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Feasibility study for the rapid determination of the amylose content in starch by near-infrared spectroscopy
European Journal of Pharmaceutical Sciences 21 (2004) 155–159
Feasibility study for the rapid determination of the amylose content in starch by near-infrared spectroscopy Christiane C. Fertig a , Fridrun Podczeck b,∗ , Roger D. Jee a , Mark R. Smith a b
a The School of Pharmacy, University of London, 29–39 Brunswick Square, London WC1N 1AX, UK Sunderland Pharmacy School, HNSS, University of Sunderland, Chester Road Campus, Sunderland SR1 3SD, UK
Received 20 February 2003; received in revised form 2 September 2003; accepted 25 September 2003
Abstract Near-infrared (NIR) spectroscopy was used to determine the amylose content in six different starches, whose declared amylose contents ranged from 2 to 95% m/m. The amylose content of starches can vary considerably between batches depending on growth conditions and time of harvesting. An NIR calibration model was developed for amylose using simple laboratory produced mixtures of amylose and amylopectin in different ratios. The spectral region at 1700–1800 nm showed a good correlation to the amylose content of these mixtures. A simple absorbance ratio calibration model using standard normal variate and first derivative pre-treated spectra gave a root mean standard error of prediction of 1.2% m/m. Application to real samples gave amylose contents in reasonable agreement with the average values stated by the supplier. NIR spectroscopy provides a rapid and non-destructive method for the quantitative determination and standardisation of amylose in starch and could make a suitable alternative to traditional techniques, such as complex formation of starch with iodine or n-butanol. © 2003 Elsevier B.V. All rights reserved. Keywords: Amylose content; Near-infrared spectroscopy; Starch
1. Introduction Near-infrared (NIR) spectra of materials provides both physical and chemical information about a given sample (Mark, 2001). It is able to determine the concentrations of one or more constituents of a compound from a single spectrum, and can be likened to a fingerprint of the material (Sekulic et al., 1998). Not only is it a rapid and non-destructive technique, but also requires minimal or no sample preparation. NIR spectra may be measured in either the transmittance or reflectance modes. Liquids are generally measured by transmittance, while reflectance measurements are more commonly applied to highly scattering liquids or solid samples (Mark, 2001). NIR spectra often exhibit a baseline shift caused by variations in sample compaction, scatter from the particle surface and the particle size of the material itself. Particle size, for
∗ Corresponding author. Tel.: +44-191-515-2568; fax: +44-191-515-2568. E-mail address: [email protected] (F. Podczeck).
0928-0987/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.ejps.2003.09.011
example, determines the spectral path length, which can lead to a substantial effect on the resultant spectrum. To minimize the influence of these parameters the raw spectra are usually subjected to mathematical pre-treatments before developing calibration models. Frequently used spectral pre-treatments are the calculation of first-derivative or second derivative and standard normal variate (SNV) transformation. Derivatives remove baseline shifts and sloping background absorption, which arises from the physical nature of the sample, such as particle size (Mark, 2001). Single wavelength absorbance versus concentration plot calibrations are rarely possible with NIR spectra because of overlapping absorbances between the various components. The simplest calibration models are typically based on multiple linear regression (MLR), using the absorbance at two or more wavelengths (Mark, 2001). By using more than one wavelength allowance for overlapping peaks can be taken into account. NIR is an important analytical tool in the pharmaceutical industry where it is used to identify materials and to measure their properties, such as composition, moisture content, content uniformity, homogeneity of mixing, particle size or
156
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
as in-process control to monitor production processes (Guo et al., 1999). Starch is a complex polysaccharide of ␣-d-glucose units exclusively, which are joined by a sequence of ␣-d-(1,4)-glucosidic linkages thus giving rise to linear or helical chains. These are referred to as ‘amylose’. The much less frequent ␣-(1,6)-glucosidic linkages form the branch points between the chains thereby creating highly branched domains, which are called ‘amylopectin’. Although amylose and amylopectin are closely related polymers they exhibit different structures which are distinguishable by NIR spectroscopy. The ratio of amylose to amylopectin in native starch is not only genetically controlled but also depends on the growth conditions and time of harvesting. Starch is widely used in pharmaceutics as a filler, binder, disintegrant and thickening agent. Also recently it has been used in biodegradable films and coatings (Ali, 2002). The classical and still commonly used method for the quantification of amylose and amylopectin is the iodine reaction coupled with potentiometric or amperometric titration (Banks et al., 1971). The method is based on the capacity inherent to amylose to accommodate polyiodide ions, chiefly I5 − , within its helical structure. Since amylopectin is unable to form such complexes because of its short chains and branch linkages interfering with the formation of stable structures, these complexes are specific for the amylose fraction (Hizukuri, 1996). However, the iodine affinity varies within species, hence compromising the accuracy of this method.
