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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.typeArtigo de periódicopt_BR
dc.titleNeural Networks To Formulate Special Fatspt_BR
dc.contributor.authorGarcia R.K.pt_BR
dc.contributor.authorMoreira Gandra K.pt_BR
dc.contributor.authorBlock J.M.pt_BR
dc.contributor.authorBarrera-Arellanoa D.pt_BR
unicamp.authorGarcia, R.K., Laboratório de Óleos e Gorduras/FEA/UNICAMP, Caixa Postal 6091, Campinas, São Paulo, 13081-970, Brazilpt_BR
unicamp.authorMoreira Gandra, K., Laboratório de Óleos e Gorduras/FEA/UNICAMP, Caixa Postal 6091, Campinas, São Paulo, 13081-970, Brazilpt_BR
unicamp.authorBarrera-Arellanoa, D., Laboratório de Óleos e Gorduras/FEA/UNICAMP, Caixa Postal 6091, Campinas, São Paulo, 13081-970, Brazilpt_BR
unicamp.author.externalBlock, J.M., Departamento de Ciência e Tecnologia de Alimentos, CCA/UFSC, Rod. Admar Gonzaga, 1346, Itacorubi. CEP 88034-001 Florianópolis-SC, Brazilpt
dc.description.abstractNeural networks are a branch of artificial intelligence based on the structure and development of biological systems, having as its main characteristic the ability to learn and generalize knowledge. They are used for solving complex problems for which traditional computing systems have a low efficiency. To date, applications have been proposed for different sectors and activities. In the area of fats and oils, the use of neural networks has focused mainly on two issues: the detection of adulteration and the development of fatty products. The formulation of fats for specific uses is the classic case of a complex problem where an expert or group of experts defines the proportions of each base, which, when mixed, provide the specifications for the desired product. Some conventional computer systems are currently available to assist the experts; however, these systems have some shortcomings. This article describes in detail a system for formulating fatty products, shortenings or special fats, from three or more components by using neural networks (MIX). All stages of development, including design, construction, training, evaluation, and operation of the network will be outlined.en
dc.relation.ispartofGrasas y Aceitespt_BR
dc.date.issued2012pt_BR
dc.identifier.citationGrasas Y Aceites. , v. 63, n. 3, p. 245 - 252, 2012.pt_BR
dc.language.isoenpt_BR
dc.description.volume63pt_BR
dc.description.issuenumber3pt_BR
dc.description.firstpage245pt_BR
dc.description.lastpage252pt_BR
dc.rightsabertopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn173495pt_BR
dc.identifier.doi10.3989/gya.119011pt_BR
dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84863914986&partnerID=40&md5=c846b730b8068c0e89eb1aa689bd79b5pt_BR
dc.date.available2015-06-26T20:29:17Z
dc.date.available2015-11-26T14:25:48Z-
dc.date.accessioned2015-06-26T20:29:17Z
dc.date.accessioned2015-11-26T14:25:48Z-
dc.description.provenanceMade available in DSpace on 2015-06-26T20:29:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2012en
dc.description.provenanceMade available in DSpace on 2015-11-26T14:25:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2012en
dc.identifier.urihttp://www.repositorio.unicamp.br/handle/REPOSIP/96968
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/96968-
dc.identifier.idScopus2-s2.0-84863914986pt_BR
dc.description.referenceAnklam, E., Bassani, M.R., Eiberger, T., Kriebel, S., Lipp, M., Matissek, R., Characterization of cocoa butters and other vegetable fats by pyrolysis-mass spectrometry (1997) Fresenius' Journal of Analytical Chemistry, 357 (7), pp. 981-984pt_BR
dc.description.referenceArteaga, G.E., Nakaii, S., Predicting protein functionality with artificial neural networks: Foaming and emulsifying properties (1993) J. Food Sci., 58, pp. 1152-1156pt_BR
dc.description.referenceBarrera-Arellano, D., Akamine, E.A., Rka, G., Gandra, K.M., Zero trans fats formulation through a neural network operated with five components: Two soybean-soybean interesterified fats, palm, soybean and palm kernel oils (2011) 9th Euro Fed Lipid Congress, , Rotterdam, The Netherlands, Abstract. Barrera-Arellano D, Block JM, Grimaldi R, Figueiredopt_BR
