Prediction of Long-Grain Rice Texture and Pasting Properties From Starch and Protein Fractions

Transcription

1 RICE QUALITY AND PROCESSING Prediction of Long-Grain Rice Texture and Pasting Properties From Starch and Protein Fractions A. Brun, J.-F. Meullenet, and W.-K. Chung ABSTRACT The objective of this study was to determine predictive models of rice functionality from protein and starch chemistry data. One long-grain rice variety (Cocodrie) was harvested from two fields in Arkansas. Rice was harvested from 8 locations in each field. Samples were then stored at 21ºC/50% RH until equilibrated to a storage moisture content of 12%. Texture properties were measured using a TPA test on ten cooked rice kernels in conjunction with a Texture Analyer (TAXT2i, Texture Technologies, Scarsdale, NY) while pasting properties were measured with a Rapid Visco-Analyer. Rice chemical properties measured included amylose and protein contents, starch molecular weight profiles, and protein molecular weight distribution. Rice harvested from a single rice field was quite variable in physicochemical properties. For example, the protein contents of rice flour samples ranged from 6.62 to 8.44% and peak viscosity of the rice flours ranged from to 217 RVU. This implies that small changes in water or nutrient availability can have a significant impact on rice quality. It was also established that functional characteristics can be predicted from both starch and protein chemistry. Peak viscosity (Rc=0.98, Rp=0.72) was well predicted by protein and starch data. Overall, data indicate that starch was more important in predicting peak viscosity than were proteins. INTRODUCTION Because of the increasing use of rice in the food industry, there is a need for rice processors to pay close attention to rice quality, including functional characteristics 351

2 AAES Research Series 504 such as cooking, texture, and pasting properties. There is abundant literature dealing with the effects of postharvest handling (i.e., drying, storage, and milling) on functional characteristics of rice (Villareal et al, 1976; Tsugita et al, 1993; Chrastil, 1990; Hamaker and Griffin, 1993; Perdon et al, 1997; Pearce et al., 2001). However, there is little information on the variability in the functionality of specific cultivars grown in various locations. The aim of this study was to correlate functional characteristics of rice samples from a single cultivar harvested from various locations in several fields, to starch and protein compositional data. MATERIALS AND METHODS Sample Collection and Preparation One long-grain rice variety (Cocodrie) was used in this study. Rice was harvested from two fields in Arkansas at moisture contents of 19±1% on a wet basis. Rice was harvested from 8 locations in each of the two 80-acre fields. Immediately after harvest, samples were brought to the University of Arkansas Rice Processing Laboratories (Fayetteville, AR) and cleaned with a Carter-Day Dockage Tester (Carter-Day Co., Minneapolis, MN). Samples were then stored in an equilibrium chamber set at 21º C/50% RH until equilibrated to a storage moisture content of 12% after which samples were transferred into airtight storage buckets and stored in the same equilibrium chamber for 90 days until testing. For testing requiring milled white rice, the rough rice sample was milled. Removal of the hull was performed with a McGill sample sheller and milling with a McGill No.2 mill. Samples were milled to a constant degree of milling (DOM) of 90 for all samples. Some of each milled sample was ground into rice flour for analyses requiring flour using a Cyclone sample mill (Udy Corporation, Fort Collins, CO) with a 25-mm sieve. Rice Functionality Measurements Pasting Properties Amylography was performed on rice flour using a Rapid Visco-Analyer (model- 4, Newport Scientific, Warriewood, Australia) according to AACC method Parameters calculated included peak, trough, and final viscosities and pasting temperature. Samples were analyed in duplicates. Chemical Characteriation Protein Analysis The amount of protein in rice flour samples was determined by a standard HPLC method. The percent of crude protein was calculated from a nitrogen conversion factor 352

