyruvate and acetate contents were obtained 0.13% and 0.29% w/w, respectively. Results of the proposed procedure for different real samples and those obtained by their reference methods were compared. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Xanthan is a heteropolysaccharide produced industrially by fermentation with the bacteria Xanthomonas campestris (Garcia-Ochoa, Santos, Casas, & Gomez, 2000). It has widespread commercial applications in food (Hernandez, Delegido, Alfaro, & Munoz, 2007; Mirhosseini, Tan, Hamid, & Yusof, 2008; Zhao, Zhao, Yang, & Cu, 2009), pharmaceutical (Pongjanyakul & Puttipipatkhachorn, 2007), and petrochemical (Baba Hamed & Belhadri, 2009) industries. This biopolymer contains pyruvate and acetate groups within its structure, and the content of these groups varies due to the condition of the post-fermentation process. Measurements of pyruvate and acetate contents are important because these two parameters have a significant effect on the rheological behaviour of xanthan solutions (Cheetham & Norma, 1989; Shatwell, Sutherland, & Ross-Murphy, 1990; Smith, Symes, Lawson, & Morris, 1984). Although several wet procedures have been described for the analysis of either acetate or pyruvate in xanthan (Taylor & Nasr-El-Din, 1993), reports on the simultaneous determination of these two groups in xanthan are limited to two analytical methods based on high performance liquid chromatography (HPLC) (Cheetham & Punruckvong, 1985) and nuclear magnetic resonance spectroscopy (NMR) (Cheetham & Mashimba, 1992). These meth-
* Corresponding author. Tel./fax: +98 21 88041344. E-mail addresses: [email protected], [email protected] (Z. Talebpour). 0308-8146/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2010.07.016
ods are capable of simpler assays than wet procedures but they are destructive, laborious, costly and some what unsatisfactory for routine analysis. As a result, the most common methods for the determination of pyruvate and acetate are still based on wet chemistry procedures namely the lactate dehydrogenase (Duckworth & Yaphe, 1970) and hydroxamic acid (Hestrin, 1949) methods, respectively. As a fast, accurate and easy technique, Fourier transfer infrared spectroscopy (FT-IR) is widely used in the analysis of carbohydrate polymers (Boulet, Williams, & Doco, 2007; Copikova et al., 2006; Kacurakova & Wilson, 2001; Yuen, Choi, Phillips, & Ma, 2009). The advantages of FT-IR could be made good used of deriving information from complex samples because of the development of chemometrics (Koca, Kocaoglu-Vurma, Harper, & Rodriguez-Saona, 2010; Xie, Ye, Liu, & Ying, 2009). Partial least squares regression (PLS) (Wold, Sjostrom, & Eriksson, 2001) is one of the most popular chemometric algorithms for calibration model creation owing to its simplicity and small volume of calculations, which can be performed with easily accessible statically software. In recent years, as growing interest is taken in addressing complex problems by FT– IR, the combination of spectral pre-processing methods with multivariate calibration techniques has drawn great attention and shown good prospects in constructing robust and parsimonious FT-IR calibration models. Due to the extremely low solubility of xanthan in common organic solvents employed in FT-IR spectrometry and the high viscosity of aqueous solutions of xanthan, this biopolymer cannot
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be readily analysed in solution. As such, it is necessary to obtain the infrared spectra directly from the solid samples. The different physical characteristics and inhomogenities of solid samples are responsible for the spectral variability present in FT-IR measurements. Thus, spectra can be pre-processed in order to improve multivariate calibration (Blanco, Castillo, Peinado, & Beneyto, 2007). This is achieved by traditional filtering methods for FT-IR spectra, including first and second derivative (D1, D2) (Savitzky & Golay, 1964), standard normal variate (SNV) (Barnes, Dhanoa, & Lister, 1989), and multiplicative signal correction (MSC) (Geladi, MacDougall, & Martens, 1985) methods that are used in order to eliminate effects caused by sample preparation. These methods are all applied to the spectral matrix. Therefore, it is difficult to exclude the irrelevant information whilst not removing the spectral variations related to system components. Orthogonal signal correction (OSC) was introduced