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2015 | 29 | 2 |

Tytuł artykułu

Extruded bread classification on the basis of acoustic emission signal with application of artificial neural networks

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

29

Numer

2

Opis fizyczny

p.221-229,fig.,ref.

Twórcy

  • Department of Physics, University of Life Sciences in Lublin, Akademicka 13, 20-950 Lublin, Poland
autor
  • Department of Physics, University of Life Sciences in Lublin, Akademicka 13, 20-950 Lublin, Poland
autor
  • Department of Food Engineering and Process Management, Warsaw University of Life Sciences - SGGW, Nowoursynowska 159c, 02-776 Warsaw, Poland

Bibliografia

  • AlChakra W., Allaf K., and Jemai A.B., 1996. Characterization of brittle food products: application of the acoustical emission method. J. Texture Studies, 27, 327-348.
  • Aslan K., Bozdemir H., Şahin C., Oğulata S.N., and Erol R., 2008. A radial basis function neural network model for classification of epilepsy using EEG signals. J. Medical Systems, 32, 403-408.
  • Bishop C.M., 1995. Neural networks for pattern recognition, Oxford University Press.
  • Castellano G., Fanelli A., and Pelillo M., 1997. An iterative pruning algorithm for feedforward neural networks, IEEE Trans. Neural Networks, 8, 519-531.
  • Cottrell M., Fort J.C., and Pages G., 1998. Theoretical aspects of the SOM algorithm. Neurocomputing, 21, 119-138.
  • Dacremont C., 1995. Spectral Composition of Eating Sounds Generated by Crispy Crunchy and Crackly Foods. J. Texture Studies, 26, 27-43.
  • Drake B.K., 1963. Food crushing sounds. An introductory study. J. Food Sci., 28, 233-241.
  • Drake B.K., 1965. Food crushing sounds: comparisons of objective and subjective data. J. Food Sci., 30, 556-559.
  • Duizer L., 2001. A review of acoustic research for studying the sensory perception of crisp, crunchy and crackly textures. Trends Food Sci. Technol., 12, 17-24.
  • Edmister J.A. and Vickers Z.M., 1985. Instrumental acoustical measures of crispness in foods. J. Texture Studies, 16, 153-167.
  • Gelzinis A., Verikas A., and Bacauskiene M., 2008. Automated speech analysis applied to laryngeal disease categorization, Computer Methods Programs Biomedicine, 91, 36-47.
  • Godin N., Huguet S., Gaertner R., and Salmon L., 2004. Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers. NDT&E International, 37, 253-264.
  • Gondek E., Lewicki P.P., and Ranachowski Z., 2006. Influence of water activity on the acoustic properties of breakfast cereals. J. Texture Studies, 37(5), 497-515.
  • Hung M.S., Hu M.Y., Shanker M.S., and Patuwo B.E., 1996. Estimating posterior probabilities in classification problems with neural networks. Int. J. Computational Intelligence Organizations, 1(1), 49-60.
  • Isa D., Kallimani V.P., and Lee L.H., 2009. Using the self orga-nizing map for clustering of text documents. Expert Systems Applications, 96, 9584-9591.
  • Karnin E.D., 1990. A simple procedure for pruning back-propagation trained neural networks. IEEE Trans. Neural Networks, 1(2), 239-242.
  • Kline D.M. and Berardi V.L., 2005. Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neural Computing Appl., 15, 310-318.
  • Kohonen T., 2001. Self-Organizing Maps, Springer, Berlin.
  • Lee J.A. and Verleysen M., 2002. Self-organizing maps with recursive neighbourhood adaptation. Neural Networks, 15, 993-1003.
  • Lewicki P.P., Marzec A., and Ranachowski Z., 2009. Acoustic properties of foods. In: Food Properties Handbook (Ed. M.S. Rahman). CRC Press, Boca Raton, FL, USA.
  • Marzec A., Lewicki P.P., and Pietrowska A., 2007a. Staling of bread evaluation with application of acoustical method (in Polish). Food Sci. Technol. Quality, 2(51), 72-79.
  • Marzec A., Lewicki P.P., and Ranachowski Z., 2007b. Influence of water activity on acoustic emission of flat extruded bread. J. Food Eng., 79, 410-422.
  • Mulier F.M. and Cherkassky V.S., 1995. Statistical analysis of self-organization. Neural Networks, 8(5), 717-727.
  • Ono K. and Huang Q., 1994. Pattern recognition analysis of acoustic emission signals. Progress in Acoustic Emission. Japanese Soc. NDI, 7, 69-78.
  • Pearson T.C., Eniscetin A., Tewfik A.H., and Haff R.P., 2007. Feasibility of impact acoustic emissions for detection of damaged wheat kernels. Digital Signal Proc., 17, 617-633.
  • Primo-Martín C., Sözer N., Hamer R.J., and Vliet T.V., 2009. Effect of water activity on racture and acoustic characteristics of a crust model. J. Food Eng., 90(2), 277-284.
  • Saeleaw M. and Schleining G., 2011. A review: Crispness in dry foods and quality measurements based on acoustic-mechanical destructive techniques. J. Food Eng., 105, 387-399.
  • Salvador A., Varela P., Sanz T., and Fiszman S.M., 2009. Under-standing potato chips crispy texture by simultaneous fracture and acoustic measurements, and sensory analysis, LWT – Food Science and Technol., 42(3), 763-767.
  • Seymour S.K. and Hamann D.D., 1988. Crispness and crunchiness of selected low moisture foods. J. Texture Studies, 19, 79-95.
  • Song X.-H. and Hopke P.K., 1996. Kohonen neural network as a pattern recognition metod based on weight interpretation. Analytica Chimica Acta, 334, 57-66.
  • Vickers Z.M. and Bourne M.C., 1976. Crispness in foods – a review. J. Food Sci., 41, 153-157.
  • Vickers Z.M. and Wasserman S.S., 1979. Sensory qualities of food sounds based on individual perceptions. J. Texture Studies, 10, 319-332.
  • Yang P., Zhu Q., and Zhong X., 2009. Subtractive clustering based RBF neural network model for outlier detection. J. Computers, 4(8), 755-762.
  • Zdunek A., Frankevych L., Konstankiewicz K., and Ranachowski Z., 2008. Comparision of puncture test, acoustic emission and spatial-temporal speckle correlation technique as me-thod foe apple quality evaluation. Acta Agrophysica, 11(1), 303-315.
  • Zdunek A., Cybulska J., Konopacka D., and Rutkowski K., 2010a. New contact acoustic emission detector for texture evaluation of apples. J. Food Eng., 99, 83-91.
  • Zdunek A., Konopacka D., and Jesionkowska K., 2010b. Crispness and crunchiness judgment of apples based on contact acoustic emission. J. Texture Studies, 41,75-91.
  • Zdunek A., Cybulska J., Konopacka D., and Rutkowski K., 2011a. Inter-laboratory analysis of firmness and sensory texture of stored apples. Int. Agrophysics, 25, 67-75.
  • Zdunek A., Cybulska J., Konopacka D., and Rutkowski K., 2011b. Evaluation of apple texture with contact acoustic emission detector: A study on performance of calibration models. J. Food Eng., 106(1), 80-87.

Uwagi

PL

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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