2. Materials and methods 2.1. Materials Six starch samples were provided by the National Starch and Chemical Company, Bridgewater, NJ, USA. They consisted of four commercially available types, Hylon 5® , Hylon 7® , LAPS® and PURE® , and two experimental samples, called ‘Melojel® corn starch’ and ‘Amioca® powder waxy corn starch’. For reasons of briefness the latter two are subsequently referred to as ‘Corn starch’ and ‘Waxy starch’. Hylon 5 (batch number: BJ 9960, release date: 19.10.1999) and Hylon 7 (batch number: FG 5514, release date: 19.10.1999) are unmodified high-amylose starches containing 50 and 71% m/m amylose, respectively (values given by the supplier). LAPS (batch number: 37–4964, release date: 27.09.2000) is a modified low-amylopectin starch typically containing less than 10% m/m amylopectin and most commonly no more than 5% m/m. Derived from native starches it is made up of around 75% m/m of a high-molecular-mass amylose and of around 20% m/m of a low-molecular-mass amylose with the amylopectin balance being approximately 5% m/m. PURE (batch number: 1180:6B, release date: 19.12.2000) is a pure corn starch with an amylose content of 95% m/m or higher. Accordingly the
amylopectin content is 5% m/m or less. Corn starch (batch number: 45–0801, release date: 17.7.2001) is an untreated corn starch normally containing 27% m/m amylose and 73% m/m amylopectin. Waxy starch (batch number: 51–6002, release date: 17 July 2001) is essentially composed of amylopectin thus containing 0% m/m amylose. Amylopectin (batch number: 9561E, EC-number: 9037-22-3, Aurora, OH, USA) is isolated from corn starch and of analytical grade. 2.2. Methods 2.2.1. Preparation of the samples A set of 29 mixtures were prepared from pure amylose and analytical-grade amylopectin covering the amylose content range 0–100% m/m in approximately 5% m/m steps. Calculated proportions of amylose and amylopectin were weighed into 10 ml scintillation vials and mixed in a Turbula mixer (Turbula mixer, Type T2C, Nr. 870290, Willy Bachofen AG, Basel, Switzerland; Blender cage: Type 32–422, Glen Creston, Stanmore, England) at 42 rpm for 10 min. The mixtures were stored in the scintillation vials. 2.2.2. NIR measurements NIR reflectance (R) spectra were measured using a FOSS NIRSystems 6500 near-infrared spectrometer fitted with a Rapid Content Analyser (FOSS NIRSystems, Silver Spring, MD, USA). Prior to measurement, a system suitability test consisting of checking the wavelength, absorbance scale and photometric noise, was performed. All spectra were measured with respect to the instrument’s ceramic reflectance standard. Samples were measured directly through the bottom of the scintillation vials and were handled with latex gloves. Samples were vigorously shaken before being place on the sample stage. Each recorded spectrum was the average of 64 scans over the wavelength range of 1100–2500 nm (approximately 40 s measurement time). 2.2.3. Data analysis Data analysis was performed using the instrument suppliers software (Vision® 2.51, FOSS NIRSystems, Silver Spring, MD, USA).