by Wold and coworkers (Wold, Antii, Lindgren, & Ohman, 1998) to remove systematic variation from response matrix X that is unrelated or orthogonal to the property matrix Y. This treatment has been used to correct systematic spectral alterations such as baseline changes and multiplicative effects, which are independent of concentration. In the current work, a new method for the direct and simultaneous determination of pyruvate and acetate contents of microbial xanthan solid samples based on FT-IR measurements using a partial least squares regression model is proposed. The effect of different spectral pre-processing methods and spectral regions in improving the performance of the PLS-1 models are studied. The predictive ability of the final PLS models is also compared with those obtained by reference methods for real samples of xanthan. 2. Materials and methods 2.1. Materials Sodium pyruvate, sodium acetate and KBr (IR spectroscopy grade) were obtained from Merck (Darmstadt, Germany). Xanthan standard (free from pyruvate and acetate groups) was prepared in the laboratory as described in Section 2.3. Commercial xanthan samples, including food grade xanthan (CHEMCOLLOIDS, Cheshire, UK), drilling mud xanthan (IOOC, Tehran, Iran), and industrial grade xanthan (CPKelco, Leatherhead, UK), were used as received. 2.2. Instrumentation Spectra were recorded using a Bruker FT-IR spectrometer model Tensor 27 (Bremen, Germany) equipped with a TGS detector having a standard device for pellet preparation. The OPUS software (version 4.1, Bruker) was employed throughout the work. For each spectrum, 16 scans were performed at a resolution of 4 cm1 over the mid-IR range (4000–400 cm1). All of the spectra were recorded at the transmittance mode and collected spectra were rationed against air as a background. Prior to data analysis, the region corresponding to the absorption of atmospheric CO2 (2410–2295 cm1) was eliminated. Weight measurements were performed on a Sartorius balance (Goettingen, Germany) with a precision of ±0.01 mg. 2.3. Biosynthesis of xanthan and preparation of standard samples Xanthan was produced in a 2-L bench-top fermenter (BiostatB, B-Braun, Germany) using Xanthomonas campestris strain b82, a native isolate that was previously introduced (Soudi, Ebrahimi, & Sharyat Panahi, 2006). Preculture was prepared in yeast extract, malt extract, and glucose broth, and a synthetic medium containing sucrose, citric acid, and mineral salts was used as the standard
production medium. To separate bacterial cells, the product was diluted and centrifuged several times, and xanthan gum was extracted using isopropyl alcohol. To prepare the xanthan standard (free of pyruvate and acetate groups), pyruvate and acetate groups were removed by heating a solution of xanthan (5 g/L, pH 8) for 3 h at 95 °C (alkaline treatment) and then reneutralising. Preparation of the b82 and 1706 laboratory samples (S1 and S2) were performed with the same procedure described for the xanthan standard by using the Xanthomonas campes b82 and X. campes 1706DSMZ strains, respectively, without an alkaline treatment. 2.4. Pellet preparation All pellets were prepared by mixing KBr and sample in a 95–5% w/w proportion. The sample is defined as a mixture of different amounts of xanthan standard, sodium pyruvate, and sodium acetate. The method described in reference Dijiba, Zhang, and Niemczyk (2005) was adapted to ensure the reproducibility of the method and the homogeneity of samples. Forty milligrams of the final prepared mixture were used for each pellet, which was then poured into the sample holder of the FT-IR apparatus. The top surface of the sample was smoothed by careful pulling and pressed up to 6 tons of pressure for 1 min. Triplicate spectra for each pellet were averaged. A mixture containing 95% w/w KBr and 5% w/w xanthan standard was used as the blank. 