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Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network.
Mol Nutr Food Res. 2007 Apr; 51(4):383-9.MN

Abstract

The artificial neural network (ANN) modeling approach was used to predict acrylamide formation and browning ratio (%) in potato chips as influenced by time x temperature covariants. A series of feed-forward type network models with back-propagation training algorithm were developed. Among various network configurations, 4-5-3-2 configuration was found as the best performing network topology. Four neurons in the input layer were reflecting the asparagine concentration, glucose concentration, frying temperature, and frying time. The output layer had two neurons representing acrylamide concentration and browning ratio of potato chips. The ANN modeling approach was shown to successfully predict acrylamide concentration (R = 0.992) and browning ratio (R = 0.997) of potato chips during frying at different temperatures in time-dependent manner for potatoes having different concentrations of asparagine and glucose. It was concluded that ANN modeling is a useful predictive tool which considers only the input and output variables rather than the complex chemistry.

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Authors+Show Affiliations

Serpen A
Department of Food Engineering, Hacettepe University, Beytepe, Ankara, Turkey.
Gökmen V
No affiliation info available

MeSH

AcrylamideAsparagineCarbohydratesFood HandlingHot TemperatureMaillard ReactionNeural Networks, ComputerSolanum tuberosumTime Factors

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

17357985

Citation

Serpen, Arda, and Vural Gökmen. "Modeling of Acrylamide Formation and Browning Ratio in Potato Chips By Artificial Neural Network." Molecular Nutrition & Food Research, vol. 51, no. 4, 2007, pp. 383-9.
Serpen A, Gökmen V. Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network. Mol Nutr Food Res. 2007;51(4):383-9.
Serpen, A., & Gökmen, V. (2007). Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network. Molecular Nutrition & Food Research, 51(4), 383-9.
Serpen A, Gökmen V. Modeling of Acrylamide Formation and Browning Ratio in Potato Chips By Artificial Neural Network. Mol Nutr Food Res. 2007;51(4):383-9. PubMed PMID: 17357985.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modeling of acrylamide formation and browning ratio in potato chips by artificial neural network. AU - Serpen,Arda, AU - Gökmen,Vural, PY - 2007/3/16/pubmed PY - 2007/7/20/medline PY - 2007/3/16/entrez SP - 383 EP - 9 JF - Molecular nutrition & food research JO - Mol Nutr Food Res VL - 51 IS - 4 N2 - The artificial neural network (ANN) modeling approach was used to predict acrylamide formation and browning ratio (%) in potato chips as influenced by time x temperature covariants. A series of feed-forward type network models with back-propagation training algorithm were developed. Among various network configurations, 4-5-3-2 configuration was found as the best performing network topology. Four neurons in the input layer were reflecting the asparagine concentration, glucose concentration, frying temperature, and frying time. The output layer had two neurons representing acrylamide concentration and browning ratio of potato chips. The ANN modeling approach was shown to successfully predict acrylamide concentration (R = 0.992) and browning ratio (R = 0.997) of potato chips during frying at different temperatures in time-dependent manner for potatoes having different concentrations of asparagine and glucose. It was concluded that ANN modeling is a useful predictive tool which considers only the input and output variables rather than the complex chemistry. SN - 1613-4125 UR - https://www.unboundmedicine.com/medline/citation/17357985/Modeling_of_acrylamide_formation_and_browning_ratio_in_potato_chips_by_artificial_neural_network_ L2 - https://doi.org/10.1002/mnfr.200600121 DB - PRIME DP - Unbound Medicine ER -