NIR spectroscopy and partial least squares regression for the determination of phosphate content and viscosity behaviour of potato starch

Transcription

1 L.G. Thygesen et al., J. Near Infrared Spectrosc. 9, (2001) 133 Determination of Phosphate Content and Viscosity Behaviour of Potato Starch L.G. Thygesen et al., J. Near Infrared Spectrosc. 9, (2001) NIR spectroscopy and partial least squares regression for the determination of phosphate content and viscosity behaviour of potato starch L.G. Thygesen, a* S.B. Engelsen, a M.H. Madsen b and O.B. Sørensen c a Food Technology, Department of Dairy and Food Science, The Royal Veterinary and Agricultural University, Rolighedsvej 30, 1958 Frederiksberg C, Denmark. lit@kvl.dk b Danish Institute of Agricultural Sciences, PO Box 50, 8830 Tjele, Denmark c KMC, Nr. Lindvej 14, 7400 Herning, Denmark A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, ( nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation (RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature). Keywords: near infrared spectroscopy (NIR), Rapid Visco Analyzer (RVA), viscosity, phosphate, phosphorus, potato starch, peak viscosity Introduction The viscosity behaviour of the starch paste is a most important quality parameter for potato starch. At potato starch factories, the viscosity is normally characterised by viscograms obtained using either the Brabender Amylograph (Brabender) or the Rapid Visco Analyzer (RVA). The Brabender is the oldest (more than 50 years old 1 ) and the most wellestablished method. The working principles of this instrument are described in textbooks on food analysis, for example, Pomeranz and Meloan (1994). 2 However, a Brabender measurement normally takes more than two hours and requires approximately 24 g of starch. During the nineties the RVA was introduced as a more rapid alternative. A RVA measurement is similar to a Brabender measurement, but it uses a smaller sample (about 2.5 g), which can be NIR Publications 2001, ISSN

2 134 Determination of Phosphate Content and Viscosity Behaviour of Potato Starch heated faster than the larger sample required by the Brabender, and, therefore, the measurement takes only about 15 minutes. A number of studies have demonstrated good correlations between the two methods. 3 6 In the present study, we tested the feasability of using near infrared (NIR) spectroscopy as an even faster alternative for quality control of potato starch, i.e. by measuring the dried starch directly, with no need for suspension and subsequent gelatinisation. Some potato starch factories already use NIR spectroscopy for pricing potato deliveries according to dry matter content and for determination of nitrogen content, so an additional use of the instruments would be advantageous. The viscosity of starch pastes is normally described as being closely related to the degree of phosphorylation of the starch, although this may not always be the case. 7 The phosphate groups are located as monoesters at the C-6 or the C-3 positions of a small part of the glucose units in the amylopectin, and the degree of phosphorylation expresses the amount of such units relative to the total number of glucose units. In light of the expected relationship between the viscosity behaviour and the degree of phosphorylation, it was decided to test the ability of NIR to measure both the phosphate content and the RVA viscosity behaviour. To the best of the authors knowledge, no attempts to measure either of these properties using NIR have been published earlier. There is a particular phosphate-related absorbance band in the NIR range, namely the first overtone of the stretching vibration of the OH band at 1908 nm. 8 However, given the low degree of phosphorylation in native starches (about 0.1 1%), 9 NIR-based prediction of the phosphate content in potato starch presents a challenge to the sensitivity of NIR measurements and may turn out to rely on indirect covarying effects. Materials and methods Starch samples Starch samples were prepared from a set of 100 potato samples as described earlier. 10 The potatoes were grown during 1998 at four different locations in Denmark and the set included ten different starch potato varieties: Dianella, Posmo, Godiva, Kardal, Karnico, Kuras, Meva, Oleva, Ponto and Producent. At three of the locations, each variety was grown in three replicates, i.e the number of unique combinations of location and variety was 40. Phosphate content Total starch bound phosphates were determined by wet oxidation with sulphuric acid and colorimetric determination of the formed inorganic phosphate according to Stuffins (1967). 11 Thus, the phosphate content is given as the corresponding phosphorus content. The measurement was unsuccessful for three samples, reducing the set to 97 samples. Rapid Visco Analyzer (RVA) A Rapid Visco Analyzer (Newport Scientific, Australia) was used. 2 g of starch (dry weight) was mixed with the amount of deionised water resulting in a total weight of 28 g. The stirring speed was 160 rpm, and the heating profile was as follows: heating C (3 minutes and 42 seconds) hold at 95 C (2 minutes and 30 seconds) cooling to 50 C (3 minutes and 48 seconds) hold at 50 C (2 minutes) Figure 1 gives an example of a RVA viscogram and illustrates how the RVA variables are derived from it. The RVA variables are Peak viscocity, Through, Breakdown, Final viscosity, Setback, Peak time and Pasting temperature. The latter is not shown in the figure as it is the temperature at the start of the main peak, i.e. when gel formation begins. Near infrared spectroscopy Prior to the NIR measurements, the starch samples were dried at 50 C over night. NIR spectra from 700 to 2498 nm with 900 points (2 nm steps) were obtained using a NIRSystems 6500 instrument (Foss Electric, Hillerød, Denmark, equipped with a spinning, circular sample cup with an internal diameter of 38 mm. Each spectrum was an average of 32 scans and one spectrum was obtained per sample. Software All modelling and spectral pretreatment was carried out using Matlab version 5.3 (The Mathworks, Natick, MA, USA, the PLS toolbox version 2.0 (Eigenvector Research, Man-

