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Licensed Unlicensed Requires Authentication Published by De Gruyter October 8, 2014

Modeling of Furfural and 5-Hydroxymethylfurfural Content of Fermented Lotus Root: Artificial Neural Networks and a Genetic Algorithm Approach

Libin Xu , Ning Xu , Xia Zhu , Yupeng Zhu , Yong Hu , Dongsheng Li and Chao Wang EMAIL logo

Abstract

The aim of this study was to investigate the effect of different pretreatment and reducing sugar content on furfural (F) and 5-hydroxymethylfurfural (HMF) contents of fermented lotus root by vinegar. The lotus root samples were fermented using vinegar for 15 days, at different solution concentrations and temperatures. The processing conditions were considered as inputs of neural network to predict the F and HMF contents of lotus root. Genetic algorithm was applied to optimize the structure and learning parameters of ANN. The developed genetic algorithm-artificial neural network (GA-ANN) which included 23 and 17 neurons in the first and second hidden layers, respectively, gives the lowest mean squared error (MSE). The correlation coefficient of ANN was compared with multiple linear regression-based models. The GA-ANN model was found to be a more accurate prediction method for the F and HMF contents of fermented lotus root than linear regression-based models. In addition, sensitivity analysis and Pearson’s correlation coefficient were also analyzed to find out the relation between input and output variables.

Acknowledgment

This study was financially supported by the National Key Technology R&D Program (2012BAD27B03).

References

1. LiLJ, ZhangXD, PanEC, SunL, XieK, GuL, et al. Relationship of starch synthesis with its related enzymes’ activities during rhizome development. SCI Agric Sin2006;39:230712.Search in Google Scholar

2. AmesJM. The Maillard reaction. In: HudsonBJ, editor. Biochemistry of food proteins. London: Elsevier, 1992:99153.Search in Google Scholar

3. AntonelliA, ChinniciF, MasinoF. Heat-induced chemical modification of grape must as related to its concentration during the production of traditional balsamic vinegar: a preliminary approach. Food Chem2004;88:638.10.1016/j.foodchem.2004.01.023Search in Google Scholar

4. BozkurtH, GogusF, ErenS. Nonenzymic browning reactions in boiled grape juice and its models during storage. Food Chem1999;64:8993.10.1016/S0308-8146(98)00081-8Search in Google Scholar

5. CocchiM, FerrariG, ManziniD, MarchettiA, SighinolfiS. Study of the monosaccharide’s and furfurals evolution during the preparation of cooked grape musts for aceto Balsamico Tradizionale production. J Food Eng2007;79:143844.10.1016/j.jfoodeng.2006.01.091Search in Google Scholar

6. FerrerE, AlgeriaA, FarrèR, AbellanP, RomeroF. High-performance liquid chromatographic determination of furfural compounds in infant formula: changes during heat treatment and storage. J Chromatogr A2002;947:85–95.10.1016/S0021-9673(01)01593-XSearch in Google Scholar

7. Lo CocoF, ValentiniC, NovelliV, CecconI. High-performance liquid chromatographic determination of 2-furaldehyde and 5-hydroxymethyl-2- formaldehyde in honey. J Chromatogr A1996;749:85102.10.1016/0021-9673(96)00392-5Search in Google Scholar

8. MurkovicM, BornikMA. Formation of 5-hydroxymethyl-2-furfural (HMF) and 5-hydroxymethyl-2-furoic acid during roasting of coffee. Mol Nutr Food Res2007;51:3904.10.1002/mnfr.200600251Search in Google Scholar PubMed

9. ChenL, HuangHH, LiuW, PengN, HuangXS. Kinetics of the 5-hydroxymethylfurfural formation reaction in Chinese rice wine. J Agric Food Chem2010;58:350711.10.1021/jf904094qSearch in Google Scholar PubMed

10. FallicoB, ZappalàM, ArenaE, VerzeraA. Effects of conditioning on HMF content in unifloral honeys. Food Chem2004;85:30513.10.1016/j.foodchem.2003.07.010Search in Google Scholar

11. AmeurLA, MathieuO, LalanneV, TrystramG, Birlouez-AragonI. Comparison of the effects of sucrose and hexose on furfural formation and browning in cookies baked at different temperatures. Food Chem2007;101:140716.10.1016/j.foodchem.2006.03.049Search in Google Scholar

12. AmeurLA, TrystramG, Birlouez-AragonI. Accumulation of 5-hydroxymethyl-2-furfural in cookies during the baking process: validation of an extraction method. Food Chem2006;98:7906.10.1016/j.foodchem.2005.07.038Search in Google Scholar