2.5. Multivariate calibration method 2.5.1. Calibration and prediction sets A calibration set of 35 pellets was prepared by the addition of known aliquots of sodium pyruvate and sodium acetate to a xanthan standard covering an appropriate % (w/w) concentration level to provide sample characteristics similar to real samples of xanthan. This set was divided into factorial and random designs. In the factorial design, 30 calibration pellets were prepared using six concentration levels for pyruvate and five concentration levels for acetate spanning a concentration range of 1.0–3.7% and 1.5– 5.0% w/w, respectively (Nos. 1–30). The second group of calibration pellets (Nos. 31–35) was prepared by using random design with pyruvate and acetate concentrations range between 4.0–6.0% and 5.5–7.5% w/w, respectively. The prediction set with six pellets was built up to ensure a good reference range for the validation. The compositions of the calibration and prediction sets are shown in Table 1. To evaluate the precision of the proposed method, the pellet preparation step for one sample (2:3.5:95.5% w/w pyruvate:acetate:xanthan standard) was repeated 10 times. The proposed method was evaluated for the determination of the pyruvate and acetate contents in three commercial samples of Table 1 The concentration of pyruvate and acetate groups in the calibration (Nos. 1–35) and prediction (Nos. 1p–6p) sets. No. Pyruvate Acetate No. Pyruvate Acetate No. Pyruvate Acetate (% w/w) (% w/w) (% w/w) (% w/w) (% w/w) (% w/w) 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1.0 1.3 1.6 2.1 2.8 3.7 1.0 1.3 1.6 2.1 2.8 3.7 1.0 1.3
1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7 3.8
15 16 17 18 19 20 21 22 23 24 25 26 27 28
1.6 2.1 2.8 3.7 1.0 1.3 1.6 2.1 2.8 3.7 1.0 1.3 1.6 2.1
5.0 1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7 3.8 5.0 1.5 2.0 2.7
29 30 31 32 33 34 35
2.8 3.7 4.0 4.5 5.0 5.5 6.0
3.8 5.0 6.5 5.5 7.5 7.0 5.5
1p 2p 3p 4p 5p 6p
1.2 4.7 2.2 1.7 3.4 4.0
1.8 4.0 3.0 2.5 4.9 3.6
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xanthan (food, drilling mud, and industrial grade) and two synthetic laboratory xanthan samples (S1 and S2). The results were compared with the results of the reference methods. 2.5.2. Data analysis Triplicate spectra for each pellet were averaged, and the averaged spectra were subtracted from the spectra of the blank sample. In order to remove or reduce noise, offset, and/or bias, spectra were smoothed according to the procedure of Savitzky and Golay (1964) by means of a 23-point smoothing filter. Several spectral pre-processing methods and their combinations were also employed, giving rise to a total of seven pre-processing methods, including the raw data. These included: first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), orthogonal signal correction (OSC), D1– OSC, and D2–OSC. For investigation of the different spectral preprocessing methods, the full range of spectra was used. After choosing the optimised pre-processing method, several data intervals were analysed in order to establish the best models, including 1320–1350 and 1700–1800 cm1 for pyruvate, and 1380–1420 and 1500–1600 cm1 for acetate. The partial least squares approach (PLS-1) was used for building calibration models. The parameters of different spectral pre-processing methods (the number of OSC components), the spectral regions, and the number of latent variables were all optimised by leave-one-out (LOO) cross-validation on the calibration set (through RMSECV). The predictive ability of the optimum calibration model was evaluated by the root mean square error and correlation coefficient of prediction sets (RMSEP and R2pre , respectively). The parameters of RMSECV and RMSEP are defined as:
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 ^ i¼1 ðyni yi Þ RMSECV ¼ n sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn ^ 2 i¼1 ðyi yi Þ RMSEP ¼ n
where n is the number of samples in the calibration and prediction sets, and yi is the reference measurement result for the sample i in ^ni is the estimated result for sample i in the calibration set each set. y ^ is the estiwhen the model is constructed without sample i and y mated result of sample i in the prediction set. 2.5.3. Software The data pre-processing included SNV, MSC, and OSC and also PLS modelling was carried out using MATLAB V.6.1. (Math works, Natick, USA) and PLS-Toolbox 3.5 (Eigenvector Research, Inc., Manson, WA), as implemented in Matlab 6.1. 2.6. Reference methods 2.6.1. Lactate dehydrogenase method for pyruvate determination The lactate dehydrogenase method for the determination of pyruvate in polysaccharides (Duckworth & Yaphe, 1970) is known to be one of the most accurate methods for the determination of pyruvate in xanthan, and is commonly used for this purpose. In this method, the pyruvate group in xanthan is first hydrolysed using oxalic acid or dilute HCl, and then the amount of nicotinamide adenine dinucleotide (NAD)+ released is measured at 340 nm using lactate dehydrogenase (LDH). 2.6.2. Hydroxamic acid method for acetate determination In the hydroxamic acid method (Hestrin, 1949), the acetate ester in xanthan reacts with hydroxamic acid to produce an acetohydroxamic acid. This acid forms a water-soluble complex with iron that absorbs light at 520 nm. The method is applicable for the determination of 2–10 micromoles of acetate group per mL. 3. Results and discussion 3.1. FT-IR spectra Fig. 1 shows the spectra of xanthan, sodium pyruvate, and the sodium acetate standard in the full range of the mid-IR region with an air background. As is evident, intense absorption bands
