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1 This rticle is from the My-June 2006 issue of pulished y AACC Interntionl, Inc. For more informtion on this nd other topics relted to cerel science, we invite you to visit AACCnet t

2 Modeling Selected Properties of Extruded Rice Flour nd Rice Strch y Neurl Networks nd Sttistics 1 G. Gnjyl, 2,3 M. A. Hnn, 3 5 P. Supprung, 6 A. Noomhorm, 6 nd D. Jones 5 ABSTRACT Cerel Chem. 83(3): Rice flour nd rice strch were single-screw extruded nd selected product properties were determined. Neurl network (NN) models were developed for prediction of individul product properties, which performed etter thn the regression models. Multiple input nd multiple output (MIMO) models were developed to simultneously predict five product properties or three product properties from three input prmeters; they were extremely efficient in predictions with vlues of R 2 > All models were feedforwrd ckpropgtion NN with three-lyered networks with logistic ctivtion function for the hidden lyer nd the output lyers. Also, model prmeters were very similr except for the numer of neurons in the hidden lyer. MIMO models for predicting product properties from three input prmeters hd the sme rchitecture nd prmeters for oth rice strch nd rice flour. Food extrusion process modeling hs een difficult tsk due to its complexities. Vrious reserch efforts intended to model the process hve een more mchine- nd product-specific. The whole process cn e viewed s consisting of set of input prmeters such s rw mteril chrcteristics, moisture content, feed rte, screw speed, screw configurtion, nd rrel temperture; system prmeters such s residence time, specific mechnicl energy, nd pressure uild up; nd product properties such s rdil expnsion, mechnicl properties, nd chemicl properties. These prmeters re interdependent. Mny reserchers hve tried to relte input prmeters nd output prmeters, minly using regression models to fit the experimentl dt. Some reserch efforts hve concentrted on using regression nlysis to predict system prmeters from input prmeters. Two pproches hve een followed in modeling extrusion opertions: dynmic modeling nd stedy stte modeling. Dynmic modeling (Levine et l 1986, 1987) descries the rection of process immeditely fter perturtion (10 15 sec) nd is prticulrly useful for control nd utomtion, while stedy stte modeling descries the stte of the process fter period long enough for mchine stiliztion. Between dynmic nd stedy stte models lies the domin of long period ( few minutes) instilities nd metstle sttes (Roerts nd Guy 1986, 1987) tht proly cn e explined y qulittive models. Most studies directed towrd understnding trnsformtions in extruders hve een empiricl in nture. The most widely used pproch is response surfce methodology. This pproch llows one to estlish mthemticl reltionships etween input vriles nd product properties (Olkku nd Vinionp 1980; Antil et l 1983; Frzier et l 1983; Olkku et l 1984; Fletcher et l 1985). These results re clerly product- nd mchine-specific, nd the conclusions re limited to the scope of the investigtions. Other pproches hve een proposed. Mueser et l (1987) nd Mueser nd Vn Lngerich (1984) proposed system nlyticl 1 A contriution of the University of Nersk Agriculturl Reserch Division, Lincoln, NE Journl Series No This study ws conducted t the Industril Agriculturl Products Center. 