dc.description.referenceGomide Fac, M.F., Almeida, R.R., (2005) Programa MIX, Software for the Formulation of Fats with Neural Networks, , Register INPI 98003155, Campinas, Brazilpt_BR
dc.description.referenceBarbosa, A.H., Freitas, M.S.R., Neves, F.A., Confiabilidade estrutural utilizando o método de Monte Carlo e redes neurais (2005) Rev. Esc. de Minas, 58, pp. 247-255pt_BR
dc.description.referenceBarile, D., Coisson, J.D., Arlorio, M., Rinaldi, M., Identification of production area of Ossolano Italian cheese with chemometric complex approach (2006) Food Control, 17 (3), pp. 197-206. , DOI 10.1016/j.foodcont.2004.10.016, PII S0956713504002336pt_BR
dc.description.referenceBerg, E.P., Engel, B.A., Forrest, J.C., Pork Carcass Composition Derived from a Neural Network Model of Electromagnetic Scans (1998) Journal of Animal Science, 76 (1), pp. 18-22pt_BR
dc.description.referenceBlock, J.M., Barrera-Arellano, D., Figueiredo, M.F., Gomide, F.A.C., Blending process optimization into special fat formulation by neural networks (1997) JAOCS, Journal of the American Oil Chemists' Society, 74 (12), pp. 1537-1541pt_BR
dc.description.referenceBlock, J.M., Barrera-Arellano, D., Figueiredo, M.F., Gomide, F.C., Sauer, L., Formulation of special fats by neural networks: A statistical approach (1999) JAOCS, Journal of the American Oil Chemists' Society, 76 (11), pp. 1357-1361pt_BR
dc.description.referenceBlock, J.M., Barrera-Arellano, D., Almeida, R., Gomide, F.C., Moretti, R.B., Formulation of fats using neural networks: Commercial products and pilot-plant production (2003) Grasas y Aceites, 54 (3), pp. 240-244pt_BR
dc.description.referenceBorggaard, C., Madsen, N.T., Thodberg, H.H., In-line image analysis in the slaughter industry, illustrated by Beef Carcass Classification (1996) Meat Sci., 43, pp. 151-163pt_BR
dc.description.referenceLal, B., Importancia de la instrumentación y computarización en programas de calidad total (1994) Aceites y Grasas, 15, pp. 99-102pt_BR
dc.description.referenceCarrapiso, A.I., Ventanas, J., Jurado, A., Garcia, C., An electronic nose to classify Iberian pig fats with different fatty acid composition (2001) JAOCS, Journal of the American Oil Chemists' Society, 78 (4), pp. 415-418pt_BR
dc.description.referenceCebula, D.J., Smith, K.W., Differential scanning calorimetry of confectionery fats. Pure triglycerides: Effects of cooling and heating rate variation (1991) J. Am. Oil Chem. Soc., 68, pp. 591-595pt_BR
dc.description.referenceDe Cerqueira, E.O., De Andrade, J.C., Poppi, R.J., Mello, C., Neural networks and its applications in multivariate calibration (2001) Quimica Nova, 24 (6), pp. 864-873pt_BR
dc.description.referenceChiu, M.C., Gioielli, L.A., Grimaldi, R., Lipídios estruturados obtidos a partir da mistura de gordura de frango, sua estearina e triacilgliceróis de cadeia média, II-pontos de amolecimento e fusão (2008) Quim. Nova, 31, pp. 238-243pt_BR
dc.description.referenceCimpoiu, C., Cristea, V., Hosu, A., Sandru, M., Seserman, L., Antioxidant activity prediction and classification of some teas using artificial neural networks (2011) Food Chem., 127, pp. 1323-1328pt_BR
dc.description.referenceCheroutre-Vialette, M., Lebert, A., Application of recurrent neural network to predict bacterial growth in dynamic conditions (2002) International Journal of Food Microbiology, 73 (2-3), pp. 107-118. , DOI 10.1016/S0168-1605(01)00642-0, PII S0168160501006420pt_BR
dc.description.referenceDa Cruz, A.G., Walter, E.H.M., Cadena, R.S., Faria, J.A.F., Bolini, H.M.A., Franttini-Fileti, A.M., Monitoring the authenticity of low-fat yogurts by an artificial neural network J. Dairy Sci., 92, pp. 4797-4804pt_BR
dc.description.referenceDaniels, R.L., Hyun, J.K., Min, D.B., Hydrogenation and interesterification effects on the oxidative stability and melting point of soybean oil (2006) Journal of Agricultural and Food Chemistry, 54 (16), pp. 6011-6015. , DOI 10.1021/jf053263rpt_BR