3 B.R. Wells Rice Research Studies 2002 by multiplying the sample nitrogen content by This analysis was performed in duplicates. Molecular weight distribution of the rice was determined by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) (Mini-protean 3, Bio-Rad Corp., Hercules, CA) using a modification of the Laemmli's (1970) procedure. Starch Analysis Starch was extracted by an alkaline steeping method described by Hoover et al. (1985). Total amylose content was determined on rice starch according to Juliano (1971). Starch profiles were examined by high performance sie-exclusion chromatography (model-1515 Waters Corp., Milford, MA). From these data, refractive indices at various retention times (i.e. a total of 33) were extracted. Each profile was base-line adjusted and then normalied using a visual basic macro (Microsoft Excel 2000). Data Analysis Analysis of variance was performed for functional and physico-chemical data to determine the sample location main effect using Proc Glm of SAS (version 7.0, SAS Institute, Cary, NC). Partial Least Squares Regression (Unscrambler version 7.5, CAMO, Trondheim, Norway) was used to evaluate the relationship between physicochemical characteristics and functional parameters. RESULTS AND DISCUSSION Amylose and Protein Contents The protein content of rice flour and the amylose content of starch flour are presented in Table 1. The protein contents of rice flour samples ranged from 6.62 to 7.76% and from 7.28 to 8.44%, and the amylose content of rice flour ranged from to 28.59% and to 28.14% for fields 1 and 2, respectively. There were significant differences (p<0.05) within each field for both the protein and the amylose contents of samples harvested at the various locations. This illustrates the fact that variability can exist within the same field. This result provides the perfect opportunity to determine if compositional changes within a single field (i.e. where the genetic material should be uniform) have a significant impact on functional characteristics such as amylography and texture. Molecular Weight Distribution of Proteins The relative densities for each SDS-PAGE band are shown in Table 2. The most concentrated bands were found between 10 and 30 kdal. The 60-kdal sub-unit has been known as a starch granule-bound protein (Villareal and Juliano, 1986). This protein is known as the waxy gene factor and is highly correlated with amylose content and 353

4 AAES Research Series 504 consequently with hardness or firmness of cooked rice. The 60-kdal band was found to be present in the SDS-PAGE profiles. Statistical analysis showed some significant differences in the proportions of the bands present for samples harvested in different locations within each field. However, as a general rule, greater differences were found for field 1. It will be demonstrated later in this article that field 1 also exhibited greater functional variability. Pasting Properties The pasting parameters of rice flour samples from fields 1 and 2 are presented in Table 3. For the samples tested, the peak viscosity of the rice flours ranged from to RVU for field 1 and to for field 2. Significant differences were observed only for peak viscosity, trough, and final viscosity for both fields. However, variations were more pronounced for field 1 than for field 2. For example, peak viscosity showed 5 statistically different groups (i.e. according to Duncan's multiple paired differences tests) for field 1 and only 2 for field 2. The same was true for trough and final viscosity measurements. Pasting temperatures were found to be statistically different only for samples harvested from field 1. Prediction of Functional Characteristics from Physicochemical Data Amylose and Protein Contents Correlations between functional data and amylose and protein contents are given in Table 4. It was decided to evaluate separate models for the two fields studied. The intent was to determine if the same trends could be seen in both fields when treated as separate data sets. For field 2, the predictive models of viscoamylographic parameters did not validate well (Rp<0.16). This result shows that the prediction of properties such as peak or final viscosity from amylose and protein contents were not possible and should not be relied upon. However, predictive models for field 1 were relatively good (Rc>0.80, Rp>0.54). For all three amylographic parameters studied, both amylose and protein contents were negatively correlated to the parameters. This shows that as amylose and protein contents increased, the peak, trough, and final viscosities all decreased. In addition, the examination of the weighted regression coefficients indicates that protein content seemed to play a greater role in decreasing viscosity than did the amylose content. Our conclusion is that for the set of samples studied, at least when conclusions could be drawn, protein content dictated the bulk of changes in amylographic properties. Starch and Proteins In order to more precisely determine the influence of protein and starch on functional characteristics, the structure of the starch measured by HPSEC and protein molecular-weight distribution by SDS PAGE were used as predictors. Table 5 presents 354