3. Results and discussion Absorbance (−log10 R) spectra of a number of natural starch samples are shown in Fig. 1A. Standard normal variate transformation of the spectra (Fig. 1B) removes the baseline offsets making it easier to compare spectra. Perhaps the most notable feature is the water absorption peaks at approximately 1450 and 1940 nm. Clearly the water content of the samples vary. The differences due to the varying amylose contents is much less pronounced, however, it can be more clearly seen in Fig. 2. Fig. 2 shows spectra of pure
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
3.1. Development of the calibration model
(A) 5 6 4
Absorbance
7
3
0.0
-0. 2 1100
1300
1 2
1500
1700
1900
2100
2300
2500
1500
1700 1900 Wavelength/nm
2100
2300
2500
2.6
SNV absorbance
(B)
0.0
-1. 8 1100
1300
Fig. 1. (A) Absorbance (−log10 R) spectra for Hylon 5 (1), Hylon 7 (2), LAPS (3), PURE (4), Corn (5), Waxy (6) and amylopectin (7) starch samples. (B) As in part A, but SNV transformed spectra.
amylose (100% m/m) and amylopectin (i.e. 0% m/m amylose). The spectral region 1700–1800 nm showing the most important differences. Further spectral interpretation is difficult, however, impurities such as lipids and proteins could be attributed to the peaks around 1700 and 2000–2200 nm, respectively.
Ideally to develop a calibration model to measure the amylose content of starch samples a large number of naturally occurring starches with a wide range of amylose contents and containing varying ratios of low and high molecular mass amylose is required. As such a set of samples was not available it was decided to carry out a feasibility study by setting up a model using samples obtained by mixing pure amylose and amylopectin. Twenty nine mixtures covering the range 0–100% m/m amylose were prepared and then divided into two sets. A calibration set (21 samples) with which to develop the model and a validation set (eight samples) with which to test the model. Examination of the score plot from Principal Component Analysis of the calibration set revealed an outlier and this sample was removed leaving 20 samples in the calibration set. SNV first-derivative (segment size = 16 nm, gap size = 0 nm) spectra showed particularly good correlation to the amylose content. Fig. 3A shows the SNV first-derivative spectra of the calibration set, while Fig. 3B shows the corresponding correlation plot. Good correlation between the spectral value and amylose content is shown widely across much of the wavelength range measured. The highest correlations
0.8
(A) SNV first-derivative absorbance
0.3
157
0.0
-0.4 1100
1300
1500
1700
1900
2100
2300
2500
1.0
(B)
0.0
Correlation coefficient
SNV absorbance
2.6
100%
0.0
0%
-1. 8 1100
-1.0 1100 1300
1500
1700 1900 Wavelength/nm
2100
2300
2500
Fig. 2. SNV absorbance (−log10 R) spectra for amylose (100% m/m) and amylopectin (i.e. 0% m/m amylose) samples.
1300
1500
1700 1900 Wavelength/nm
2100
2300
2500
Fig. 3. (A) SNV first-derivative absorbance spectra for the calibration set, (B) plot of correlation between SNV first-derivative absorbance value and percentage amylose for the calibration set as a function of wavelength.
158
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
Fig. 4. Expanded spectral region showing the dependence of the SNV first derivative absorbance on percentage amylose content.
(both positive and negative) are to be found in the region 1700–1800 nm. Fig. 4 shows this region for blended samples containing 0–100% m/m amylose in 20% m/m steps. The best-fit model for the calibration set was found to be given by using SNV first-derivative spectral pre-treatments and using a MLR model involving the ratio of two spectral values, Eq. (1). A1740 y = 167.3 − 111.2 (1) A1704 where y is the % m/m amylose content, A is the transformed absorbance at the subscripted wavelength while the coefficients 167.3 and 111.2 came from the least squares fitting process. The standard error of calibration (SEC) was 1.4% m/m. Applying this model to the validation set gave good NIR predicted values with a root mean standard error of prediction (RMSEP) of 1.2% m/m, confirming the good predictive ability of the model. The SEC and RMSEP were calculated according to Eqs. (2) and (3). (y − yˆ )2 SEC = (2) nc − 2 RMSEP =
(y − yˆ )2 nv
Fig. 5. Plots of NIR predicted percentage amylose vs. reference amylose content for both the calibration and validation sets.