2 MGP Ingredients, Inc., Atchison, KS University of Nersk, Industril Agriculturl Products Center, 208 L.W. Chse Hll, Lincoln, NE Corresponding uthor. Phone: Fx: E-mil: mhnn1@unl.edu 5 University of Nersk, Biologicl Systems Engineering Deprtment, 215 L.W. Chse Hll, Lincoln, NE Asin Institute of Technology, Food Engineering nd Bioprocess Technology, P.O. Box 4, Pthumthni, Thilnd DOI: / CC AACC Interntionl, Inc. model for extrusion cooking of strch. Their model distinguished etween process nd system prmeters tht influenced trget product properties (output prmeters). Process prmeters re the operting conditions tht cn e controlled nd mnipulted directly. System prmeters re the properties tht re influenced y the process prmeters nd susequently ffect the product chrcteristics (trget prmeters). It is elieved tht there is n pproprite function to descrie the reltionship etween process prmeters nd system prmeters or etween system prmeters nd trget prmeters. This pproch llows one to compre results otined on the sis of more meningful independent vriles y eliminting the effects of operting conditions, mterils processed, nd extruder lyout nd geometry. In ddition, the informtion is prticulrly useful for the scle-up of extrusion processes. Building on tht model, mny reserchers hve studied the reltionships etween process nd trget prmeters (Trnto et l 1975; Olkku nd Vinionp 1980; Antil et l 1983; Frzier et l 1983; Luny nd Lisch 1984; Olkku et l 1984; Owusu-Ansh et l 1984; Fletcher et l 1985; Bhttchry nd Hnn 1987; Chinnswmy nd Hnn 1988). Only limited numer of studies hve een reported on modeling system prmeters from process prmeters (Ycu 1985; Tye et l 1988) or uilding correltions etween system prmeters nd trget prmeters (Guy nd Horne 1988; Kiry et l 1988; Mueser et l 1987). Though regression techniques re commonly used, difficulties rise when deling with the complex chrcteristics of some systems. Regression is usully limited to liner nd sttic systems, nd conventionl nonliner regression lgorithms re clumsy when hndling systems like the extrusion process with multiple inputs nd outputs. One limittion to trditionl mthemticl modeling is tht the mthemticl reltionships descriing ech process of the system must e closely pproximted to otin good results. Limittions in informtion introduce error in model predictions. Alterntive techniques such s neurl networks (NN) cn reduce this difficulty (Btchelor et l 1997). For nonliner prolems, NN re promising lterntive technique (Borggrd nd Thoderg 1992). NN lern from exmples through itertion, without requiring priori knowledge of reltionships etween vriles under investigtion (Linko et l 1992; Erikineen et l 1994). The dvntge of NN over rule-sed model is tht, if the process under nlysis chnges, new exmples cn e dded nd the NN cn e retrined. This is esier thn determining new models or rules. Moreover, no sttisticl ssumptions re mde on the ehvior of the dt. NN re not known for precision; if precision is less importnt thn speed, NN my e useful. NN models hve performed well even with noisy, incomplete, or inconsistent dt (Bochereu et l 1991). Linko et l (1992) used NN with output feedck nd time delys for the Vol. 83, No. 3,

3 control of specific mechnicl energy on the sis of screw speed for flt red production through twin-screw extrusion cooker. As food extruder is multiple input nd multiple output (MIMO) system, dynmic chnges in torque, specific mechnicl energy, nd pressure were modeled nd susequently controlled using two independently trined feedforwrd rtificil NN (Eerikinen et l 1994). Linko (1998) presented review on the potentil of some novel tools in food process control. NN hve gret potentil s softwre sensors for online, rel-time stte estimtion, nd prediction in complex process control pplictions. Tking into considertion the current sttus of extrusion modeling, the ojective of this reserch ws to model the extrusion of rice flour nd rice strch with NN. The specific ojectives were to develop more roust NN models for prediction of selected product properties from the input process vriles individully for ech property nd to develop multiple input nd multiple output models for simultneous prediction of ll product properties. MATERIALS AND METHODS Rice flour nd rice strch were extruded t three levels of