5 B.R. Wells Rice Research Studies 2002 model statistics for the prediction of rice functionality from SDS-PAGE and HPSEC data combined. Because of the large number of predictive variables and although multivariate regression methods were used (i.e. Partial Least Squares Regression), predictive models were determined for samples from both fields combined (i.e. n=16). All models showed relatively high calibration correlation coefficients (0.65<Rc<0.98). This means that the SDS-PAGE and HPSEC data were significantly correlated to functional properties. Peak viscosity (Rc=0.98, Rp=0.72) was well predicted by protein and starch data. Weighted regression coefficients were plotted for each of the predictive variables for this attribute (Fig. 1). Overall, the regression coefficients indicate that the starch profile was more important in predicting peak viscosity than were proteins, although several protein bands were important. High molecular-weight starch fractions (retention time between 13 and 15 minutes) were positively correlated to peak viscosity. Lower molecular-weight carbohydrates (retention times between 15.4 and 23.4 minutes) were all negatively correlated to peak viscosity, which is not surprising since low molecular-weight starch such as amylose has been found to be negatively correlated to stickiness. Proteins with molecular weights of 100, 60 and 34 Kdal were positively correlated with peak viscosity while 38-, 36-, 22-, and 10-Kdal sub-units were negatively correlated with this attribute. This is interesting in the sense that it implies that different proteins may play different roles in determining rice functionality. Other RVA parameters were also well predicted by the HPSEC and SDS-PAGE data and model results were found to be similar (i.e., regression coefficients not shown). SIGNIFICANCE OF FINDINGS This study has established several pieces of information. First, rice harvested from a single rice field can be quite variable in functional properties. This is interesting because growing conditions in a single field should be similar. This implies that even small changes in water or nutrient availability can have a significant impact on rice quality. Second, we established that soil properties measured at the time of planting are not good predictors of rice functionality. This could be due to the fact that soil analysis may need to be done during the early stage of plant growth or that the soils property measurements are not an indicator of plant nutrient uptake or health. Third, we established that functional characteristics can be predicted from both starch and protein chemistry. This is not necessarily a new finding since many studies have demonstrated the role of starch and to some extent proteins in determining functional characteristics such as cooked-rice texture. However, this study illustrates the fact that the same general principles apply to variations in functionality within a single cultivar. In addition, the study raised questions about the roles of specific starch and protein fractions in determining rice functionality. The use of multivariate regression analysis for the investigation of the role of specific rice components in determining functional parameters was found to be useful. This study dealt only with a single cultivar harvested from only 2 fields. The observations made here should be confirmed by a much larger study involving several cultivars and additional sampling locations. 355

6 AAES Research Series 504 REFERENCES AACC Approved Methods of the AACC. Methods Eighth edition, AACC, Inc. St Paul, MN. Chrastil, J Chemical and physiochemical changes of rice during storage at different temperatures. J. Cereal Sci. 11: Hamaker, B. and Griffin, V.K Effect of disulfide bond-containing protein on rice starch gelatiniation and pasting. Cereal Chem. 70:377. Hoover, R., Sailaja, Y., and Sosulski, F.W Characteriation of starches from wild and long grain brown rice. Food Res. International. 29(2): Juliano, B.O A simplified assay for milled-rice amylose. Cereal Science Today. 16(10): , 360. Laemmli, H Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 22: Pearce, M.D., Marks, B.P., and Meullenet, J-F Effects of post-harvest parameters on functional changes during rough rice storage. Cereal Chem. 78(3): Perdon, A.A., Marks, B.P., Siebenmorgen, T.J., and Reid, N.B Effects of rough rice storage conditions on the amylograph and cooking propertiesof medium-grain (cultivar Bengal) rice. Cereal Chem. 74: Tsugita, T., Ohta,T., and Kato, H Cooking flavor and texture of rice stored under different conditions. Agric. Biol. Chem. 47: Villareal, R.M., Resurreccion, A.P., Suuki, L.B., and Juliano, B.O Changes in physicochemical properties of rice during storage. Starch 28: Villareal, R.M. and Juliano, B.O Waxy gene factor and residual protein of rice starch granules. Starch 38:

7 B.R. Wells Rice Research Studies 2002 Table 1. Amylose and protein contents of rice flour. Field Amylose Protein (%) Field 1 a 28.6 a 7.8 a b 28.6 a 7.1 b c 24.3 d 7.7 a d 28.1 a 7.7 a e 27.6 a 6.6 c f 27.0 ab 7.7 a g 25.2 cd 6.9 bc h 26.0 bc 6.9 bc Field 2 a 24.4 cd 7.5 bcd b 23.5 d 7.6 bcd c 24.6 cd 8.0 b d 27.7 ab 7.3 d e 24.7 cd 7.8 bc f 28.1 a 7.5 bcd g 26.1 bc 8.4 a h 26.5 ab 7.5 cd Means within a column and a field with different letters were significantly different from each other according to Duncan s paired comparison tests (alpha=0.05). 357