3.2. Sample analysis The natural starch samples were measured and analysed using the same calibration model. Table 1 summarises the NIR predicted values for the amylose content along with
(3)
where, y: reference value, yˆ : NIR predicted value, nc : number of calibration samples and nv : number of validation samples. A plot of NIR predicted amylose content versus reference amylose content for the calibration set (Fig. 5) yielded a straight line with a slope and intercept of 0.997 and 0.15, respectively. The correlation coefficient was 0.9987. Similarly, the plot for the validation set (Fig. 5) gave a straight line with a slope of 0.979, intercept 1.4 and a correlation of >0.999.
Table 1 Amylose content of natural starch samples Starch sample
Amylose content (% m/m)
Hylon 5 Hylon 7 LAPS PURE Corn Waxy
50.0 71.0 95.0 95.0 27.0 2.0
NIR predicted value (% m/m) 1
2
Mean
56.0 69.0 65.0 98.0 24.0 8.0
56.0 69.0 66.0 97.0 24.0 7.0
56.0 69.0 65.5 97.5 24.0 7.5
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
those provided by the supplier. The amylose content in Hylon 5 was 56% m/m, which is 6% m/m higher than the stated value, whereas that for Hylon 7 was 69% m/m, only slightly below the value stated (71% m/m). By contrast, the NIR predicted amylose content for LAPS of 65% m/m was far below the claimed value of 95% m/m. This discrepancy most probably arises from the fact that LAPS is made up of 75% m/m high molecular mass amylose and 20% m/m low molecular mass amylose with the rest being amylopectin. The NIR model developed purely on simple mixtures which did not include samples of varying low and high molecular mass amylose could not be expected to predict such a sample accurately. Sample PURE gave an amylose content of 97% m/m, which lies near the declared minimum content of 95% m/m. Corn starch was found to comprise 24% m/m amylose, which is close to the claimed value of 27% m/m. Finally, the predicted amylose content for Waxy starch was low, 7.5% m/m, though a little higher than the stated value of 2% m/m and it can therefore be concluded that Waxy starch is essentially an amylopectin-only material. The analytical-grade amylopectin gave a value of −2.0% m/m, which effectively confirms the absence of amylose.
4. Conclusion This feasibility study has clearly demonstrated that NIR can provide a simple method for determining the amylose content in starch samples. Despite the availability of other, more conventional, methods, such as complex formation with iodine or n-butanol (Ring et al., 1985), the NIR technique is more practical in that it is rapid and non-destructive.
159
Minimum or no sample preparation as well as short sampling time make this technique particularly interesting for routine analysis. An NIR model set up with a truly representative set of natural starch samples could be expected to provide a precise and accurate assay procedure.
Acknowledgements The authors are grateful to the National Starch Company for provision of the starch samples, and to the School of Pharmacy, London, for a scholarship for CF.