dc.description.referenceEyng, E., Fileti, A.M.F., Control of absorption columns in the bioethanol process: Influence of measurement uncertainties (2010) Eng. Appl. Artif. Intel., 23, pp. 271-282pt_BR
dc.description.referenceErickson, D.R., Erickson, M.D., Hydrogenation and base stock formulation procedures (1995) Pratical Handbook of Soybean Processing and Utilization, pp. 218-238. , en Erickson DR (ed.). AOCS Press, Champaignpt_BR
dc.description.referenceGallegos-Infante, J.A., Rocha-Guzman, N.E., Gonzalez-Laredo, R.F., Rico-Martinez, R., The kinetics of crystallization of tripalmitin in olive oil: An artificial neural network approach (2002) Journal of Food Lipids, 9 (1), pp. 73-86pt_BR
dc.description.referenceGandra, K.M., (2011) Formulação de Gorduras Zero Trans Para Recheio de Biscoitos Utilizando Redes Neurais, , University of Campinas (UNICAMP). Campinas, Brazilpt_BR
dc.description.referenceGandra, K.M., Rka, G., Block, J.M., Barrera-Arellano, D., Construction and training of a neural network for the formulation of specialty fats using interesterified fats (2009) World Congress on Oils and Fats & 28th ISF Congress. Oils and Fats Essential for Life-Program & Abstract Book Sydney, pp. 117-118pt_BR
dc.description.referenceGarcia, R.K.A., Formulação de Gorduras Para Aplicação em Margarinas Zero Trans Com Redes Neurais A Partir de Gorduras Interesterificadas (2010) University of Campinas (UNICAMP). Campinas, Brazilpt_BR
dc.description.referenceGarcía, R.K.A., Gandra, K.M., Barrera-Arellano, D., Formulação de blends para aplicação em margarinas zero trans por redes neurais baseado no SFC e ponto de fusão de gorduras comerciais (2010) V Simpósio Internacional Tendências e Inovaçoes em Tecnologia de Óleos e Gorduras, , Campinas, Brazilpt_BR
dc.description.referenceGlassey, J., Montague, G.A., Ward, A.G., Kara, B.V., Artificial neural network based experimental design procdures for enhancing fermentation development (1994) Biotechnology and Bioengineering, 44 (4), pp. 397-405. , DOI 10.1002/bit.260440402pt_BR
dc.description.referenceGomes, H.M., Awruch, A.M., Comparison of response surface and neural network with other methods for structural reliability analysis (2004) Structural Safety, 26 (1), pp. 49-67. , DOI 10.1016/S0167-4730(03)00022-5pt_BR
dc.description.referenceGoodacre, R., Kell, D.B., Bianchi, G., Rapid assessment of the adulteration of virgin olive oils by other seed oils using pyrolysis mass-spectrometry and artificial neural networks (1993) J. Sci. Food Agr., 63, pp. 297-307pt_BR
dc.description.referenceGoshawk, J.A., Binding, D.M., Kell, D.B., Goodacre, R., Rheological phenomena occurring during the shearing flow of mayonnaise (1998) Journal of Rheology, 42 (6), pp. 1537-1553pt_BR
dc.description.referenceHaykin, S., (1999) Neural Networks: A Comprehensive Foundation, , Prentice Hall Englewood Cliffs, NJpt_BR
dc.description.referenceHaugen, J.E., Electronic noses in food analysis (2001) Headspace Analysis of Foods and Flavors: Theory and Practice (Advancesin Experimental Medicine and Biology), 488, pp. 43-57. , Rouseff L.R., Cadwallader K.R. (ed.). Kluwer Academic Plenum Publishers, New Yorkpt_BR
dc.description.referenceHuang, Y., Whittaker, A.D., Lacey, R.E., Neural network prediction modeling for a continuous, snack food frying process (1998) Transactions of the American Society of Agricultural Engineers, 41 (5), pp. 1511-1517pt_BR
dc.description.referenceHumphrey, K.L., Narine, S.S., A comparison of lipid shortening functionality as a function of molecular ensemble and shear: Crystallization and melting (2004) Food Research International, 37 (1), pp. 11-27. , DOI 10.1016/j.foodres.2003.09.012pt_BR
dc.description.referenceHush, D.R., Horne, B.G., Progress in supervised neural networks (1993) IEEE Signal Proc. Mag., 10, pp. 8-39pt_BR
dc.description.referenceIndahl, U.G., Sahni, N.S., Kirkhus, B., Naes, T., Multivariate strategies for classification based on NIR-spectra-with application to mayonnaise (1999) Chemometrics and Intelligent Laboratory Systems, 49 (1), pp. 19-31. , DOI 10.1016/S0169-7439(99)00023-4, PII S0169743999000234pt_BR