13. YouSJ, ParkN, ParkED, ParkM-J. Partial least squares modeling and analysis of furfural production form biomass-derived xylose over solid acid catalysts. J Ind Eng Chem2014;in press.10.1016/j.jiec.2014.02.044Search in Google Scholar

14. ZhangY-Y, SongY, HuX-S, LiaoX-J, NiY-Y, LiQ-H. Effects of sugar in batter formula and baking conditions on 5-hydroxymethylfurfural and furfural formation in sponge cake models. Food Res Int2012;49:43945.10.1016/j.foodres.2012.07.012Search in Google Scholar

15. HertzJ, KroghA, PalmerRG. Introduction to the theory of neural computation. Redwood City, CA: Addison-Wesley, 1991.Search in Google Scholar

16. DehghaniAA, Beig MohammadiZ, MaghsoudlouY, Sadeghi MahoonakA. Intelligent estimation of the canola oil stability using artificial neural networks. Food Bioprocess Technol2010. DOI:10.1007/s11947-009-0314-8Search in Google Scholar

17. BardotI, MartinN, TrystramG, HossenloppJ, RogeauxM, BochereauL. A new approach for formulation of beverages. Part II: interactive automatic method. Lebensm Wiss Technol1994;27:51321.10.1006/fstl.1994.1103Search in Google Scholar

18. DornierM, DeclouxM, TrystramG, LebertA. Dynamic modeling of crossflow microfiltration using neural networks. J Membr Sci1995;98:26373.10.1016/0376-7388(94)00195-5Search in Google Scholar

19. LertworasirikulS, SaetanS. Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. J Food Eng2010;90:21433.10.1016/j.jfoodeng.2009.12.030Search in Google Scholar

20. LlaveY, HagiwaraT, SakiyamaT. Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods. J Food Eng2012;109:55360.10.1016/j.jfoodeng.2011.10.024Search in Google Scholar

21. GoniSM, OddoneS, SeguraJA, MascheroniRH, SalvadoriVO. Prediction of food freezing and thawing time: artificial neural networks and genetic algorithm approach. J Food Eng2008;84:16478.10.1016/j.jfoodeng.2007.05.006Search in Google Scholar

22. KimGH, YoonJE, AnSH, ChoHH, KangaKI. Neural network model incorporating a genetic algorithm in estimating construction costs. Build Environ2004;39:133340.10.1016/j.buildenv.2004.03.009Search in Google Scholar

23. SaemiM, AhmadiM, VarjaniAY. Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng2007;59:97105.10.1016/j.petrol.2007.03.007Search in Google Scholar

24. MohebbiA, TaheriM, SoltaniA. A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants. Int J Refrigeration Revue Int Du Froid2008;31:131727.10.1016/j.ijrefrig.2008.04.008Search in Google Scholar

25. ChenHC, WangMN. Application of multiple linear regression design spectrophotometry to simultaneous determination of lemon yellow and sunset yellow. Instrum Anal Monit2001;4:2931.Search in Google Scholar

26. RumelhartDE, HintonGE, WilliamsRJ. Learning representations by back-propagating errors. Nature1986;323:5336.10.1038/323533a0Search in Google Scholar

27. MorimotoT. Genetic algorithm. In: SablaniSS, RahmanMS, DattaAK, MujumdarAS, editors. Food and bioprocess modeling techniques. New York: CRC, 2006.Search in Google Scholar

28. ShankarTJ, SokhansanjS, BandyopadhyayS, BawaAS. A case study on optimization of biomass flow during single-screw extrusion cooking using genetic algorithm (GA) and response surface method (RSM). Food Bioprocess Technol2010;3:498510.10.1007/s11947-008-0172-9Search in Google Scholar

29. KapoorV, DeyS, KhuranaAP. Empirical analysis and random respectful recombination of crossover and mutation in genetic algorithms. Int J Comput Appl2010;1:2530.10.5120/1530-133Search in Google Scholar

30. MajdiA, BeikiM. Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mechanics Mining Sci2010;47:24653.10.1016/j.ijrmms.2009.09.011Search in Google Scholar

31. BelitzHD, GroschW, SchieberleP. Carbohydrates. In: Food chemistry. Berlin, Heidelberg: Springer, 2009;4:248339.Search in Google Scholar

32. KrohLW. Caramelisation in food and beverages. Food Chem1994;51:3739.10.1016/0308-8146(94)90188-0Search in Google Scholar

33. KwakE-J, LimS-I. The effect of sugar, amino acid, metal ion, and NaCl on model Maillard reaction under pH control amino acids. Food Chem2004;27:8590.10.1007/s00726-004-0067-7Search in Google Scholar PubMed

Published Online: 2014-10-8
Published in Print: 2014-12-1

©2014 by De Gruyter

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