The Jasmine Rice (KDML 105 Variety) Adulteration Detection Using Physico-Chemical Properties

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1 Chiang Mai J. Sci. 2011; 38(1) 105 Chiang Mai J. Sci. 2011; 38(1) : Contributed Paper The Jasmine Rice (KDML 105 Variety) Adulteration Detection Using Physico-Chemical Properties Sawidtree Pitiphunpong, Sirirat Champangern, and Prisana Suwannaporn* Department of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand. *Author for correspondence; prisana.s@ku.ac.th Received: 13 July 2009 Accepted: 3 September 2010 ABSTRACT Jasmine rice was produced from varieties Khao Dowk Mali 105 (KDML 105) and Kor Kho 15 (RD15). Adulteration of Jasmine rice with other varieties such as Pathum Thani 1 (PTT 1) and Chai Nat 1 (CNT 1) is a problem encountering Thailand rice export. Department of Agriculture of Thailand try to establish testing method for Jasmine rice s adulteration using dyed grain method. Results showed that this method was not accurate when Jasmine rice was adulterated with similar amylose content rice varieties or at low adulteration level. This study tried to investigate more rapid and accurate method to detect Jasmine rice s adulteration. Jasmine rice, KDML 105 variety, was adulterated with CNT 1 and PTT 1 at level 5, 8, 10, 15, 20, 25 and 30 %. Classification of these adulterations of Jasmine rice using its textural and pasting properties was achieved by applying Principal Component Analysis (PCA). PCA could classified adulteration of Jasmine rice efficiently. The first two principal components explained 81.6% and 90.5% of the total variance explained for CN1 and PTT1 adulteration respectively. Prediction model using linear regression confirmed that physico-chemical properties could be used as the more accurate parameters to predict adulteration in Jasmine rice. Its coefficient of determination (R 2 ) equaled to and for CNT 1 and PTT 1 respectively. Keywords: texture, pasting, adulteration, jasmine rice, regression analysis. 1. INTRODUCTION Jasmine rice is the most popular aromatic rice variety grown in Thailand. The grain is characterized by its unique appearance, cooking quality and aroma. The annual export quantities of aromatic rice are as high as million tons and represent 20 27% of the total rice export [1]. Jasmine rice is produced from Khao Dowk Mali 105 (KDML 105) and Kor Kho 15 (RD15) varieties. It is harvested once a year in main crop. Annual production is only 3 4 million tons of paddy rice, which is not enough for the market demand. This brings about an increase in the price of paddy rice. The high price and unfavorable, cooked-rice texture for some export countries such as those in the EU caused blending of aromatic rice with other rice varieties, especially Pathum Thani 1

2 106 Chiang Mai J. Sci. 2011; 38(1) (PTT1; low amylose content, low gelatinization temperature) and Chai Nat 1 (CN1; high amylose content, intermediate gelatinization temperature). Uncontrolled blending of rice causes lower cooked-rice quality and unfair trade. In order to protect the image of Thai rice and maintain export quality, rice grading standards were set up so that rice millers have to declare the mixing level. However, control is limited by available detection methods. Nowadays, rapid detection methods such as the alkali spreading value and the dyed grain method are applied [2]. However their accuracy is low, especially when Jasmine rice is adulterated with similar amylose content rice such as PTT1 variety, or at low adulteration level. Texture is an important attribute of cooked rice and has been used as an indicator for consumer acceptance [3]. Factors that affect texture quality of cooked rice include: variety, amylose content, pasting properties, protein content [4,5], post harvest practice, drying condition, moisture content, temperature and storage period [6]. For Asian markets, the most important parameter to determine cooked rice palatability is hardness, followed by stickiness [7]. Results from Juliano and Pascual [8] found that different amylose contents affected textural properties. In addition, Champange et al. [9] determined the extent to which Rapid Visco Analyzer (RVA) could be used to predict the textural properties of cooked rice. Textural properties were assessed by descriptive sensory analysis (an analytical, objective methodology) and instrumental texture profile analysis (TPA), then related to RVA measurements. They concluded that sensory texture attributes (cohesiveness of mass, stickiness, and initial starchy mouth-coating) and TPA attributes (adhesiveness) had the strongest correlation with RVA measurement. Setback explained most of the variance contributed to model describing these attributes. Champagne et al. [9] used multiple regression analysis to determine the relationship between cooked rice texture and RVA measurements. It had been successfully applied as a tool to predict sensory texture characteristics of cooked rice by using spectral stress strain analysis (SSSA) and partial least squares (PLS) regression along with an extrusion test [10]. In addition, regression analysis was previously used as a technique to identify adulteration of foods such as soft fruit puree using SPME-GC and chemometrics [11], durum wheat using NIR spectroscopy [12]. In this study we tried to apply PCA and multiple linear regression analysis to identify the level of Jasmine rice adulteration more precisely using textural and pasting measurements. PCA was employed to study the variance loading of the corresponding measurements on the extracted factors [13,14]. Simplified indicators were discussed for predictive evaluation of Jasmine rice adulteration, which were expected to be particularly applicable for the rice industry. 2. MATERIALS AND METHODS 2.1 Sample selection and preparation Three rice varieties, including Khao Dok Mali 105 (KDML 105), Pathum Thani 1 (PTT 1) and Chai Nat 1 (CNT 1) were selected, as they were commonly use in commercial. All samples were obtained as paddy rice from the Rice Research Institute, Department of Agriculture, Pathum Thani, Thailand. A McGill sample sheller (dehusker) and a McGill No.2 mill were used to remove hull and bran. Samples were milled to a constant degree of milling (DOM = 90). The DOM was measured using a Satake Milling Meter MM-1B (Japan). Rice was ground using an ultra-centrifugal mill type ZM 1 (Retsch, Haan, Germany) until a particle size of 150 microns was reached. For the adulteration