References Ali, J., 2002. Functional starches. Chem. Ind. 6, 18–20. Banks, W., Greenwood, C.T., Muir, D.D., 1971. The characterization of starch and its components. Part 3. The technique of semimicro, differential, potentiometric titration, and the factors affecting it. Stärke 23, 118–127. Guo, J.H., Skinner, G.W., Harcum, W., Malone, J.P., Weyer, L.G., 1999. Application of near-infrared spectroscopy in the pharmaceutical solid dosage form. Drug Dev. Ind. Pharm. 25, 1267–1270. Hizukuri, S., 1996. Starch: analytical aspects. In: Eliasson, A.-C. (Ed.), Carbohydrates in Food. Marcel Dekker, New York, pp. 347–429. Mark, H., 2001. Fundamentals of near-infrared spectroscopy. In: Raghavachan, R. (Ed.), Near-infrared Applications in Biotechnology. Marcel Dekker, New York, pp. 293–321. Ring, S.G., I’Anson, K., Morris, V., 1985. Static and dynamic light scattering studies of amylose solutions. Macromolecules 18, 182– 188. Sekulic, S., Wakeman, J., Doherty, P., Hailey, P., 1998. Automated system for the online monitoring of powder blending processes using NIR spectroscopy. Part II. Qualitative approaches to blend evaluation. J. Pharm. Biomed. Anal. 17, 1285–1309.
Feasibility study for the rapid determination of the amylose content in starch by near-infrared spectroscopy Christiane C. Fertig a , Fridrun Podczeck b,∗ , Roger D. Jee a , Mark R. Smith a b
a The School of Pharmacy, University of London, 29–39 Brunswick Square, London WC1N 1AX, UK Sunderland Pharmacy School, HNSS, University of Sunderland, Chester Road Campus, Sunderland SR1 3SD, UK
Received 20 February 2003; received in revised form 2 September 2003; accepted 25 September 2003
Abstract Near-infrared (NIR) spectroscopy was used to determine the amylose content in six different starches, whose declared amylose contents ranged from 2 to 95% m/m. The amylose content of starches can vary considerably between batches depending on growth conditions and time of harvesting. An NIR calibration model was developed for amylose using simple laboratory produced mixtures of amylose and amylopectin in different ratios. The spectral region at 1700–1800 nm showed a good correlation to the amylose content of these mixtures. A simple absorbance ratio calibration model using standard normal variate and first derivative pre-treated spectra gave a root mean standard error of prediction of 1.2% m/m. Application to real samples gave amylose contents in reasonable agreement with the average values stated by the supplier. NIR spectroscopy provides a rapid and non-destructive method for the quantitative determination and standardisation of amylose in starch and could make a suitable alternative to traditional techniques, such as complex formation of starch with iodine or n-butanol. © 2003 Elsevier B.V. All rights reserved. Keywords: Amylose content; Near-infrared spectroscopy; Starch
1. Introduction Near-infrared (NIR) spectra of materials provides both physical and chemical information about a given sample (Mark, 2001). It is able to determine the concentrations of one or more constituents of a compound from a single spectrum, and can be likened to a fingerprint of the material (Sekulic et al., 1998). Not only is it a rapid and non-destructive technique, but also requires minimal or no sample preparation. NIR spectra may be measured in either the transmittance or reflectance modes. Liquids are generally measured by transmittance, while reflectance measurements are more commonly applied to highly scattering liquids or solid samples (Mark, 2001). NIR spectra often exhibit a baseline shift caused by variations in sample compaction, scatter from the particle surface and the particle size of the material itself. Particle size, for
∗ Corresponding author. Tel.: +44-191-515-2568; fax: +44-191-515-2568. E-mail address: [email protected] (F. Podczeck).