8 AAES Research Series 504 Table 2. Molecular weight distribution (% of total) of protein fractions extracted from rice flour. Protein molecular weight,y 100 kdal 60 kdal 38 kdal 37 kdal 36 kdal 34 kdal 27 kdal 25 kdl 22 kdal 10 kdal Field 1 a 0.24 c x 1.81 de a a 5.57 bc 1.80 bc 8.31 c a a bc b 1.36 abc 5.91 a 7.61 a a 4.38 a 1.69 c 8.49 c a a a c 0.58 bc 3.75 bc a 10.23abc 9.97 ab 3.24 abc 9.03 bc a a c d 0.15 c 1.53 e 8.11 a 2.03 c a 3.01 abc abc a a ab e 2.51 a 5.07 ab 7.35 a ab 5.92 bc 5.31 a ab a a bc f 0.68 bc 3.51 bc a 2.95 bc a 5.27 a a a a bc g 0.51 bc 3.14 cd 8.23 a a 7.57 abc 4.15 ab 9.83 abc a a c h 1.58 ab 5.86 a 7.33 a a 8.79 abc 5.11 a 9.55 abc a a c Field 2 a 0.61 cd 3.10 a a a 5.35 a 5.13 ab 9.83 bc a a a b 0.71 bc 3.05 a a 8.30 a 9.29 a 3.58 b 9.17 c a a a c 0.61 cd 3.17 a 9.40 ab 8.01 a 8.94 a 5.17 ab 9.66 bc a a a d 0.52 de 3.12 a 9.50 ab a 7.36 a 5.36 ab 9.45 bc a a a e 0.82 b 3.03 a a 8.40 a 8.46 a 5.88 ab abc a a a f 0.81 b 4.29 a a 7.53 a 8.82 a 6.56 a ab a a a g 1.17 a 3.68 a 5.47 c a 8.13 a 6.89 a a a a a h 0.40 e 4.11 a 6.33 bc a 7.25 a 5.63 ab 9.28 c a a a Relative proportions of each of the bands were determined by densitometry using the software on images of the gels scanned at 2400DPI. y Molecular weights were determined using standards. x Means within a column and a field with different letters were significantly different from each other according to Duncan's paired comparison tests (alpha=0.05). 358

9 B.R. Wells Rice Research Studies 2002 Table 3. Pasting properties of rice flour determined by Rapid Visco-Analyer. Peak viscosity Trough viscosity Final viscosity Pasting temp (RVU) (ºC) Field 1 a e d f 81.9 a b c bcd cd 80.7 b c d bc de 81.1 ab d e cd ef 82.0 a e a a a 81.1 ab f d ab cd 81.6 ab g b ab ab 80.8 b h a ab bc 80.8 b Field 2 a a a a 78.5 a b a ab a 79.2 a c b ab b 78.8 a d a ab a 78.8 a e b c c 78.8 a f b c c 78.8 a g b abc b 79.2 a h b ba a 79.2 a Means within a column and a field with different letters were significantly different from each other according to Duncan s paired comparison tests (alpha=0.05). 359

10 AAES Research Series 504 Table 4. Model statistics for the prediction of rice functionality using amylose and protein contents. R c R p y Amylose x Protein w Field 1 Pasting properties Peak viscosity NS v Trough viscosity Final viscosity NS Field 2 Pasting properties Peak viscosity NS Trough viscosity 0.40 NS NS NS Final viscosity 0.44 NS NS NS Rc = calibration correlation coefficient. y Rp = validation correlation coefficient; full cross-validation was employed. x Weighted regression coefficient obtained from partial least squares regression for amylose content. w Weighted regression coefficient obtained from partial least squares regression for protein content. v NS = not significant. Table 5. Prediction of functional properties from SDS-PAGE and HPSEC data. # PCs y x R c R p Pasting properties Peak viscosity Trough vicsocity Final viscosity Pasting temperature y x Number of principal components used in the model (n=16). Rc = calibration correlation coefficient. Rp = validation correlation coefficient; full cross-validation was employed. 360

11 B.R. Wells Rice Research Studies 2002 Weighted regression coefficients Sample starch profile Protein Starch Protein molecular weight (Kdal) and starch fractions retention time (min) Fig. 1. Prediction of peak viscosity from protein SDS-PAGE and Starch HPSEC. 361