moisture content, screw speed, nd rrel temperture. Broken kernels of KDML 105 rice, s y-product from the milling process, were otined from the Sim Grin Compny, Bngkok, Thilnd. Rice flour ws prepred y wet-milling method nd rice strch ws prepred y n lkline method (Hogn 1967). Extrusions Extrusions were conducted in single-screw lortory cooking extruder (19 mm screw dimeter; L/D rtio 20:1) (C.W. Brender Instruments, NJ). Uniformly tpered screws with nominl compression rtio of 4:1 were used. The zone 3 (die section) rrel temperture ws djusted to the desired tempertures of 140, 170, nd 200 C, wheres zone 1 (the feed section) of the rrel ws fixed t 125 C nd zone 2 ws fixed t 135 C. Screw speeds were 150, 200, nd 250 rpm with fixed feed rte of 40 g/min. The moisture contents of the smples used were 18 ± 2.0, 23 ± 2.0, nd 28 ± 2.0% (w). There were two replictes of ech test run. It ws complete rndomized design with full fctoril rrngement of tretments. Desired moisture level ws chieved y sprying distilled wter s fine mist onto the smples. Smples were then tempered for 20 min in lender nd moisture content ws determined t this point. These smples were seled in plstic gs nd refrigerted t 4 C for one dy. Before extrusion, the smples were rought to out room temperture nd mixed to ssure even moisture distriution. Ech extrusion run ws rought to stedy stte s indicted y constnt torque nd melt tempertures efore smpling nd dt collection. The extrudtes collected were cooled t room temperture for 2 hr nd seled in plstic gs for nlyses (Mrtinez et l 1988). Anlyticl Methods Moisture contents of rice flour nd rice strch were nlyzed y AOAC methods (1984). Men vlues were otined from three mesurements. Expnsion rtio (ER) ws mesured s the rtio of cross-sectionl re of extrudte to tht of the die nozzle (Bhttchry nd Choudhury 1994). Men expnsion rtio of ech smple ws determined from 10 oservtions. Modified procedures of Anderson et l (1969) were used to determine wter sorption index (WAI) nd wter soluility index (WSI) of extrudtes. For determintion of WAI, 0.5 g of extruded nd ground smple (100 mesh) were suspended in 15 ml of distilled wter t 25 C with constnt stirring for 30 min nd then centrifuged t 3,000 rpm for 10 min. Superntnt liquid ws poured into trred evporting dish nd dried t 100 ± 5 C for 4 hr. Weight of the remining gel ws tken s WAI nd expressed s g/g of dry smple. Amount of dried solids recovered y evporting the superntnt ws tken s WSI nd expressed s percentge of dry solids. Experiments were performed in triplicte. Degree of geltiniztion (DG) is defined s the weight rtio of geltinized flour or strch to totl weight of dry smple. DG ws determined using the method of Birch nd Priestley (1973), which is sed on formtion of lue iodine complex y mylose relesed during geltiniztion. Percentge DG ws clculted y the sornce rtio of mylose-iodine complex for smples dispersed in 0.060M KOH compred with respective smples dispersed in 0.4M KOH. Reported results were verges of three replicte nlyses. Initil pek viscosity (IPV) of extrudte smples ws mesured using Rpid Visco Anlyser (Series 4, RVA Newport Scientific Pty. Ltd., Austrli) t 25 C just efore heting. Mesurement ws sed on the method of Guh et l (1998). Apprent viscosity nd temperture profiles were recorded nd monitored with PC. Curves were nlyzed for IPV. Three mesurements were mde on ech smple nd IPV ws reported in rpid visco units (RVU). Sttisticl Anlysis Multiple regression models (SPSS/PC v. 6) were used to nlyze dt. All possile procedures for vrile reduction were used to determine the predictors for regression models (Neter et l 1990). Criteri for selection of model were 1) the numer of vriles should e close to the numer of prmeters in the model, 2) R 2 should e close to 1, nd 3) SE should e low. TABLE I Neurl Network (NN) Models for Ech Product