dc.description.referenceJayas, D.S., Paliwal, J., Visen, N.S., Multi-layer neural networks for image analysis of agricultural products (2000) J. Agr. Eng. Res., 77, pp. 119-128pt_BR
dc.description.referenceJansson, P.A., Neural networks: An overview (1991) Anal. Chem., 63, pp. 357-362pt_BR
dc.description.referenceKatz, W.T., Snell, J.W., Merickel, M.B., Artificial neural networks (1992) Method Enzymol., 210, pp. 610-636pt_BR
dc.description.referenceKhataee, A.R., Kasiri, M.B., Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis (2010) J. Mol. Catal. A: Chem., 331, pp. 86-100pt_BR
dc.description.referenceKruzlicova, D., Mocak, J., Balla, B., Petka, J., Farkova, M., Havel, J., Classification of Slovak white wines using artificial neural networks and discriminant techniques (2009) Food Chem., 112, pp. 1046-1052pt_BR
dc.description.referenceLange, J., Wittmann, C., Enzyme sensor array for the determination of biogenic amines in food samples (2002) Analytical and Bioanalytical Chemistry, 372 (2), pp. 276-283. , DOI 10.1007/s00216-001-1130-9pt_BR
dc.description.referenceLefebvre, J., Finished product formulation (1983) J. Am. Oil Chem., 60, pp. 295-300pt_BR
dc.description.referenceLemes, M.R., Junior, A.D.P., A tabela periódica dos elementos químicos prevista por redes neurais artificiais de kohonen (2008) Quim. Nova, 31, pp. 1141-1144pt_BR
dc.description.referenceLichan, E., Developments in the detection of adulteration of olive oil (1994) Trends Food Sci. Tech., 5, pp. 3-11pt_BR
dc.description.referenceLipp, M., Determination of the adulteration of butter fat by its triglyceride composition obtained by GC. A comparison of the suitability of PLS and neural networks (1996) Food Chemistry, 55 (4), pp. 389-395. , DOI 10.1016/0308-8146(95)00162-Xpt_BR
dc.description.referenceLiu, X.Q., Tan, J.L., Acoustic wave analysis for food crispness evaluation (1999) J. Text. Stud., 30, pp. 397-408pt_BR
dc.description.referenceLou, W., Nakai, S., Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products (2001) Journal of Agricultural and Food Chemistry, 49 (4), pp. 1799-1804. , DOI 10.1021/jf000650mpt_BR
dc.description.referenceMarini, F., Artificial neural networks in foodstuff analyses: Trends and perspectives-A review (2009) Anal. Chim. Acta, 635, pp. 121-131pt_BR
dc.description.referenceMárquez, A.J., Herrera, M.P.A., Ojeda, U.M., Maza, G.B., Neural network as tool for virgin olive oil elaboration process optimization (2009) J. Food Eng., 95, pp. 135-141pt_BR
dc.description.referenceMartin, Y.G., Pavon, J.L.P., Cordero, B.M., Classification of vegetable oils by linear discriminant analysis of Electronic Nose data (1999) Anal. Chim. Acta, 384, pp. 83-94pt_BR
dc.description.referenceMattioni, B., (2010) Aplicação de Redes Neurais Na Formulação de Gorduras Para Massa Folhada Baseada em Gorduras Interesterificadas de Soja e Algodão, , University of Santa Catarina. Florianópolis, Brazilpt_BR
dc.description.referenceMcCulloch, W.S., Pitts, W., A logical calculus of the ideas immanent in nervous activity (1943) Bull Math Biophysics, 5, pp. 115-133pt_BR
dc.description.referenceMeisel, H., Lorenzen, P.Chr., Martin, D., Schlimme, E., Chemometric identification of butter types by analysis of compositional parameters with neural networks (1997) Nahrung - Food, 41 (2), pp. 75-80pt_BR
dc.description.referenceMutlu, A.C., Boyaci, I.H., Genis, H.E., Ozturk, R., Basaran-Akgul, N., Sanal, T., Evlice, A.K., Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks (2011) Eur. Food Res. Technol., 233, pp. 267-274pt_BR
dc.description.referenceNi, H.X., Gunasekaran, S., Food quality prediction with neural networks (1998) Food Technol., 52, pp. 60-65pt_BR
dc.description.referenceNitatori, C.Y., Gandra, K.M., Garcia, R.K.A., Barrera-Arellano, D., Construção e treinamento de uma rede neural para formulação de gorduras especiais a partir de gorduras interesterificadas, óleos de soja e palma (2010) XVII Congresso de Iniciação Científica da Unicamp, , Campinas, Brazilpt_BR
