Estimating the starch-iodine blue value of residualliquid of rice by near-infrared reflectance spectroscopy. Bo-Jein Kuo 1 ) Mei-Chu Hong 2 )

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1 ,l 林學報 50(3) : 67 ~77(2001) - 67 一 Estimating the starch-iodine blue value of residualliquid of rice by near-infrared reflectance spectroscopy Bo-Jein Kuo 1 ) Mei-Chu Hong 2 ) (Accepted for publication: August 22, 2001) Abstract: Starch-iodine blue value of residual liquid (BV) could be used to predict the palatability of rice The objectives of this study were to evaluate the potential of Near-infrared ref1ectance spec 甘 oscopy (NIRS) to estimate BV of rice and compare the perfoηnance of model-building and prediction in developing the calibration equation using multiple linear regrcssion (MLR), principal component regression (PCR), and partial least squares regression (PLSR), respectively A total of 238 rice samples, including indica, japonica, and waxy rices, were employed in analyzing the BV Thc 4-wavelength MLR model gave the highest correlation coefficient and the lowest standard eπor of prediction than PCR and PLSR models ln other words, This MLR model had the best predictive ability in estimating the BV of rice The predictive ability of thc PCR modcl with 17 principal components was slightly better than that ofthc PLSR model with 6 components However, The IRV value in MLR model (82042) was much larger than that in PCR (18331) model and PLSR (8356) model, suggesting that more random noise was involved in MLR model than that in other models Generally speaking, the performances of model-building and prediction demonstrate that calibration for 8V is less accurate and NIRS can be used only for the first approximation of 8V in the rice breeding program Ke 句 y Words: s 剖 tarc 吋 h- 恥 叩 din 悶 1 唸 eb 划 lu 叫 c value ofr 閃 E 臼叩 s 剖 id 山 ua 訓 I1 炯 1 叫 qu 山 lì d ( 伯 BV 叫 ), NI 服 RS, rηml 孔 m 叫 u 叫 JI 山圳 lt 圳 tl 忱 l 叩 c o 州 llinea 訂 n 川 l 此昀 ty, MLR, PCR, PLSR INTRODUCTION Rice (Oryza sa/im L) is thc stapk; food in Taiwan Howevcr, the consumption of rice m Taiwan is declined sharply because of the impact of westem culture and industrialization To increase the rice consumption, producing the rice with high palatability quality has become one of the most important strategies Rice breeders in Taiwan have widely employed taste panel test to evaluate the palatability of cooked rice Furthermore, only small quantities of sample could be available for evaluatior> in ear1y generation Hence, the exploration of the relationship 1) Agronomy Departmer 此, National Chung Hsing University, Taichung 402, Taiwan ROC (Corresponding author) 2) Taichung District Agricultural Improvement Station 司 Changhwa, Taiwan, ROC

2 Estimating the starch-iodine blue value ofresidualliquid ofrice by near-infrared re f1ectance spectroscopy between some physiochemical properties and palatability might be feasible to screen the rice lines calibration equation using MLR, PCA, and PLSR, respectively with high palatability (Chang and Lii, 1985; Kuo et 泣, 1987; Kuo et a1, 2001a; Lii et a1, 1986; Liu et al, 1988) Chikubu et al (1985) found that protein content, maximum viscosity, minimum viscosity and breakdown viscosity in amylographic characteristics of milled rice flour, and starch-iodine blue value of Materials and Methods A total of 238 rice samples were employed in this residual liquid (BV) were the most important study These samples, including cultivars or lines, characteristics for predicting the palatability of rice consisted of indica, japoni 間, and waxy rices The Conventional chemical analysis of BV is samples were threshed and dried ovemight at 45'C to time-consuming and inconvenient Therefore, establishing a fast and convenient screening technique is needed for breeding the rice with high palatability Near-infrared reflectance spectroscopy (NIRS) is a fast and convenient technique widely used on the analysis of several rice quality traits (Chen, 1996; Delwiche et 刻, 1995, 1996; Kuo et al, 2001b; Li and Shaw, 1997; Villareal et al, 1994; Windham et al, 1997; Wu et al, 1997, 2000) However, the feasibility of NIRS technique in estimating the BV is not well documented Kuo et al (200Ib) demonstrated that NIRS was unsuccessful in estimating the BV of ground milled ri 白 They also found that MLR model had the best predictive ability in estimating the BV of ground milled rice MLR showed large random noise due to the serious multicollinearity problems among the selected wavelengths Jn addition, the in t1 ated variability of the estimated regression coefficients was found in MLR model and the change of model performance was significant after deleting the outliers PCR performed more stable than MLR, while PLSR was the most robust calibration method The objectives of this study were to evaluate the potential of NIRS to estimate starch-iodine blue value of residual liquid and compare the performance of model-building and prediction in developing the approximately 140 士 0 5% moisture content The rough rice was hulled with a Satake testing husker and milled with a McGill #3 mill for 60 sec Then, the milled rice was ground with a cyclone grinder (Udy, Fort Collins, CO, USA) equipped with a 025 mm screen Eight grams of milled rice were placed in a small cylinder made of 20-mesh metal wire gauze The metal wire cylinder and contcnts were slowly put into a 300 ml beaker containing 160 ml distilled water The beaker was placed into a boiling water bath for 20 min After cooking, the cooked rice was drained for 10 min and the residual liquid was saved for later use One ml of the cold residual liquid was made up to 100 ml with distilled water Two ml iodine solution were added and shaken wel l 甘 le absorbance of the BV was measured with a colorimeter at the wavelength of 600 nm A Bran + Luebbe In 什 aalyzer 500 U ωrderstedt, Germany) was used to collect spectrum measurement for each ground sample Each rice t10ur sample in a c10sed standard cup was scanned from 1,100 to 2,500 at 4-nm intervals The re t1 ectance (R) spectrum readings were recorded and transformed to log (IIR) MLR, PCA, and PLSR were performed on the spectra and coηesponding BV to develop the calibration equation (Bran + Luebbe) 甘 le stepwise search procedure was used to find the best one-wavelength