Property for Rice Flour Extrusion, Output ER WAI WSI DG IPV R SE Network SE, stndrd error; ER, expnsion rtio; WAI, wter sorption index; WSI, wter soluility index; DG, degree of geltiniztion; IPV, initil pek viscosity. LR, lerning rte = 0.3; MO, momentum = 0.2; IW, initil weight = 0.3. TABLE II Neurl Network (NN) Model for Prediction of All Five Product Properties (MIMO model) for Rice Flour Extrusion, Output ER WAI WSI DG IPV R SE Network MIMO, multiple input nd multiple output; SE, stndrd error; ER, expnsion rtio; WAI, wter sorption index; WSI, wter soluility index; DG, degree of geltiniztion; IPV, initil pek viscosity. LR, lerning rte = 0.3; MO, momentum = 0.2; IW, initil weight = CEREAL CHEMISTRY

4 Neurl Network Modeling NN modeling ws performed using commercil softwre (Neuro- Shell 2, Wrd Systems Group, Frederick, MD). A typicl single lyered neurl network is shown in Fig. 1. The criteri used for the NN model evlution were the R 2 nd stndrd error (SE). Dt sets were divided rndomly, 70% s trining nd 30% s testing sets. Three-lyered feedforwrd networks were used with ckpropgtion lgorithm. The networks were trined rigorously vrying the numer of neurons in the hidden lyer, lerning rtes, momentum, nd initil weights to rrive t optimum vlues of ll the prmeters when the error ws lowest. The est networks were sved. RESULTS AND DISCUSSION NN models were developed to predict individul product properties from the three input prmeters. The models developed for individul product properties for oth rice flour nd rice strch re shown in Tles I nd IV. The corresponding regression models re shown in Tle VII. The NN models hd lmost the sme rchitecture, with the only vrition eing in the numer of neurons in the hidden lyer. All the models performed well with the testing dt with R 2 vlues of greter thn 0.91 nd mjority of them greter thn The regression models lso performed well, with R 2 vlues greter thn The vriles nd their comintions were very different for the regression models. NN models hd higher R 2 vlues nd lower stndrd error vlues of prediction thn did the regression models. MIMO models were developed for the prediction of ll product properties t one time from the input prmeters. The NN models for rice flour nd rice strch re shown in Tles II through V. Agin it ws found tht the rchitecture of the models were very similr except for the numer of neurons in the hidden lyer. Another set of MIMO models ws developed to predict three of the product properties (ER, WAI, nd WSI, s these three properties would e sufficient to explin the product chrcteristics) from the input prmeters. Interestingly, the models developed for oth rice flour nd rice strch were similr (Tles III nd VI, respectively). So, it ws possile to develop generl model for prediction of ER, WAI, nd WSI for oth rice flour nd rice strch. It is not possile to hndle the MIMO systems y regression. Thus, NN proved to e more powerful in modeling the extrusion of rice flour nd strch. CONCLUSIONS Neurl networks models, developed to predict expnsion rtios, wter sorption index, nd wter soluility index individully from moisture content, screw speed, nd rrel temperture were etter thn regression models. The NN models developed for different rw mterils were very much similr in their prediction Fig. 1. Typicl single-lyered neurl network (Gnjyl nd Hnn 2001). TABLE III Neurl Network (NN) Model for Prediction of Three Product Properties (MIMO model) for Rice Flour Extrusion, Output ER WAI WSI R SE Network MIMO, multiple input nd multiple output; SE, stndrd error; ER, expnsion rtio; WAI, wter sorption index; WSI, wter soluility index. LR, lerning rte = 0.3; MO, momentum = 0.2; IW, initil weight = 0.3. TABLE VI Neurl Network (NN) Model for Prediction of Three Product Properties (MIMO model) for Rice Strch Extrusion, Output ER WAI WSI R SE Network