Fig. 1. The spectrum of (a) xanthan, (b) sodium pyruvate, and (c) sodium acetate standards in the full range of mid-IR with air background. Each pellet consisted of KBr and sample with a 95–5% w/w proportion.
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corresponding to the pyruvate and acetate functional groups are observed in the range of 1000–1800 cm1. The spectra of pyruvate and acetate have similar band positions owing to the similarity of these functional groups. The obvious overlap between the pyruvate and acetate bands in this region allows for the use of multivariate regression for the simultaneous determination of pyruvate and acetate content in xanthan sample. 3.2. Multivariate calibration method 3.2.1. Rationale behind modelling approach Prior to developing models, it is essential to consider some other factors, as it is preferable to develop models with a clear analytical basis relating to each analyte. The difference between the molecular environment of synthetic calibration mixtures and real samples can influence the position of the characteristics bonds in their IR spectra. For this reason, a feasibility study (spiking experiment) was conducted using a set of spiked synthetic samples ranging from 0% to 11% w/w for each analyte (acetate and pyruvate) in order to comparing the position of the characteristic bands in synthetic and real samples. It was evident from this study that though there are some little differences between the spectral feature of either calibration mixtures with real samples or different real samples, which was produced by using different strains of X. campes, with each other, but all of them have a same pattern in the region of 1300–2000 cm1. 3.2.2. Effects of spectral pre-processing methods Fig. 2a indicated the FT-IR spectra of the calibration set (Table 1) after subtraction of the blank sample spectrum. Additionally, pretreatment average-smoothing and mean centreing spectral data were employed to remove noise and eliminate common spectral information. Consistent baseline offsets and biases were also present in the spectra due to differences in the scatter profile of the solid samples. These offsets and biases typically produce calibration models that require a larger number of factors or that bear lower predictive abilities (higher RMSECV). In order to minimise these features and eliminate sources of non-linearity, different spectral data pre-processing methods (D1, D2, SNV, MSC and OSC) were applied using the calibration set. The number of OSC components was changed from 1 to 4, and two components sufficed to remove the majority of the spectral variance not related to the analyte concentration. D1 and D2 effectively reduced the bias originally present in the spectra. However, as the derivatives are numerically calculated as differences in intensity in intervals of wavenumbers they have the detrimental effect of reducing the signal-to-noise ratio, especially when applied to noisy spectra (Roychoudhury, Harvey, & McNeil, 2006). This effect can be seen in Fig. 2b, which shows the D1 spectra of the calibration set. The OSC processed spectra are shown in Fig. 2c. It is evident that prior to OSC pre-treatment, all spectra overlapped, whilst after OSC correction, the difference between the spectra was more obvious. The multivariate statistical method of partial least squares (PLS) was employed to create calibration models to relate the spectra to the known content of pyruvate and acetate groups in xanthan. In order to investigate spectral pre-processing methods, data were analysed in the range of 400–4000 cm1 and full cross-validation was employed. Plots of the RMSECV versus latent variable (LV) for the pyruvate and acetate groups were obtained for the spectral pre-processing methods of D1, D2, SNV, MSC, OSC and their combinations. Table 2, part A displays the extracted results from RMSECV plot determined using different spectral pre-processing methods. The calibration models for pyruvate and acetate presented the lowest values for RMSECV for the spectra set when the D1, D2, and OSC were applied. These methods were more effective than the classi-