3 L.G. Thygesen et al., J. Near Infrared Spectrosc. 9, (2001) 135 Viscosity Final visc Peak visc. Through Breakdown = Peak visc. Through Setback = Final visc. Through Peak time Time (s) Figure 1. Example of RVA viscogram with explanation of RVA variables. The RVA variable pasting temperature is not shown on the diagram. It is the temperature at the start of gel formation, i.e. at the left starting point of the main peak. default settings of the OSCCALC Matlab procedure (available at Partial least squares regression (PLSR) modelling was carried out using full cross-validation. Ideally, when preprocessing is followed by cross-validation, and the preprocessing of one sample involves data from other training samples, the preprocessing should be carried out segmentwise with separate scaling of each test segment, as pointed out by Fearn (2000). 14 No commercial software using this procedure is presently available, except for OSC. In the present study, we have therefore chosen only to display results from OSC and from SNV, for which the preprocessing of a spectrum does not involve other spectra, in contrast to what is the case for multiplicative scatter correction. 15,16 However, as mean centering was performed on the whole data set prior to the preprocessing/modelling, the results presented here may still be slightly too optimistic. son, WA, USA, and The Unscrambler version 7.5 (CAMO, Oslo, Norway, Preprocessing and cross-validation The spectroscopic preprocessing methods tested in this study are: mean centering (MC), standard normal variate (SNV) 12 and orthogonal signal correction (OSC) 13 using one OSC component and the Results Figure 2 shows the RVA curves and the NIR spectra for the two most extreme samples. No obvious outliers were observed in the RVA data or in the NIR data, and principal component analysis (PCA) of the two data sets confirmed this observation (results not shown). Table 1 gives descriptive statistics of the phosphate content and of the seven RVA variables. Figure 2. NIR spectra (a) and RVA curves (b) of the two most extreme potato starch samples.

