Abstract
Short-chain fructo-oligosaccharides (FOS) are considered as low-calorie carbohydrates with prebiotic function. They can be produced from sucrose by fructosyltransferase activity, resulting in a mixture of saccharides with different chain lengths. Current practice for carbohydrate analysis involves the use of time-costly and off-line chromatographic procedures. This study is dedicated to the development of an artificial neural network (ANN) model for predicting carbohydrate composition from the direct measurement of UV spectra. A total of 182 samples were generated by operating an enzyme membrane reactor (EMR) under both optimal and suboptimal settings. The concentration data determined by HPLC and corresponding absorbance readings were used to train a two-layer feedforward neural network. The optimized model was then validated by using new observations that were not involved in the training. The model explained 98, 97, and 88% of the variation in the composition of the new observations regarding the main components sucrose, kestose, and glucose with a mean squared error of prediction of 6.59, 3.40, and 2.81, respectively. The results indicate that the proposed UV-ANN method has a great potential to be used for the real-time monitoring of the bioconversion.
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References
Bali, V., Panesar, P.S., Bera, M.B., & Panesar, R. (2013). Fructo-oligosaccharides: production, purification and potential applications. Critical Reviews in Food Science and Nutrition, 8398 (2014), 37–41. https://doi.org/10.1080/10408398.2012.694084. http://www.ncbi.nlm.nih.gov/pubmed/24915337.
Barclay, T., Ginic-Markovic, M., Johnston, M.R., Cooper, P.D., & Petrovsky, N. (2012). Analysis of the hydrolysis of inulin using real time 1H NMR spectroscopy. Carbohydrate Research, 352, 117–125. https://doi.org/10.1016/j.carres.2012.03.001.
Borromei, C., Careri, M., Cavazza, A., Corradini, C., Elviri, L., Mangia, A., & Merusi, C. (2009). Evaluation of fructooligosaccharides and inulins as potentially health benefiting food ingredients by HPAEC-PED and MALDI-TOF MS. International Journal of Analytical Chemistry, 2009, 530,639. https://doi.org/10.1155/2009/530639.
Cámara, M., Torrecilla, J.S., Caceres, J.O., Cortes Sánchez Mata M., & Fernandez-Ruiz, V. (2010). Neural network analysis of spectroscopic data of lycopene and β-carotene content in food samples compared to HPLC-UV-Vis. Journal of Agricultural and Food Chemistry, 58(1), 72–75. https://doi.org/10.1021/jf902466x.
Chen, S.C., Sheu, D.C., & Duan, K.J. (2014). Production of fructooligosaccharides using β-fructofuranosidase immobilized onto chitosan-coated magnetic nanoparticles. Journal of the Taiwan Institute of Chemical Engineers, 45(4), 1105–1110. https://doi.org/10.1016/j.jtice.2013.10.003.
Corradini, C., Bianchi, F., Matteuzzi, D., Amoretti, A., Rossi, M., & Zanoni, S. (2004). High-performance anion-exchange chromatography coupled with pulsed amperometric detection and capillary zone electrophoresis with indirect ultra violet detection as powerful tools to evaluate prebiotic properties of fructooligosaccharides and inulin. Journal of Chromatography A, 1054(1-2), 165–173. https://doi.org/10.1016/j.chroma.2004.07.109.
Corradini, C., Lantano, C., & Cavazza, A. (2013). Innovative analytical tools to characterize prebiotic carbohydrates of functional food interest. Analytical and Bioanalytical Chemistry, 405(13), 4591–4605. https://doi.org/10.1007/s00216-013-6731-6.
Demuth, H., Beale, M., & Hagan, M. (2009). Neural Network toolbox TM 6 User’s Guide, Revised for Version 6.0.3 (Release 2009b).
Dias, L.G., Veloso, A.C.A., Correia, D.M., Rocha, O., Torres, D., Rocha, I., Rodrigues, L.R., & Peres, A.M. (2009). UV Spectrophotometry method for the monitoring of galacto-oligosaccharides production. Food Chemistry, 113(1), 246–252. https://doi.org/10.1016/j.foodchem.2008.06.072.
