Optimisation of the addition of carrot dietary fibre to a dry fermented sausage (sobrassada) using artificial neural networks

被引:25
|
作者
Eim, Valeria S. [1 ]
Simal, Susana [1 ]
Rossello, Carmen [1 ]
Femenia, Antoni [1 ]
Bon, Jose [2 ]
机构
[1] Univ Balear Isl, Dept Chem, Palma De Mallorca 07122, Spain
[2] Univ Politecn Valencia, Dept Food Technol, ASPA Res Team, Valencia 46071, Spain
关键词
Dry fermented sausage; Carrot dietary fibre; Optimisation; Artificial neural network; MEAT-BASED PRODUCT; MASS-TRANSFER; EXTERNAL RESISTANCE; SENSORY PROPERTIES; FAT SUBSTITUTE; ORANGE FIBER; MANUFACTURE; PARAMETERS; FOOD;
D O I
10.1016/j.meatsci.2013.02.009
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
An optimisation problem was formulated to maximise the amount of carrot dietary fibre (CDF) in a dry fermented sausage, while maintaining product quality, by using 0-12% CDF as the decision variable, and limiting values of several physico-chemical and textural parameters (moisture content, water activity, pH, colour, non-protein nitrogen, free fatty acid, compression work and hardness) as constraints. The evolution of each quality parameter during the ripening process was estimated by developing a multi-layer feed forward artificial neural network (ANN), taking into consideration the CDF concentration and the ripening time as independent variables. Results indicate an optimum CDF concentration of 4.9% with a good correlation between experimental and estimated values (mean relative error-3.35%). (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:341 / 348
页数:8
相关论文
共 50 条
  • [1] Effects of addition of carrot dietary fibre on the ripening process of a dry fermented sausage (sobrassada)
    Eim, Valeria S.
    Simal, Susana
    Rossello, Carmen
    Femenia, Antoni
    MEAT SCIENCE, 2008, 80 (02) : 173 - 182
  • [2] Influence of the Addition of Dietary Fiber on the Drying Curves and Microstructure of a Dry Fermented Sausage (Sobrassada)
    Eim, Valeria S.
    Garcia-Perez, Jose V.
    Rossello, Carmen
    Femenia, Antoni
    Simal, Susana
    DRYING TECHNOLOGY, 2012, 30 (02) : 146 - 153
  • [3] Predicting survival of Escherichia coli O157:H7 in dry fermented sausage using artificial neural networks
    Palanichamy, A.
    Jayas, D. S.
    Holley, R. A.
    JOURNAL OF FOOD PROTECTION, 2008, 71 (01) : 6 - 12
  • [4] Optimisation of Oxygen Delignification Using Artificial Neural Networks
    Bohacek, S.
    Chemical and Biochemical Engineering Quarterly, 11 (02): : 75 - 80
  • [5] Optimisation of oxygen delignification using artificial neural networks
    Bohacek, S
    CHEMICAL AND BIOCHEMICAL ENGINEERING QUARTERLY, 1997, 11 (02) : 75 - 80
  • [6] Towards pultrusion process optimisation using artificial neural networks
    Wilcox, JAD
    Wright, DT
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1998, 83 (1-3) : 131 - 141
  • [7] Well-placement optimisation using sequential artificial neural networks
    Jang, Ilsik
    Oh, Seeun
    Kim, Yumi
    Park, Changhyup
    Kang, Hyunjeong
    ENERGY EXPLORATION & EXPLOITATION, 2018, 36 (03) : 433 - 449
  • [8] Optimisation of competition indices using simulated annealing and artificial neural networks
    Richards, M.
    McDonald, A. J. S.
    Aitkenhead, M. J.
    ECOLOGICAL MODELLING, 2008, 214 (2-4) : 375 - 384
  • [9] Optimisation of Ondansetron Orally Disintegrating Tablets Using Artificial Neural Networks
    Aksu, Buket
    Yegen, Gizem
    Purisa, Sevim
    Cevher, Erdal
    Ozsoy, Yildiz
    TROPICAL JOURNAL OF PHARMACEUTICAL RESEARCH, 2014, 13 (09) : 1379 - U22
  • [10] Process modelling and optimisation using artificial neural networks and gradient search method
    Bai, H.
    Kwong, C.K.
    Tsim, Y.C.
    International Journal of Advanced Manufacturing Technology, 2007, 31 (7-8): : 790 - 796