3 林學報 50(3) : 67-77(2001) MLR model, the best two-wavelength MLR model, set, respectively The performance of model-building and so on This procedure continued until the best and prediction was evaluated using the standard eπor 9-wavelength MLR model was found The number of of calibration (SEC), correlation coefficient calculated cross-validation segments (CVS) was 5 for PCR and from calibration set (r 叫 ), correlation coefficient PLSR The standard eηor of prediction was calculated calculated from validation set (rval ), the standard eηor over alj cross-validation segments for the first 20 of predíction (SEP), and the average dífference components (SEPcv)' The optímal component number between chemical and NIR predicted values (bias) of PCR and PLSR was determined by SEPcv value The index of random variatíon (IRV) was calculated as (Bran+Luebbe) The samples were divided into the 呵 uare root of the sums of the squar 的 of the calibration and validation sets by actual BV The actual regressíon coefficients except the íntercept BV of the whole samples was sorted by ascending order In other words, the samples were arranged 合 om lowest to highest according to the actual BV Starting with the five samples with the lowest actual chemical values, the odd samples were assigned to the Results and Discussion calibration set, and the even samples were assigned to Ihe validation set The other samples were assigned in the same way until 3 samples wilh the highest actual chemical value were remained Two odd samples were The descriptive statistics of the calibration and validation sets are listed in Table 1 The BV in calibration set ranged from 0009 to 0220 with an average of 0109 The BV in validation set ranged assigned to the calibration s 前, and one even sample from 0011 to 0207 with an average of 0108 AlI was assigned to the validation set There were 145 samplcs in calibration set and 93 samples in validation values in validation set are within the calibration set range Table 1 Summary of descriptive statistics for starch-iodine blue value of residual liquid of ground milled rice sample in calibratíon and validation sets Set n Mean Min Max SO SO,,,, Calibration Validation SO,ot = Standard deviation of samples of calibration and validation Fig I A shows Ihe effect of standard e 叮 or and dramatically to 0869 The 4-wavelength model was corrclation coefficient when MLR was used 10 develop recommended by means of SEP and rval values As the calibration equation As the number of shown in Fig 1 B, the model performance of PCR, 的 wavelengths was increased to 4, the value of SEP evaluated by an increase in ryal value and a decrease in decreased to 0025 and the value of rval increased SEP value, increased readily up to 10 components The

4 B 旬扭的 山司 c何m可h OK002-7 一 Estimating the starch-iodine blue value of residual liquid of rice by near-infrared reflectance spectroscopy A 006 = 盈 005 k山em司ce的 : 三 wavelength no 8 10 山百 仿C冒 日 5 1 日 15 components 20 日 mc 日 15 components 20 Fig 1 The plot of model-buliding and predictive ability performance for the blue val ue of residual liquid by different statistical methods