0928-0987/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.ejps.2003.09.011
example, determines the spectral path length, which can lead to a substantial effect on the resultant spectrum. To minimize the influence of these parameters the raw spectra are usually subjected to mathematical pre-treatments before developing calibration models. Frequently used spectral pre-treatments are the calculation of first-derivative or second derivative and standard normal variate (SNV) transformation. Derivatives remove baseline shifts and sloping background absorption, which arises from the physical nature of the sample, such as particle size (Mark, 2001). Single wavelength absorbance versus concentration plot calibrations are rarely possible with NIR spectra because of overlapping absorbances between the various components. The simplest calibration models are typically based on multiple linear regression (MLR), using the absorbance at two or more wavelengths (Mark, 2001). By using more than one wavelength allowance for overlapping peaks can be taken into account. NIR is an important analytical tool in the pharmaceutical industry where it is used to identify materials and to measure their properties, such as composition, moisture content, content uniformity, homogeneity of mixing, particle size or
156
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
as in-process control to monitor production processes (Guo et al., 1999). Starch is a complex polysaccharide of ␣-d-glucose units exclusively, which are joined by a sequence of ␣-d-(1,4)-glucosidic linkages thus giving rise to linear or helical chains. These are referred to as ‘amylose’. The much less frequent ␣-(1,6)-glucosidic linkages form the branch points between the chains thereby creating highly branched domains, which are called ‘amylopectin’. Although amylose and amylopectin are closely related polymers they exhibit different structures which are distinguishable by NIR spectroscopy. The ratio of amylose to amylopectin in native starch is not only genetically controlled but also depends on the growth conditions and time of harvesting. Starch is widely used in pharmaceutics as a filler, binder, disintegrant and thickening agent. Also recently it has been used in biodegradable films and coatings (Ali, 2002). The classical and still commonly used method for the quantification of amylose and amylopectin is the iodine reaction coupled with potentiometric or amperometric titration (Banks et al., 1971). The method is based on the capacity inherent to amylose to accommodate polyiodide ions, chiefly I5 − , within its helical structure. Since amylopectin is unable to form such complexes because of its short chains and branch linkages interfering with the formation of stable structures, these complexes are specific for the amylose fraction (Hizukuri, 1996). However, the iodine affinity varies within species, hence compromising the accuracy of this method.
2. Materials and methods 2.1. Materials Six starch samples were provided by the National Starch and Chemical Company, Bridgewater, NJ, USA. They consisted of four commercially available types, Hylon 5® , Hylon 7® , LAPS® and PURE® , and two experimental samples, called ‘Melojel® corn starch’ and ‘Amioca® powder waxy corn starch’. For reasons of briefness the latter two are subsequently referred to as ‘Corn starch’ and ‘Waxy starch’. Hylon 5 (batch number: BJ 9960, release date: 19.10.1999) and Hylon 7 (batch number: FG 5514, release date: 19.10.1999) are unmodified high-amylose starches containing 50 and 71% m/m amylose, respectively (values given by the supplier). LAPS (batch number: 37–4964, release date: 27.09.2000) is a modified low-amylopectin starch typically containing less than 10% m/m amylopectin and most commonly no more than 5% m/m. Derived from native starches it is made up of around 75% m/m of a high-molecular-mass amylose and of around 20% m/m of a low-molecular-mass amylose with the amylopectin balance being approximately 5% m/m. PURE (batch number: 1180:6B, release date: 19.12.2000) is a pure corn starch with an amylose content of 95% m/m or higher. Accordingly the
amylopectin content is 5% m/m or less. Corn starch (batch number: 45–0801, release date: 17.7.2001) is an untreated corn starch normally containing 27% m/m amylose and 73% m/m amylopectin. Waxy starch (batch number: 51–6002, release date: 17 July 2001) is essentially composed of amylopectin thus containing 0% m/m amylose. Amylopectin (batch number: 9561E, EC-number: 9037-22-3, Aurora, OH, USA) is isolated from corn starch and of analytical grade. 2.2. Methods 2.2.1. Preparation of the samples A set of 29 mixtures were prepared from pure amylose and analytical-grade amylopectin covering the amylose content range 0–100% m/m in approximately 5% m/m steps. Calculated proportions of amylose and amylopectin were weighed into 10 ml scintillation vials and mixed in a Turbula mixer (Turbula mixer, Type T2C, Nr. 870290, Willy Bachofen AG, Basel, Switzerland; Blender cage: Type 32–422, Glen Creston, Stanmore, England) at 42 rpm for 10 min. The mixtures were stored in the scintillation vials. 2.2.2. NIR measurements NIR reflectance (R) spectra were measured using a FOSS NIRSystems 6500 near-infrared spectrometer fitted with a Rapid Content Analyser (FOSS NIRSystems, Silver Spring, MD, USA). Prior to measurement, a system suitability test consisting of checking the wavelength, absorbance scale and photometric noise, was performed. All spectra were measured with respect to the instrument’s ceramic reflectance standard. Samples were measured directly through the bottom of the scintillation vials and were handled with latex gloves. Samples were vigorously shaken before being place on the sample stage. Each recorded spectrum was the average of 64 scans over the wavelength range of 1100–2500 nm (approximately 40 s measurement time). 2.2.3. Data analysis Data analysis was performed using the instrument suppliers software (Vision® 2.51, FOSS NIRSystems, Silver Spring, MD, USA).