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cal pre-treatment methods, such as SNV and MSC. The greatest advantage of OSC is that it provides simpler models with a reduced number of PLS factors. Additionally, the combination of the D1 and D2 pre-processing with OSC was applied for the construction of calibration models (Table 2, part B). Subsequent evaluation of RMSECV indicated that the first derived OSC-treated data provided substantially lower values than others. Thus, this combination was chosen as the optimal spectral pre-processing method for the prediction of pyruvate and acetate levels in solid samples of xanthan by FT-IR. 3.2.3. Effect of spectral regions The selection of relevant spectral regions improved the results by reducing the contribution to the overall noise from these regions in the absence of relevant information about analyte concentrations. In order to find the best spectral region, different spectral intervals upon the characteristic bands of pyruvate and acetate were analysed. However, only the most significant results are detailed herein. The optimum region was selected based on the RMSECV of the D1–OSC pre-processing method for the calibration model, and the results are shown in Table 3. As evident, the optimal results for pyruvate and acetate determination were obtained by using wavenumber regions 1320–1350 and 1500–1600 cm1, respectively. As such, these regions were applied for construction of the final models. 3.2.4. Characteristics of optimum models The optimal models using PLS-1 employed in the optimal preprocessing method (D1–OSC), and selected regions with the lowest RMSECV were used to predict the pyruvate and acetate levels in the separate prediction sets. The optimums LVs that lead to the minimum values for error belong to factor numbers 5 and 3 for pyruvate and acetate, respectively. The RMSECV and RMSEP produced for pyruvate and acetate levels were 0.13%, 0.22% w/w and 0.29%, 0.35% w/w, respectively. The square of the correlation coefficient for calibration and prediction sets (R2cal , R2pre ) obtained for pyruvate and acetate were obtained at (0.9986, 0.9910) and (0.9514, 0.9332), respectively. Evidently, the correlation coefficients of the calibration and prediction sets for acetate are not as good as for pyruvate due to the lower overall peak sensitivity. In order to evaluate the precision of the method, 10 pellets of a sample containing 2% w/w pyruvate and 3.5% w/w acetate were prepared to verify the reproducibility of the obtained spectra. The triplicate spectra for each pellet were averaged, and pyruvate and acetate content was predicted using the optimal models. The relative inter-pellet standard deviation was calculated to be 12.7% and 16.6% for pyruvate and acetate, respectively. Additionally, the lower–upper limits of pyruvate and acetate at the 95% confidence interval were obtained at 2.03–2.37 and 3.16–3.84, respectively. 3.3. Analysis of xanthan real samples In order to verify the accuracy of the constructed PLS models, three commercial xanthans (food, drilling mud, and industrial grade) and two synthetic laboratory xanthans (S1, S2) containing pyruvate and acetate were analysed. The results obtained by the proposed methods were compared with those found by the aforementioned lactate dehydrogenase method (for pyruvate) and hydroxamic acid method (for acetate) (Table 4). The correlation coefficients (R2) between the mid-IR prediction value and the reference measurement for pyruvate and acetate content were determined to be 0.9544 and 0.9441, respectively. The results obtained by the proposed method for pyruvate and acetate content for varying real samples were statistically compared with those
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Fig. 2. The spectra of the calibration set after: (a) subtraction of the blank sample spectrum (none), (b) first derivative (D1), and (c) orthogonal signal correction (OSC).
obtained by their reference methods using analysis of variance (ANOVA). Comparison between D1–OSC–PLS and these methods
shows that these results are reliable within a 95% of confidence interval.