5 4c γ2農林學報 50(3) : 67-77(2001) component model was chosen as optimal in terms ofthe SEPcv value This recommended PCR model had the rvol and SEP of 0841 and 0028, respectively Fig model with 6 components in items of rval and SEP values These results are consistent with the findings of K uo et al (200 1 b), who showed that the best 1 C presents the performance of standard error and calibration and prediction of BV were obtained by the correlation coefficient using PLSR in developing the calibration 問 uation As the number of components MLR model with 9 wavelengths, which produced a rv 國 of 0754 and a SEP of 00437, respectively 甘 le was increased to 6, the value of SEP declined to 0028 samples had the readings of BV between 0009 缸 ld and the value of rval increased to 0828 Six 0303 Although the calibration equation developed in components were included to develop the optimal calibration equation by PLSR method this study had better predictive ability, the equation could only apply to a n 甜 ow range ( ) Kuo Table 2 summarizes the model perfoηnance of et al (2001 的 also found that the change of BV by different statistical methods The IRV value in MLR modej (82042) was much larger than that in PCR (18331) model and PLSR (8356) m 吋 el, suggesting that the more random noise was involved in MLR model than that in other models The 4-wavelength MLR model gave the highest rval value and the lowest SEP value than the other models The predíctíve abílity of PCR model with 17 principal components was slightly better than that of PLSR perform 個 ce in model-building and prediction by PLSR was not significant after removing the outlying observations Therefore, PLSR was a robust approach Moreover, PLSR might achieve the optimal model with a small number of components than PCR Generally speaki 月, the performance in model-building and prediction for BV was still less accurate Table 2 Thc mod 釘制 E 叫 1 pe 叮 rfì 臼 ormar 叩 nc 臼 e for s 泣 tarc 吋 h 扎 1 于 - 刊 din 附 1 唸 e blue value of r 昀 E 臼叩 5 剖 idua 訓 1 liquid by dif 仔 fer 閃 ent s ta 剖甜 t1 泊叫帥 s 甜 t1 蛇 ca 訓 1 methods Method Term l ) Eπn } 勻a SEp 6 ) rv 剖 η SEC 3 ) IRV 4 ) SEP c /) Bias las 8 ) MLR PCR PLSR ) Number ofwavelengths or components 21 rcal = correlation coefficient of calibrat>on set 3) SEC = standard eπor of calibration 41 IRV = index of random variation ' 1 SEPcv = standard error of cross-validation 6) SEP = slandard error of prediclion 71 r,al = correlatíon cocfficient of validation set 8) Bías = the average differencc between predicted and chemical values

6 Estimating the starch-iodine blue value of residualliquid of rice by near-infrared retlectance spectroscopy The scatter plots of chemical value and NIR Several authors have indicated that multicollinearity predicted value for both data sets are shown in Fig 2 among the independent variables sometimes cause The number of wavelengths and components selected severe problems in multiple regression Especially, the in calibration set was also used in validation set When estimated regression coefficients could be very the validation set was used, the NIR values predicted unstable (Montgomery and Peck, 1982; Neter et al, by MLR modellie close to the ideal regression line, ie 1996) In this study, MLR exhibited extremely large a line through the origin and with a slope of 1, than value of IRV than the value in PCR and PLSR (Table those predicted by PCR and PLSR models (Fig 28, 0, 2), suggesting that the multicollinearity problems and F) This finding also confirmed the best predictive existed among the selected wavelengths when the ability of MLR model MLR model was used Although PCR solves the The residual plots for calibration and validation multicollinearity problem and eliminates the lesser sets are shown in Figure 3 These residuals tluctuated components, some useful information may be lost in a random pattem around the base line 0, displaying through the discarded components (Geladi and no systematic tendency This also indicates regression Kowalski, 1986) However, PLSR may use the first models are appropriate All residuals calculated from fewer componcnts to achieve the optimal mod'!l MLR model based on the validation set randomly performance (Helland, 1988) distributed between -005 and 005 (fig 38) Howev 缸, some residuals calculated from PCR and PLSR models fell outside the range between -005 and 005 (Fig 3D and F) In MLR model, the wavelengths at 2036, 2192, 2296, and 2316 nm were selected, indicating that principal spectral region of 8V was within the longer wavelength region (Fig 4)ηlÍ s was congruent with the finding of Su et al (200 J), who devejoped an optimaj MLR modej with wavelengths at 2264, 2280, 2296, 2332, 2356, 2380, 2416, 2424, and 2472 nm However, the intlated variability of the estimated regression coefficients was found in MLR model, suggesting that the multicollinearity among the wavelengths reduced the precision of the estimated regression coefficients In other words, the estimatcd regression coefficients tend 10 become eπalic from sample to sample PLSR developed a more stablc calibration model Consequently, these eslimatcd regression coefficients calculated by PLSR were more close to the true parameter values (Kuo et al, 200Ib) Conclusions ln this study, 4-wavelength MLR modcl gave the highest correlation coefftcient and the lowest standard eπor of prediction than PCR and PLSR models ln other words, this MLR model had the best predictive ability in estimating the BV of rice The predictive ability of the PCR model with J 7 principaj components was slightly better than that of the PLSR model w 的 6 components Howev 釘, The (RV value iil MLR model was much larger than that in PCR model and PLSR model, indicating more random noi sc was involved in MLR model than that in other modcls The performances of model-building and prediction demonstrate that calibration for BV is less accurate and NJRS can be used only for the first screening of BV in the rice brceding program