3. Results and discussion Absorbance (−log10 R) spectra of a number of natural starch samples are shown in Fig. 1A. Standard normal variate transformation of the spectra (Fig. 1B) removes the baseline offsets making it easier to compare spectra. Perhaps the most notable feature is the water absorption peaks at approximately 1450 and 1940 nm. Clearly the water content of the samples vary. The differences due to the varying amylose contents is much less pronounced, however, it can be more clearly seen in Fig. 2. Fig. 2 shows spectra of pure
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
3.1. Development of the calibration model
(A) 5 6 4
Absorbance
7
3
0.0
-0. 2 1100
1300
1 2
1500
1700
1900
2100
2300
2500
1500
1700 1900 Wavelength/nm
2100
2300
2500
2.6
SNV absorbance
(B)
0.0
-1. 8 1100
1300
Fig. 1. (A) Absorbance (−log10 R) spectra for Hylon 5 (1), Hylon 7 (2), LAPS (3), PURE (4), Corn (5), Waxy (6) and amylopectin (7) starch samples. (B) As in part A, but SNV transformed spectra.
amylose (100% m/m) and amylopectin (i.e. 0% m/m amylose). The spectral region 1700–1800 nm showing the most important differences. Further spectral interpretation is difficult, however, impurities such as lipids and proteins could be attributed to the peaks around 1700 and 2000–2200 nm, respectively.
Ideally to develop a calibration model to measure the amylose content of starch samples a large number of naturally occurring starches with a wide range of amylose contents and containing varying ratios of low and high molecular mass amylose is required. As such a set of samples was not available it was decided to carry out a feasibility study by setting up a model using samples obtained by mixing pure amylose and amylopectin. Twenty nine mixtures covering the range 0–100% m/m amylose were prepared and then divided into two sets. A calibration set (21 samples) with which to develop the model and a validation set (eight samples) with which to test the model. Examination of the score plot from Principal Component Analysis of the calibration set revealed an outlier and this sample was removed leaving 20 samples in the calibration set. SNV first-derivative (segment size = 16 nm, gap size = 0 nm) spectra showed particularly good correlation to the amylose content. Fig. 3A shows the SNV first-derivative spectra of the calibration set, while Fig. 3B shows the corresponding correlation plot. Good correlation between the spectral value and amylose content is shown widely across much of the wavelength range measured. The highest correlations
0.8
(A) SNV first-derivative absorbance
0.3
157
0.0
-0.4 1100
1300
1500
1700
1900
2100
2300
2500
1.0
(B)
0.0
Correlation coefficient
SNV absorbance
2.6
100%
0.0
0%
-1. 8 1100
-1.0 1100 1300
1500
1700 1900 Wavelength/nm
2100
2300
2500
Fig. 2. SNV absorbance (−log10 R) spectra for amylose (100% m/m) and amylopectin (i.e. 0% m/m amylose) samples.
1300
1500
1700 1900 Wavelength/nm
2100
2300
2500
Fig. 3. (A) SNV first-derivative absorbance spectra for the calibration set, (B) plot of correlation between SNV first-derivative absorbance value and percentage amylose for the calibration set as a function of wavelength.
158
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
Fig. 4. Expanded spectral region showing the dependence of the SNV first derivative absorbance on percentage amylose content.