3 Chiang Mai J. Sci. 2011; 38(1) 107 study, KDML 105 was mixed with PTT 1 and CNT 1 at content of 5, 8, 10, 15, 20, 25 and 30% according to commercial practice (interview with C.P. Intertrade Co., ltd, 2006). 2.2 Chemical Analyses Amylose content was determined by the starch-iodine blue method [15]. Milled rice was ground into flour and then dispersed in water. Treated with ethanol and sodium hydroxide. The solution was heated for an hour and allowed to remain at room temperature overnight. The ph was then adjusted using acetic acid and then adding a solution of iodine. Amylose formed a complex with iodine. Color intensity, measured by a spectrophotometer, was then correlated with amylose content. Adulteration level was determined using the dyed grain method [2]. Whereby 3g of rice samples were soaked in Isopropyl alcohol, rinsed clean with distilled water, and then soaked in color dye; the dye was prepared from 30 milliliters of working solution (1N NaOH and 1N acetic acid), 3 drops of Thymol blue as the indicator solution and a solution of 0.05N NaOH, added drop by drop until the solution changed to a light blue color. Next, 0.05N of acetic acid was added and mixed well until the blue color of the solution disappeared. Then 1.5 ml of a solution of iodine was added and reaction mixture was shaken. The dyeing process took about 10 minutes. The different types of rice will form different coloration levels with dye. Other kinds of white rice will turn more purple than Jasmine rice. These kernels were then counted and calculated as percent of non-jasmine rice. This method is a standard method for rice adulteration detection applied in the Thailand Rice Research Institute. 2.3 RVA Analyses Pasting properties of the various samples were determined using the Rapid Visco Analyzer (Model 3D, Newport Scientific, Warriedwood, Australia) by approved Method [16]. Rice flour dispersion (10%, db) of 3g was heated from 50 to 95 o C at a rate of 12.2 C/min, held at 95 o C for 2.5 min, cooled to 50 o C at 11.2 o C/min, and held at 50 o C for 2 min. The RVA instrument provided the following parameters: peak viscosity (PV), trough (T), breakdown (BD), final viscosity (FV) and setback (SB). All values were reported in Rapid Visco Analyzer units RVU. 2.4 Instrumental Texture Analyses Mixed rice samples (33%, db) of 300g were cooked using a rice cooker (National, model SR-D10HN, Thailand). Texture of cooked rice was measured with a Texture Analyser model TA-XT2 (Stable Micro Systems, Surrey, England) using the compression test [9]. Cooked rice was kept in vacuum flask container to maintain its temperature between o C. 1 g of cooked rice was then arranged in a single-grain layer on the base plate. A compression plate was set at 5 mm above the base. A two-cycle compression, force versus distance program was used to allow the plate to travel 4.9 mm, return and repeat. The test speed was 1 mm/ sec. A cylindrical plunger of 50 mm diameter was employed. Parameters were hardness (height of the force peak on the first cycle), adhesiveness (negative force area of the first cycle), cohesiveness (ratio of the positive force areas under the first and second cycles), springiness (ratio of distance traveled by the plunger on the two cycles that relates to sample recovery after first compression) and gumminess (derived by multiplying hardness by cohesiveness). All measurement were carried out in triplicates.