7 2--duDC ad開 --mesa有 農林學報 50(3) : 67-77(2001) 一 73 一 A 當--司 3們-活一伺 由 MLR(Cal) 一向 向開兩 由 I _! -" '~' 守午, -yyd 已 1JJ /' 吋 / 屯 3 B MLR(Val) 由 呵由 由 P' 甜 icted v, 到 ue p!ed iç 祖 d' 品 三 / -_,,/, -;且,-JdF4l e 毛咚, J 一 / 一, PCR(Val) 1 1 w 05 咱 O 羽田 由 G 由 由 predided value D 留d-一何 -3個一U司開PCR(Cal) p!ed iç 揖 dvalue E 03 PLSR(Cal) F PLSR(Val) 由 O UJFYh - '_w 冉 ACZtf 1'1(";' 一 z 爭 " 由 目 由 ~ -/ J- / '/, a J,μf <- -1! ', F-ie 戶y, 1 刊由 pred 回 ed value p!ed iç 絕 dv 酬, Fig 2 The relationship between chemical and predicted values of starch-iodine blue value of residual Iiquid analyzed by MLR, PCR, and PLSR for the calibration set and validation set, respectively CaI == caiibration 5 仗, VaI = validation set

8 -Z哥22-74 一 Estimating the starch-iodine blue value of residualliquid of rice by near-infrared re f1ectance spec 甘 oscopy A 15 MLR(Cal) B 15 MLR(Val) $ o I 且 -- -_-_ - ' '_, 之, ~ "# \ _ 吧 _ ~ _ J r-_, J_- 用于 de " ' 0 個 1 有曲 -會 1,E 32蝠但 一 ~ - o~', / 元叫 f l _ - 1; a" _,, í 一 b 一 - 嘲 ~ 呵 50 曲 1 個 曲 岫 ob6no 出闇 C 15 PCR(Cal) nnd 15 1-他PCR(Val) 1 有"t l_- _ o~- 三 : ~ _- -_ '-~ _~ -,_ ~ 占 ~~ -_ 代矢,',?? AU' a -- '_ :- 一 g舟, 晶 由 xω心 一 _ -- 司 一 ----: 一, 呵 個 目 obsno obs 間 E PLSR(Cal) F PLSR(Val) 可 S 個 t -' - 一 ~ _ o i-"- ~ I - -, 哇 : 一, _- - ~ -_ ~ _ L -! 1- _ - 可 - 一 I - : - ë - -_- ~ 05 " 1 有益 也 01 由 ' o~ 占, _ p- rþ,'"'," 咕 呵 eo bs 間 obs_no Fig 3 The residual plot of starch-iodine blue value of residual liquid analyzed by MLR, PCR and PLSR using calibratin set and validation set, respectively Cal = calibration set, Val = validation set

9 農林學報 50(3) : 67-77(2001) 一 可 E 由 E g -20 : L, " " " " "~']"'J wavelengh Fig 4 The regression coefficients of principal wavelengths for starch-iodine blue value of residual liquid using MLR method References 1 Bran+Luebbe 1996 Sesame Software Operation Manual Bran+Luebbe GmbH, Norderstedt, Germany 2 Char 池, S M and C Y Lii 1985 The pasting behaviors and the eating qualities of ωme rices in Taiwan Bull Inst Chem, Academia Sinica 32: Ch 凹, Y S 1996 The application of NIR for rice quality evaluation in Taiwan In Proc 2nd Asian Crop Science Conference" eds R Ishii and T Horie, Fukui, Japan 4 Chiku 恤, S, S Watanabe, T Sugimoto, N Manabe, F Sakai and Y Taniguchi 1885 Establishment of palatability estimation formula of rice by multiple regression analysis J Jpn Soc Starch Sci 32: Delwiche, S R, M M Bean, R E Miller, B D Webb and P C Williams 1995 Appar,ent amylose content of mi l1ed rice by near-infrared reflectance spectrophotometry Cereal Chem 72: Delwiche, S R, K S Mckenzie and B D Webb 1996 QuaJity characteristics in rice by near-in 命 ared reflectance analysis of whole-grain milled samples Cereal Chem 73: Geladi, P and B R Kowalski 1986 Partialleast-squares regression: a tutorial Anal Chimica Acta 185: Hel1and, 1 S 1988 On the structure of partial least squares regression Comm Statist Simula Comput 17(2):