(both positive and negative) are to be found in the region 1700–1800 nm. Fig. 4 shows this region for blended samples containing 0–100% m/m amylose in 20% m/m steps. The best-fit model for the calibration set was found to be given by using SNV first-derivative spectral pre-treatments and using a MLR model involving the ratio of two spectral values, Eq. (1). A1740 y = 167.3 − 111.2 (1) A1704 where y is the % m/m amylose content, A is the transformed absorbance at the subscripted wavelength while the coefficients 167.3 and 111.2 came from the least squares fitting process. The standard error of calibration (SEC) was 1.4% m/m. Applying this model to the validation set gave good NIR predicted values with a root mean standard error of prediction (RMSEP) of 1.2% m/m, confirming the good predictive ability of the model. The SEC and RMSEP were calculated according to Eqs. (2) and (3). (y − yˆ )2 SEC = (2) nc − 2 RMSEP =
(y − yˆ )2 nv
Fig. 5. Plots of NIR predicted percentage amylose vs. reference amylose content for both the calibration and validation sets.
3.2. Sample analysis The natural starch samples were measured and analysed using the same calibration model. Table 1 summarises the NIR predicted values for the amylose content along with
(3)
where, y: reference value, yˆ : NIR predicted value, nc : number of calibration samples and nv : number of validation samples. A plot of NIR predicted amylose content versus reference amylose content for the calibration set (Fig. 5) yielded a straight line with a slope and intercept of 0.997 and 0.15, respectively. The correlation coefficient was 0.9987. Similarly, the plot for the validation set (Fig. 5) gave a straight line with a slope of 0.979, intercept 1.4 and a correlation of >0.999.
Table 1 Amylose content of natural starch samples Starch sample
Amylose content (% m/m)
Hylon 5 Hylon 7 LAPS PURE Corn Waxy
50.0 71.0 95.0 95.0 27.0 2.0
NIR predicted value (% m/m) 1
2
Mean
56.0 69.0 65.0 98.0 24.0 8.0
56.0 69.0 66.0 97.0 24.0 7.0
56.0 69.0 65.5 97.5 24.0 7.5
C.C. Fertig et al. / European Journal of Pharmaceutical Sciences 21 (2004) 155–159
those provided by the supplier. The amylose content in Hylon 5 was 56% m/m, which is 6% m/m higher than the stated value, whereas that for Hylon 7 was 69% m/m, only slightly below the value stated (71% m/m). By contrast, the NIR predicted amylose content for LAPS of 65% m/m was far below the claimed value of 95% m/m. This discrepancy most probably arises from the fact that LAPS is made up of 75% m/m high molecular mass amylose and 20% m/m low molecular mass amylose with the rest being amylopectin. The NIR model developed purely on simple mixtures which did not include samples of varying low and high molecular mass amylose could not be expected to predict such a sample accurately. Sample PURE gave an amylose content of 97% m/m, which lies near the declared minimum content of 95% m/m. Corn starch was found to comprise 24% m/m amylose, which is close to the claimed value of 27% m/m. Finally, the predicted amylose content for Waxy starch was low, 7.5% m/m, though a little higher than the stated value of 2% m/m and it can therefore be concluded that Waxy starch is essentially an amylopectin-only material. The analytical-grade amylopectin gave a value of −2.0% m/m, which effectively confirms the absence of amylose.
4. Conclusion This feasibility study has clearly demonstrated that NIR can provide a simple method for determining the amylose content in starch samples. Despite the availability of other, more conventional, methods, such as complex formation with iodine or n-butanol (Ring et al., 1985), the NIR technique is more practical in that it is rapid and non-destructive.
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Minimum or no sample preparation as well as short sampling time make this technique particularly interesting for routine analysis. An NIR model set up with a truly representative set of natural starch samples could be expected to provide a precise and accurate assay procedure.
Acknowledgements The authors are grateful to the National Starch Company for provision of the starch samples, and to the School of Pharmacy, London, for a scholarship for CF.
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