Dominguez, A.L., Rodrigues, L.R., Lima, N.M., & Teixeira, J.A. (2013). An overview of the recent developments on fructooligosaccharide production and applications. Food and Bioprocess Technology (pp. 1–14). https://doi.org/10.1007/s11947-013-1221-6.
Falguera, V., Aliguer, N., & Falguera, M. (2012). An integrated approach to current trends in food consumption: moving toward functional and organic products? Food Control, 26(2), 274–281. https://doi.org/10.1016/j.foodcont.2012.01.051.
Ghazi, I., Gómez De Segura, A, Fernández-arrojo, L., Alcalde, M., Yates, M., Rojas-Cervantes, ML., Plou, F.J., & Ballesteros, A. (2005). Immobilisation of fructosyltransferase from Aspergillus aculeatus on epoxy-activated Sepabeads EC for the synthesis of fructo-oligosaccharides. Journal of Molecular Catalysis B: Enzymatic, 35(1-3), 19–27. https://doi.org/10.1016/j.molcatb.2005.04.013.
Grube, M., Bekers, M., Upite, D., & Kaminska, E. (2002). Infrared spectra of some fructans. Spectroscopy, 16(3–4), 289–296. https://doi.org/10.1155/2002/637587. http://www.hindawi.com/journals/jspec/2002/637587/abs/.
Hagan, M., Demuth, H., Beale, M., & De Jesús, O. (2014). Neural network design. Martin Hagan.
Hicke, H.G., Becker, M., Paulke, B.R., & Ulbricht, M. (2006). Covalently coupled nanoparticles in capillary pores as enzyme carrier and as turbulence promoter to facilitate enzymatic polymerizations in flow-through enzyme-membrane reactors. Journal of Membrane Science, 282(1-2), 413–422. https://doi.org/10.1016/j.memsci.2006.05.051.
Huang, G.B. (2003). Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks, 14(2), 274–281. https://doi.org/10.1109/TNN.2003.809401.
Joye, D., & Hoebregs, H. (2000). Determination of oligofructose, a soluble dietary fiber, by high-temperature capillary gas chromatography. Journal of AOAC International, 83(4), 1020–1025.
Kovács, Z, Benjamins, E., Grau, K., Ur Rehman, A., Ebrahimi, M., & Czermak, P. (2014). Recent developments in manufacturing oligosaccharides with prebiotic functions (pp. 257–295). Berlin: Springer.
Li, J., Liu, X., Zhou, B., Zhao, J., & Li, S. (2013). Determination of fructooligosaccharides in burdock using HPLC and microwave-assisted extraction. Journal of Agricultural and Food Chemistry, 61(24), 5888–5892. https://doi.org/10.1021/jf400534n.
Lin, H., Zhao, J., Sun, L., Chen, Q., & Zhou, F. (2011). Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innovative Food Science and Emerging Technologies, 12 (2), 182–186. https://doi.org/10.1016/j.ifset.2011.01.008.
Lorenzoni, A.S.G., Aydos, L.F., Klein, M.P., Ayub, M.A.Z., Rodrigues, R.C., & Hertz, P.F. (2015). Continuous production of fructooligosaccharides and invert sugar by chitosan immobilized enzymes: comparison between in fluidized and packed bed reactors. Journal of Molecular Catalysis B: Enzymatic, 111, 51–55. https://doi.org/10.1016/j.molcatb.2014.11.002.
Mateo, F., Gadea, R., Mateo, E.M., & Jiménez, M. (2011). Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum. Food Control, 22(1), 88–95. https://doi.org/10.1016/j.foodcont.2010.05.013.
Mutanda, T., Mokoena, M.P., Olaniran, A.O., Wilhelmi, B.S., & Whiteley, C.G. (2014). Microbial enzymatic production and applications of short-chain fructooligosaccharides and inulooligosaccharides: recent advances and current perspectives. Journal of Industrial Microbiology and Biotechnology, 41(6), 893–906. https://doi.org/10.1007/s10295-014-1452-1.