dc.description.referenceO'brien, R.D., (2004) Fats and Oils -Formulating and Processing for Applications, , CRC Press, New Yorkpt_BR
dc.description.referencePoulton, M.M., Neural networks as an intelligence amplification tool: A review of applications (2002) Geophysics, 67 (3), pp. 979-993pt_BR
dc.description.referencePrzybylski, R., Zambiazi, R., Predicting oxidative stability of vegetable oils using neural network system and endogenous oil components (2000) J. Am. Oil Chem. Soc., 77, pp. 925-931pt_BR
dc.description.referenceRamli, N., Said, M., Loon, N.T., Physicochemical characteristics of binary mixtures of hydrogenated palm kernel oil and goat milk fat (2005) Journal of Food Lipids, 12 (3), pp. 243-260. , DOI 10.1111/j.1745-4522.2005.00021.xpt_BR
dc.description.referenceRibeiro, A.P.B., Masuchi, M.H., Grimaldi, R., Gonçalves, L.A.G., Interesterificação química de óleo de soja e óleo de soja totalmente hidrogenado: influência do tempo de reação (2009) Quim Nova, 32, pp. 939-945pt_BR
dc.description.referenceRomero, R.A.F., Lanças, F.M., Guizo, S.J., Berton, S.R., Classification of edible oils using neural networks (1991) Proceedings/Anais of International Meeting on Fats and Oils Technology-Symposium and Exhibition, pp. 9-11. , Campinas, Brazilpt_BR
dc.description.referenceRumelhart, D.E., Hinton, G.E., Williams, R.J., Learning internal representations by error propagation (1986) Parallel Distribuited Processing: Explorations in the Microstruture of Cognition, pp. 318-362. , Rumelhart DE, McClelland JL (ed) MIT Press, Cambridgept_BR
dc.description.referenceScaranto, B.A.A., (2010) Aplicação de Redes Neurais Na Formulação de Gorduras Para Bolo Baseada em Gorduras Interesterificadas de Soja e Algodão, , University of Santa Catarina, Florianópolis, Brazilpt_BR
dc.description.referenceSmallwood, N.J., Using computers for oil blending (1989) J. Am. Oil Chem. Soc., 66, pp. 644-648pt_BR
dc.description.referenceSofu, A., Ekinci, F.Y., Estimation of storage time of yogurt with artificial neural network modeling (2007) Journal of Dairy Science, 90 (7), pp. 3118-3125. , DOI 10.3168/jds.2006-591pt_BR
dc.description.referenceSorsa Timo, Koivo Heikki, N., Koivisto Hannu, Neural networks in process fault diagnosis (1991) IEEE Transactions on Systems, Man and Cybernetics, 21 (4), pp. 815-825. , DOI 10.1109/21.108299pt_BR
dc.description.referenceSousa, E.A., Teixeira, L.C.V., Mello, M.R.P.A., Torres, E.A.F.S., Neto, J.M.M., Aplicação de redes neurais para avaliação do teor de carne mecanicamente separada em salsicha de frango (2003) Ciênc. e Tecnol. de Alim., 23, pp. 307-311pt_BR
dc.description.referenceStauffer, C.E., Uso de las grasas y los aceites en productos de panadería y confíteria (2006) Grasas Aceites, 3, pp. 420-432pt_BR
dc.description.referenceTimms, R.E., Physical properties of oils and mixture (1985) J. Am. Oil Chem. Soc., 62, pp. 241-248pt_BR
dc.description.referenceWidrow, B., Lehr, M., 30 years of adaptive neural networks: Perceptron, madaline, and backpropagation (1990) Proceedings of IEEE, 78, pp. 1415-1442pt_BR
dc.description.referenceVafaei, M.T., Eslamloueyan, R., Ayatollahi, S.H., Simulation of steam distillation process using neural networks (2009) Chem. Eng. Res. Des., 87, pp. 997-1002pt_BR
dc.description.referenceVieira, W.G., Vml, S., Carvalho, F.R., Jafr, P., Amf, F., Identification and predictive control of a FCC unit using a MIMO neural model (2005) Chem. Eng. Process, 44, pp. 855-868pt_BR
dc.description.referenceYuan, J.Q., Vanrolleghem, P.A., Rolling learning-prediction of product formation in bioprocesses (1999) Journal of Biotechnology, 69 (1), pp. 47-62. , DOI 10.1016/S0168-1656(99)00002-4, PII S0168165699000024pt_BR
dc.description.referenceZafra, A., Automation and refining (1993) Inform, 4, p. 166pt_BR
dc.description.referenceZhang, Q., Reid, J.F., Litchfield, J.B., Ren, J., Chang, S.-W., A prototype neural network supervised control system for Bacillus thuringiensis fermentations (1994) Biotechnology and Bioengineering, 43 (6), pp. 483-489pt_BR
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