MIMO, multiple input nd multiple output; SE, stndrd error; ER, expnsion rtio; WAI, wter sorption index; WSI, wter soluility index. LR, lerning rte = 0.3; MO, momentum = 0.2; IW, initil weight = 0.3. TABLE IV Neurl Network (NN) Models for Ech Product Property for Rice Strch Extrusion, Output ER DG WAI WSI IPV R SE Network SE, stndrd error; ER, expnsion rtio; DG, degree of geltiniztion; WAI, wter sorption index; WSI, wter soluility index; IPV, initil pek viscosity. LR, lerning rte = 0.3; MO, momentum = 0.2; IW, initil weight = 0.3. TABLE V Neurl Network (NN) Model for Prediction of All Five Product Properties (MIMO model) for Rice Strch Extrusion, Output ER DG WAI WSI IPV R SE Network MIMO, multiple input nd multiple output; SE, stndrd error; ER, expnsion rtio; DG, degree of geltiniztion; WAI, wter sorption index; WSI, wter soluility index; IPV, initil pek viscosity. LR, lerning rte = 0.3; MO, momentum = 0.2; IW, initil weight = 0.3. Vol. 83, No. 3,

5 TABLE VII Regression Models for Product Properties Using Feed Moisture Content (M), Screw Speed (S) nd Brrel Temperture (T) s Independent Vriles Property Regression Models R 2 SE Rice flour ER * M * T * M WAI * M * ST * MS * MT WSI * M * MS * MT * T DG * M * M S * T * MST * MT * T * T IPV * M * ST * MT Rice strch ER * M * T WAI * M * MST * M * MS ST WSI * M * MT * T * MS DG * M * M * S * S * MS * MT T * T IPV * M * ST * M ER, expnsion rtio; WAI, wter sorption index; WSI, wter soluility index; DG, degree of geltiniztion; IPV, initil pek viscosity. cpilities nd with respect to the rchitecture of the networks (numer of hidden lyers, numer of neurons in the hidden lyers, ctivtion functions used for the hidden nd output lyers, nd the lerning method used). Further, efficient multiple input multiple output (MIMO) models were developed. These MIMO models were very much similr for oth rice flour nd rice strch. MIMO models predicted the product properties of expnsion rtio, wter soluility index, nd wter sorption index for oth rice flour nd rice strch exctly the sme. These nlyses confirmed the cpilities of NN to model the extruded product properties efficiently. LITERATURE CITED Anderson, R. A., Conwy, H. F., Pfeifer, W. F., nd Griffin, E. L Geltiniztion of corn grits y roll nd extrusion cooking. Cerel Sci. Tody 14:4. Antil, J., Seiler, K., Seiel, W., nd Linko, P Production of flt red y extrusion cooking using different whet/rye rtios, protein enrichment nd grin with poor king ility. J. Food Eng. 2: AOAC Officil Methods of Anlysis. 13th Ed. Assocition of Officil Anlyticl Chemists: Wshington, DC. Btchelor, W. D., Yng, X. B., nd Tschnz, A. T Development of neurl network for soyen rust epidemics. Trns. ASAE 40: Bhttchry, M., nd Hnn, M. A Mthemticl modeling of food extruder. Leensm. Wiss. Technol. 19: Bhttchry, M., nd Hnn, M. A Texturl properties of extrusion cooked corn strch. Leensm. Wiss. 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6 Owusu-Ansh, J., vn de Voort, F. R., nd Stnley, D. W Texturl nd microstructurl chnges in corn strch s function of extrusion vriles. Cn. Inst. Food Sci. Technol. J. 17: Roerts, S. A., nd Guy, R. C. E Instilities in n extrusion cooker: A simple model. J. Food Eng. 5:7-30. Roerts, S. A., nd Guy, R. C. E Metstle sttes in food extrusion cooker. J. Food Eng. 6: Trnto, M. V., Meinke, W. W., Cter, C. M., nd Mttil, K. F Prmeters ffecting production nd chrcter of extrusion texturized deftted glndless cottonseed mel. J. Food Science 40: Tye, J., Vergnes, B., nd Dell Vlle, G A sic model for twinscrew extruder. J. Food Sci. 53: Vllejo-Cordo, B., Arteg, G. E., nd Nki, S Predicting milk shelf-life sed on rtificil neurl networks nd hedspce gs chromtogrphic dt. J. Food Sci. 60: Ycu, W. A Modeling twin screw co-rotting extruder. J. Food Eng. 8:1-21. [Received Septemer 22, Accepted Ferury 5, 2006.] Vol. 83, No. 3,