Nguyen, Q.D., Rezessy-Szabó, J.M., Czukor, B., & Hoschke, Á. (2011). Continuous production of oligofructose syrup from Jerusalem artichoke juice by immobilized endo-inulinase. Process Biochemistry, 46(1), 298–303. https://doi.org/10.1016/j.procbio.2010.08.028. http://www.sciencedirect.com/science/article/pii/S1359511310003399.
Nishizawa, K., Nakajima, M., & Nabetani, H. (2000). A forced-flow membrane reactor for transfructosylation using ceramic membrane. Biotechnology and Bioengineering, 68(1), 92–97. https://doi.org/10.1002/(SICI)1097-0290(20000405)68:1<92::AID-BIT11>3.0.CO;2-1.
Panchal, G., Ganatra, A., Kosta, Y., & Panchal, D. (2011). Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering, 3(2), 332–337. http://www.ijcte.org/papers/328-L318.pdf.
Petkova, N., & Denev, P. (2015). Methods for determination of inulin. In Hadzhiev, B. (Ed.) Monograph of 4rd European young engineers conference 2015, food research & development institute. ISSN 2367-6213 (p. 135). Plovdiv: UFT Academic Publishing House.
Petkova, N., Radka, V., Denev, P., Ivanov, I., & Pavlov, A. (2014). HPLC-RID method for determination of inulin and fructooligosaccharides. Acta Scientifica Naturalis, 1, 99–107.
Prabhu, A.A., & Jayadeep, A. (2017). Optimization of enzyme-assisted improvement of polyphenols and free radical scavenging activity in red rice bran: a statistical and neural network-based approach. Preparative Biochemistry and Biotechnology, 47(4), 397–405. https://doi.org/10.1080/10826068.2016.1252926.
Rastall, R.A. (2010). Functional oligosaccharides: Application and manufacture. Annual Review of Food Science and Technology, 1, 305–339. https://doi.org/10.1146/annurev.food.080708.100746.
Sarup, R., Pal, R., & Kennedy, J.F. (2016). International Journal of Biological Macromolecules Recent insights in enzymatic synthesis of fructooligosaccharides from inulin. International Journal of Biological Macromolecules, 85, 565–572. https://doi.org/10.1016/j.ijbiomac.2016.01.026.
Tanriseven, A., & Aslan, Y. (2005). Immobilization of pectinex ultra sp-l to produce fructooligosaccharides. Enzyme and Microbial Technology, 36(4), 550–554. https://doi.org/10.1016/j.enzmictec.2004.12.001. http://www.sciencedirect.com/science/article/pii/S014102290400362X.
Ur Rehman, A., Kovacs, Z., Quitmann, H., Ebrahimi, M., & Czermak, P. (2016). Enzymatic production of fructo-oligosaccharides from inexpensive and abundant substrates using a membrane reactor system. Separation Science and Technology, 6395(April): 01496,395.2016,1167,740. https://doi.org/10.1080/01496395.2016.1167740.
Van Den Broeke, J., Langergraber, G., & Weingartner, A. (2006). On-line and in-situ UV/vis spectroscopy for measurements: a brief review. Spectroscopy Europe 1, 18(4), 4–7.
Veloso, A.C.A., Rodrigues, L.R., Dias, L.G., & Peres, AM. (2012). Chapter 14 uv spectrophotometry method for dietary sugars. In Dietary sugars: chemistry, analysis, function and effects, the royal society of chemistry. https://doi.org/10.1039/9781849734929-00229 (pp. 229–248).
Funding
The preparation of this work was supported by the Marie Skłodowska-Curie grant of the EU FP7 Framework Programme (PCIG11-GA-2012-322219) and the Bolyai Scholarship Programme of the Hungarian Academy of Sciences.
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Erdős, B., Grachten, M., Czermak, P. et al. Artificial Neural Network-Assisted Spectrophotometric Method for Monitoring Fructo-oligosaccharides Production. Food Bioprocess Technol 11, 305–313 (2018). https://doi.org/10.1007/s11947-017-2011-3
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DOI: https://doi.org/10.1007/s11947-017-2011-3