4 136 Determination of Phosphate Content and Viscosity Behaviour of Potato Starch Table 1. Phosphate content and RVA variables. Std is the population standard deviation, CV is the coefficient of variation (Std in percent of Mean). P (%) Peak Visc. Through Breakdown Final Visc. Setback Peak time (s) Pasting temp. ( C) Min Mean Max Std CV (%) The low coefficients of variation for the variables Peak time and Pasting temperature indicate a priori that modelling of these two variables is not feasible given the data set at hand. Prediction of phosphate content from RVA variables, RVA viscograms and NIR spectra Figure 3(a) shows the loadings of the first two components of a PCA of the seven RVA variables and the phosphate content. The plot illustrates that there is some redundancy in the RVA variables, with Pasting temperature, Through and Final viscosity partially expressing the same trait of the starch gel. The same can be said about Peak viscosity and Breakdown. Phosphate content is closely related to Breakdown (r = 0.90) and to Peak viscosity (r = 0.89) for this data set. Figure 3(b) illustrates the linear relationship between Breakdown and P. Figure 4 shows the prediction errors of a number of PLSR models based on either RVA viscograms or NIR spectra. The prediction errors are given as root mean square error of cross-validation (RMSECV) from a full cross-validation and for 1 15 PLSR components. The horizontal line at RMSECV = is the default RMSECV, i.e. the RMSECV obtained if the phosphate content of each sample is simply predicted to be the mean phosphate content of the rest of the samples. The horizontal line at RMSECV = is the RMSECV obtained by a linear regression Figure 3. (a) PC1 and PC2 loadings from a PCA of a data set comprising the phosphate content (P) and the seven RVA variables for all 97 samples. (b) Phosphate content vs Breakdown (r = 0.90).

5 L.G. Thygesen et al., J. Near Infrared Spectrosc. 9, (2001) 137 Figure 4. RMSECV of 1 15 component PLSR models for prediction of the phosphate content in starch. The models are based on RVA viscograms (a) or NIR spectra (b). Results for three different pretreatments are shown: MC (solid line, stars), SNV (solid line, plus signs) and OSC (dotted line, squares). The horizontal line at RMSECV = 0.019% is the default RMSECV, and the horizontal line at % is the RMSECV for a linear regression model based on Breakdown only. model for phosphate content with Breakdown as regressor [i.e. a regression line for the plot in Figure 3(b)]. Figure 4(a) shows that none of the PLSR-models based on the RVA viscograms performs better than the model based on Breakdown only. An 11- component PLSR model of SNV-treated NIR spectra [Figure 4(b)] gives a RMSECV of %, i.e. a 25% reduction in prediction error compared to the best RVA-based model. An 11-factor model would appear to be rather complicated for a model of a single constituent. The reason is that the first factors of the model are dominated by spectral variance irrelevant to phosphate content, and it is our experience from working with spectroscopic data of biological materials that this is often the case. Ideally, OSC should be able to handle this situation, and as can be seen in Figure 4(b), OSC pretreatment does reduce the number of factors (to six, one OSC factor and five PLS factors), but at the price of inferior prediction (RMSECV = 0.013%). In another attempt to simplify the model, interval PLS 17 was tested, but no wavelength interval was found to yield a smaller prediction error than the full range model, indicating that the spectral region around 1908 nm is not alone responsible for the resulting model. This may indicate that the full spectral range model is based on indirect effects in the spectra (as expected given the low phosphate content), or alternatively it may underline the holographic nature of NIR spectra with overtones and combination bands from the same vibraphore residing throughout the spectrum, which in turn may stabilise weak relationships. A test of the model on 23 starch samples with a phosphate content within the range of the training set, but industrially prepared, was unsuccessful, confirming that the model is sensitive to sample preparation, probably especially to drying procedures. However, a test of the simple linear relationship found between Breakdown and phosphate content [Figure 3(b)] was also unsuccessful, which shows that the simple RVA-based model is not robust either. In an attempt to reveal whether the NIR/PLSR model relied on indirect effects related to the compartmentalisation of moisture in the starch, the transverse relaxation times from pulsed low-field 1 H NMR experiments of starch suspensions were compared to the phosphate content of the starch samples, but no trends of a relationship were revealed (results not shown). To test for possible overfitting in the NIR model, the data set was divided in two subsets (every second sample after sorting according to phosphate content) and an 11-factor PLS model based on SNV-treated spectra was made for each subset. The regression co-