4 108 Chiang Mai J. Sci. 2011; 38(1) 2.5 Statistical Analyses Multivariate analysis software, SPSS version 10.0, significance differences between means were detected by the Duncan s multiple range test (p<0.05). The pasting and textural variables were standardized prior to the analysis. Principal component (PC) that explained a total variance greater than 85% was selected. The varimax rotation method was applied. After that the predictive model of rice adulteration was obtained by using regression analysis, stepwise method. The prediction was performed according to the level of adulteration rice by its pasting properties and texture attributes, extracted from previous factor analysis. 3. RESULTS AND DISCUSSIONS 3.1 Adulteration Detection using the Convention Method The mixtures of rice of KDML 105 with PTT 1 and CNT 1 at 5, 8, 10, 15, 20, 25 and 30% were detected using the conventional dyed grain method. Results showed that the dyed grain method could predict Jasmine rice adulterated with CNT 1 more accurately than with PTT 1 (Table 1). The error ranged from -5.9% to + 0.4% for CNT 1, where it ranged from -30.0% to -5.0% for PTT 1. Most errors were under estimates. The dyed grain method discriminated grain by its amylose content. High amylose rice yielded darker blue color than low amylose rice did. As a consequence, problems arose when adulterated with PTT 1, the frequently used variety, which was very close to KDML 105 in terms of its amylose content and grain appearance. 3.2 Texture and Pasting Properties of Mixed Rice Pasting properties of mixed rice measured by RVA are shown in Table 2. Results indicated that peak viscosity, final viscosity and setback varied in accordance to level of adulteration of both CNT 1 and PTT 1. However, peak viscosity was higher in KDML 105 mixed with high amylose Table 1. Adulteration prediction of Jasmine rice using dyed grain method. Rice variety Adulteration (%) Predicted adulteration (%) Error CNT PTT

5 Chiang Mai J. Sci. 2011; 38(1) 109 Table 2. Paste viscosity properties of Jasmine rice mixed with CNT 1 and PTT 1. Rice Adulteration Peak Trough Breakdown Final Setback variety (%) viscosity viscosity (RVU) (RVU) (RVU) (RVU) (RVU) CNT a cd a a a ab b a a b c a b a bc c a bc a c ab d ab b d bd d bc c e cd c bc c e PTT a a a a a a ab a ab a ab c b c ab abc c c cd bc d d d e cd d de c ef cd d de a f e Note: Superscripts with the same letters in column do not differ significantly (p< 0.05). content rice (CNT 1) than that with low amylose content rice (PTT 1). Han and Hamaker [17] reported that amylose content correlated negatively with starch granule swelling. Starch granules of low amylose content swelled at a high and consistent rate compared with that containing medium and high amylose content. On the contrary, setback and final viscosity of KDML 105 mixed with CNT 1 was higher than that with PTT 1 because of the higher amylose retrogradation. Textural properties of the mixed rice are shown in Table 3. The higher adulteration level the more increase in hardness and gumminess whereas decreased in adhesiveness in both CNT 1 and PTT 1. KDML 105 mixed with CNT 1 showed higher affect on hardness and gumminess. As a general rule, cooked rice of higher amylose content was much harder and its paste was less viscous [18]. In a contrary, adhesiveness decreased with the increase in adulteration level of both varieties, however the decrease was more apparent in PTT 1. Adhesiveness was reported to decrease as amylose content increased [19]. Cohesiveness, which indicated stickiness of cooked rice [8], was not statistically different at different levels of adulteration in both varieties. Singh et al. [20] concluded that the differences in textural properties among various rice varieties were mainly accounted for by their amylose contents. 3.3 Adulteration Prediction Model Applying principal component analysis using varimax rotation, variables were grouped into two components for CNT 1 and PTT 1. Table 4 showed factor loading matrix and the variance explained obtained for CNT 1 and