10 一 76 - Estimating the starch-iodine blue value of residualliquid of rice by near-infrared reflectance spec 甘 oscopy Kuo, Y C, C Liu and S Song 1987 甘 le relationship between human panel testing score and physical properties of cooked rice 1 Agric Res China 36: Kuo, B 1, M C Hong and F S Thseng 2001a The relationship between the amylographic characteristics and eating quality ofjaponica rice in Taiwan Plant Pr 吋 Sci 4(2): 回 11 Kuo, B J, K C 缸, M C Hong and FS Thseng 2001b Predictior> of quality trait in rice using different statistical methods via near-infr 訂 ed spectroscopy Breed Sci (accepted) 12 Li, W S and J T Shaw 1997 Determining the fat acidity of rough rice by near-infrared reflectance spectroscopy Cereal Chem 74: Lii, C Y, S M Chang and H L Yang 1986 Correlation between the physicochemical properties and the eating quality of milled rice in Taiwan Bull Inst Chem, Academia Sinica 33: L 凹, Y 1, L H Lin, S Song and M C Hong 1988 Studies on the chemical quality of Taiwan rice and its relationship with eating quality J Agric Res China 37: Montgomery, D C and E A Peck 1982 Multicollinearity In Introduction to Iinear regression analys 路 ", Wiley Interscience, USA 16 Neter, 1, M G Kutner, C 1 Nachtsheim and W Wasserman 1996 Applied Iinear statistical models 4th ed IRWIN, USA 17 Su, K c, B J Kuo and M C Hong 2001 甘 le comparison of stepwise regression and principal component regression in near-infrared spec 甘 oscopy 1 Agric Assoc China 2: Villare 訓, c P, NM De La Cruz and BO Juliano 1994 Rice amylose analysis by near-infrared transmittance spec 訂 oscopy Cereal Chem 71 : Windham, W R, B G Lyon, E T Champagne, F E Barton 11, B D Webb, A M Mc c1 ung, K A Moldenhauer, S Linscombe and K S Mckenzie 1997 Prediction of cooked rice texture quality using near-infrared reflectance analysis ofwhole-grain milled samples Cereal Chem 74: Wu, Y P, J S Hsi 帥, Y S Chen and Y C Huang 1997 Availability of near infrared reflectance spectroscopy and cold alkali solution method for determining the chemical components in rice Chinese Agron J7: Wu, Y P, C S Sheu and Y S Chen 2000 Chemical components analysis in brown rice by near infrared reflectance spectroscopy J Agric Assoc China 1 :

11 農林學報 50(3) : 67~77(2001) 一 77 一 利用近紅外線反射光譜估計 稻米炊飯液碘呈色度 郭寶錚 1) 洪梅珠 2) ( 接受刊載日期 中華民國 90 年 8 月 22 日 ) 摘要 : 炊飯液碘呈色度此一特性可被用來預測稻米的嗜口性本研究的目的在於評估利用近紅外線反射光譜研究估計稻米炊飯液碘呈色度的可能性並比較複線性迴歸, 主成份迴歸及淨最小平方法在建立模式及預測能力上的表現28 個水稻樣品包括干山 稜及儒稻被用來分析炊飯 ; 夜碘呈色度, 並建立檢量線, 結果發現利用 4 個波長的複線性迴歸模式可產生最高的相關係數及最低的預測機差, 也就是說其在建立模式及預測能力上表現最佳利用 17 個主成份所構成的主成份迴歸模式又較 6 個成份所構成的淨最小平方迴歸模式為佳, 但是複線性迴歸中 IRV 值 (82042) 遠大於主成份迴歸 (1 8331) 及淨最小平方迴歸 (8356) 的 IRV 值顯示複線性迴歸模式較其他模式包含更多的隨機干擾綜合言之, 就本研究得知, 稻米炊飯液碘呈色度的檢量線並不夠準確, 在水稻育種上近紅外線反射光譜對稻米炊飯液中碘呈色度僅能做初步估計之用 關鍵詞 : 炊飯液碘呈色度, 近紅外線反射光譜, 共線性, 複線性迴歸, 主成份迴歸 i 爭最小平方迴歸 1) 國立中興大學農藝系副教授 ( 通訊作者 ) 2) 台中區農業改良場副研究員