6 138 Determination of Phosphate Content and Viscosity Behaviour of Potato Starch Figure 5. RMSECV of 1 15 component NIR-based PLSR models for prediction of RVA variables. Line types etc. as in Figure 4. The horizontal line in each subplot is the default RMSECV. efficients of the models were very close to identical, which indicates that the model is not overfitting. The same procedure carried out on the two subsets, but with OSC treatment of each subset as a whole before modelling yielded two clearly different models (i.e. overfitting). To summarise, even though the NIR/ PLSR model found here is complex and may rely on indirect effects, NIR/PLSR prediction of phosphate content is a possibility if sample preparation is strictly standardised. Alternatively we have demonstrated that a simple prediction of the phosphate content can be made from the RVA variable Breakdown. It is possible, even likely, that a more robust model could have been obtained if some spectral variability had been introduced by obtaining more than one spectrum per sample (refilling, temperature differences). Prediction of RVA variables from NIR spectra Figure 5 shows the RMSECV of NIR/PLSR models for the seven RVA variables, i.e. prediction of gel-viscosity properties from NIR measurements on dry starch powder. The horizontal line in each plot corresponds to the default RMSECV. The figure shows that only the two variables related to the phosphate content (Breakdown and Peak viscosity) could be predicted successfully. This supports the validity of our NIR/PLSR phosphate model [notice the similarity between Figure 4(b) (phosphate) and Figure

7 L.G. Thygesen et al., J. Near Infrared Spectrosc. 9, (2001) 139 5(c) (Breakdown)]. As expected, modelling was futile for Peak time and Pasting temperature, but also for Setback. The results are mediocre for Through and Final Viscosity, in accordance with the co-variance with Pasting temperature [Figure 3(a)]. Conclusions NIR/PLSR prediction of the phosphate content in potato starch samples was possible with a RMSECV of 0.006%, which is 25% better than the smallest prediction error obtained from models based on RVA viscograms. The best NIR/PLSR model was found for SNV treated spectra, while OSC was unsuccessful. However, the NIR/PLSR model relies on weak spectral effects, and is highly sensitive to sample preparation. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was also possible, probably because they were highly related to phosphate content in the present data set. Acknowledgement This study was funded by the Directorate for Food, Fishing and Agro Business, contract no. NON97-5. The authors thank Henrik Pedersen (AKV, Langholt) for providing a part of the sample set. References 1. C.A. Anker and W.F. Geddes, Cereal Chem. 21, 335 (1944). 2. Y. Pomeranz and C.E. Meloan, Food Analysis Theory and Practice, 3rd edition. Chapman & Hall, New York, USA (1994). 3. L.B. Deffenbaugh and C.E. Walker, Cereal Chem. 66, 493 (1989). 4. N.U. Haase, T. Mintus and D. Weipert, Starch/Stärke 47, 123 (1995). 5. H.J. Thieves and P.A.M. Steeneken, Starch/Stärke 49, 85 (1997). 6. P.J.H.C. Mijland, A.M. Janssen and P.A.M. de Vries, Starch/Stärke 51, 33 (1999). 7. A. Blennow, A.M. Bay-Smidt and R. Bauer, International Journal of Biological Macromolecules 28, 409 (2001). 8. B. Osborne, T. Fearn and P.H. Hindle, Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, 2nd edition. Longman Scientific and Technical, Harlow, UK (1993). 9. A. Blennow, S.B. Engelsen, L. Munck and B.L. Møller, Carbohydrate Polymers 41, 163 (2000). 10. D.H. Christensen and M.H. Madsen, Pot. Res. 39, 43 (1996). 11. C.B. Stuffins, Analyst 92, 107 (1967). 12. R.J. Barnes, R.J. Dhanoa MS and S.J. Lister, Appl. Spectrosc. 43, 772 (1989). 13. J. Sjöblom, O. Svensson, M. Josefson, H. Kullberg and S. Wold, Chemo. Intell. Lab. Sys. 44, 229 (1998). 14. T. Fearn, Chemo. Intell. Lab. Sys. 50, 47 (2000). 15. P. Geladi, D. McDougall and H. Martens, Appl. Spectrosc. 39, 491 (1985). 16. T. Isaksson and T. Næs, Appl. Spectrosc. 42, 1273 (1988). 17. L. Nørgaard, A. Saudland, J. Wagner, J.P. Nielsen, L. Munck and S.B. Engelsen, Appl. Spectrosc. 54, 413 (2000). Received: 29 December 2000 Revised: 29 May 2001 Accepted: 5 June Web Publication: 23 August 2001