6 110 Chiang Mai J. Sci. 2011; 38(1) Table 3. Texture properties of KDML 105 mixed with CNT 1 and PTT 1. Rice Adulteration Hard Cohesive Adhesive Springi Gummi variety (%) ness ness ness ness ness a 0.44 ns 0.43 a 0.63 a 7.15 a a 0.42 ns 0.41 a 0.68 a 7.14 a b 0.42 ns 0.38 b 0.66 a 9.09 b CNT c 0.44 ns 0.35 b 0.66 a bc d 0.42 ns 0.31 c 0.69 a d e 0.42 ns 0.30 c 0.67 a e e 0.43 ns 0.19 d 0.74 b e a 0.46 ns 1.78 a 0.81 a 3.63 a a 0.46 ns 1.38 b 0.82 a 3.95 b ab 0.42 ns 1.36 c 0.82 a 3.91 b PTT ab 0.42 ns 1.34 c 0.84 a 3.95 b bc 0.43 ns 1.17 d 0.78 b 4.63 c bc 0.44 ns 1.14 d 0.79 b 4.84 c bc 0.42 ns 0.97 e 0.83 a 4.64 c Note: Superscripts with the same letters in column do not differ significantly (p< 0.05). Table 4. Factor loadings of rotated paste viscosity and texture properties of Jasmine rice adulteration. Variable Chai Nat 1 Pathum Thani 1 PC 1 PC 2 PC 1 PC 2 Setback Hardness Gumminess Final viscosity Adhesiveness Breakdown Springiness Trough Peak viscosity Cohesiveness Variance (%) Cumulative (%)

7 Chiang Mai J. Sci. 2011; 38(1) 111 PTT 1. The highest explain variance (PC1) for CNT 1 adulteration was associated with setback, hardness, gumminess, final viscosity and springiness in one direction while breakdown and adhesiveness in opposite direction. Its explain variance equaled to 58.5%. The second factor (PC2) was associated with trough and peak viscosity with its explain variance equaled to 23.1%. The highest explain variance or PC1 for PTT1 adulteration was associated with hardness, final viscosity, trough, peak viscosity, setback, gumminess and adhesiveness. Its explain variance equaled to 71.7%. The second factor (PC2) was springiness, breakdown and cohesiveness with its explain variance equaled to 18.8%. These two factors jointly could account for 81.6% and 90.5% of the total variance explained for CN1 and PTT1 adulteration respectively. The factor scores for the first two principal components of CNT 1 and PTT 1 were presented in Figure 1. It showed that mixed rice of KDML 105 with CNT 1 and PTT 1 were grouped according to their adulteration level: 5-8%, 10-15% and 20-30%. The first principal component which accounted for most of the explain variance was clearly the most differentiate of these 3 groups. The array of factor 1 loading continuously moved from negative to positive Chai Nat 1 Phathum Thani Figure 1. The score plot of the first two principal components of the adulteration level of the mixed rice of KDML 105 with CNT 1 and PTT 1 obtained by PCA.

8 112 Chiang Mai J. Sci. 2011; 38(1) values as the percentage of adulteration increased in both varieties. Factor loading plots which specified the location of each adulteration level were presented in Figure 2. Low levels of adulteration in both varieties (5-15%) were associated with adhesiveness. Whereas high level of adulteration were positively associated with hardness and gumminess for texture attributes and with setback, peak viscosity and final viscosity for pasting properties. Low adulteration rice still exhibited its adhesiveness which was apparent in Jasmine rice s eating quality. High adulteration rice (20-30%) lost its adhesiveness. It was harder and more gumminess while the pasting properties such as setback and final viscosity were well associated with those texture properties. Setback was generally known to correlate with starch retrogradation resulted in hardness of starch gel [21]. Regression analysis, stepwise method was then undertaken to determine factors that explain the level of rice adulteration via texture and pasting measurement and adopted to compute the Chai Nat 1 Phathum Thani 1 Figure 2. The scores and loadings plot of the first two principal components of the mixed rice of KDML 105 with CNT 1 and PTT 1 obtained by PCA.