R. Tavallaie et al. / Food Chemistry 124 (2011) 1124–1130 Table 2 Analytical features of different spectral pre-processing methods employed for PLS-1 calculation on pyruvate and acetate content in xanthan. Pyruvate Pre-processing method A None D1 D2 SNV MSC OSC B D1–OSC D2–OSC a b c
Acetate
LVa
RMSECVb (% w/w)
R2cal c
LVs
RMSECV (% w/w)
R2cal
3 8 10 8 10 1
0.65 0.48 0.54 0.63 0.70 0.30
0.7879 0.9749 0.9692 0.9234 0.9097 0.9579
4 7 4 7 10 1
0.28 0.35 0.33 0.31 0.49 0.24
0.9295 0.9457 0.9016 0.9740 0.9650 0.9690
4 2
0.03 0.20
0.9998 0.9161
5 4
0.01 0.25
0.9999 0.9826
Number of latent variables. Root mean square error of cross-validation. Correlation between the predicted and expected values for calibration set.
Table 3 Analytical features of different spectral regions employed for PLS-1 calculation on pyruvate and acetate content in xanthan.
a b c
Region
Wavenumber (cm1)
LVsa
RMSECVb (% w/w)
R2cal c
Pyruvate 1 2 3
1320–1350 1700–1800 Combination of 1 and 2
5 6 6
0.13 0.24 0.14
0.9986 0.9761 0.9906
Acetate 1 2 3
1380–1420 1500–1600 Combination of 1 and 2
5 3 6
0.45 0.29 0.40
0.7878 0.9514 0.8434
Number of latent variables. Root mean square error of cross-validation. Correlation between the predicted and expected values for calibration set.
Table 4 Pyruvate and acetate levels in commercial and laboratory made xanthan samples using the optimum models. Real samples
Xanthan food grade Xanthan drilling mud Xanthan industrial grade S1 S2 a
Pyruvate (% w/w)
Acetate (% w/w)
D1–OSC– PLS method
Reference method
D1–OSC– PLS method
Reference method
3.42 ± 0.27a 2.98 ± 0.11 1.69 ± 0.41
3.75 ± 0.17 2.90 ± 0.24 2.07 ± 0.31
1.29 ± 0.59 1.66 ± 0.17 2.02 ± 0.63
1.98 ± 0.32 2.19 ± 0.19 3.07 ± 0.42
1.11 ± 0.15 1.21 ± 0.34
1.40 ± 0.86 1.80 ± 0.98
1.06 ± 0.42 1.20 ± 0.25
1.28 ± 0.19 1.50 ± 0.28
Mean ± standard deviation (n = 3).
4. Conclusion The results of the present study indicate that solid-state FT-IR measurements can be employed for direct determination of both pyruvate and acetate in xanthan biopolymer samples, both typically present as very minor components. The simplicity of the PLS procedure in combination with its low cost are significant advantages over related reference methods. This procedure is also rapid, efficient, and environment friendly, as the amount of organic solvents used during its quantitative determination process is sig-
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nificantly reduced. As such, this procedure might serve as an application for xanthan quality control in industry. We have further demonstrated that when care is taken to produce homogeneous calibration samples and optimised pre-processing methods are carried out, good results can be obtained by FT-IR measurements on KBr disks for quantitative purposes, a variation of the method that offers an alternative for analysis of poorly water-soluble biopolymers such as xanthan.
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