9 Chiang Mai J. Sci. 2011; 38(1) 113 regression equations. The results of regression analysis showed that the linear model gives the best fit for the sample data studied. Correlation analysis presented in Figure 3 showed positive linear correlation between adulteration level and texture/pasting properties. The coefficient of determination (R 2 ) for CNT 1 and PTT 1 were and 0.921, respectively. This coefficient gave the proportion of the total variation in the dependent variable explained by the predictors included in the model. Chai Nat 1 Phathum Thani 1 Figure 3. Plot of correlation of adulteration level versus texture and pasting properties. The test statistic in linear model (Table 5) showed that the first factor (PC1) associated with setback, hardness, gumminess, final viscosity, breakdown, adhesiveness and springiness positively and significantly influenced the adulteration with CNT 1 (P < 0.05). As for the adulteration with PTT 1, the first factor (PC1) was significantly associated with the adulteration of CNT 1 and PTT 1 at p = 0.00 which indicated reliable prediction. Table 5. Linear regression equations to predict Jasmine rice adulteration. Rice adulteration varieties β 0 β 1 R 2 Sig Chai Nat Pathum Thani CONCLUSION Adulteration of low and high amylose rice cultivars with Jasmine rice resulted in a decrease or increase of its amylose content. This affected the pasting and textural properties of mixed rice. Classification adulteration of Jasmine rice using factor analysis confirmed that physico-chemical properties could be used as more accurate

10 114 Chiang Mai J. Sci. 2011; 38(1) parameters to classify adulteration of Jasmine rice. The use of a physico-chemical properties in combination with multivariate analysis techniques provided a satisfactory classification for adulteration of Jasmine rice. ACKNOWLEDGEMENTS This research was funded by Kasetsart University Research and Development Institute year Additional thanks go to the Thailand Rice Research Institute, Department of Agriculture and C.P. Intertrade Co., ltd. for their information and support. REFERENCES [1] Agricultural statistics of Thailand, Center for Agricultural Information, Office of Agricultural Economics, Ministry of Agriculture and Cooperatives, Bangkok, Thailand. Published online at www. oae.go.th. crop year 2003/ [2] Department of Foreign Trade Thai Hom Mali Rice Standards. Notification of Ministry of Commerce. 2002, The Royal Gazette, Vol. 119, Special Part 55d, 21 June, B.E [3] Sitakalin C. and Meullenet J.F.C. Predict of cook rice texture of using extrusion and compression test in conjunction with spectral stress strain analysis, Cereal Chem, 2000; 77: [4] Champagne E.T., Rice starch: composition and characteristics. Cereal Food World, 1996; 41: [5] Ramesh M., Ali S.Z. and Bhattacharya K.R., Structure of rice starch and its relation to cook-rice texture, Carbohydrate Polymers, 1999; 38: [6] Pearce M.D., Marks B.P. and Meullenet J.F., Effects of postharvest parameters on functional change during rough rice storage, Cereal Chem, 2001; 78: [7] Okabe M. Texture measurement of cooked rice and its relationship to the eating quality, J. Texture Stud, 1979; 10: [8] Juliano B.O. and Pascual C.G., Quality characteristics of milled rice grown in different countries, IRRI Res. Paper Ser. 48, Los Ban ~ os, Laguna, Philippines, International Rice Research Institute, [9] Champagne E.T., Bett-Garber K.L., Vinyard B.T., McCiung A.M., Barton F.E., Moldenhauer K., Linscombe S. and MckenZie K.S., Correlation between cook rice texture and Rapid Visco Analyser measurement. Cereal Chem, 1999; 76: [10] Meullenet B.P., Griffin K. and Daniels M.J., Effects of rough rice drying and storage conditions on sensory profiles of cooked rice, Cereal Chem, 1999; 76: [11] Reid L.M., O Donnell C.P. and Downey G., Potent of SPME-GC and chemometrics to detect adulteration of soft fruit purees, J. Agric. Food Chem, 2004; 52: [12] Marina C., Caterina D., Giorgia F., Andrea M., Lorenzo T. and Alessandro U., Durum wheat adulteration detection by NIR spectroscopy multivariate calibration, Talanta, 2006; 68: [13] Hair J.F., Anderson R.E., Tatham R.L. and Black W.C., Multivariate Data Analysis with Readings 4 th (ed.), New Jersey: Prentice Hall Inc., [14] Johnson R.A. and Wichern D.W., Applied multivariate statistical analysis, New Jersey, Prentice Hall Inc., [15] Juliano B.O., A simplified assay for mill-rice amylase. Cereal Sci Today, 1971; 16: [16] American Association of Cereal Chemists, Approved Methods of the American Association of Cereal

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