Food and Bioprocess Technology

, Volume 7, Issue 5, pp 1433–1444 | Cite as

Prediction of Physical Quality Parameters of Frozen Shrimp (Litopenaeus vannamei): An Artificial Neural Networks and Genetic Algorithm Approach

  • Imran Ahmad
  • Chawalit Jeenanunta
  • Pisit Chanvarasuth
  • Somrote Komolavanij
Original Paper
First Online:
Received:
Accepted:

Abstract

Application of genetic algorithm to optimize an artificial neural network (ANN) model for predicting end-of-storage quality parameters of frozen shrimp (Litopenaeus vannamei), which influence consumer purchase decisions, is demonstrated in this paper. Freezing rate (FR), thawing rate (TR), storage time, width, thickness, and length of frozen shrimp were measured and chosen as input variables to train the ANN against Commission International de l’ Eclairage Color L*a*b* values, and textural properties (hardness, cohesiveness, and chewiness) as dependent variables. Experimentally obtained randomized data points (500) were used to develop the network, of which 20 % were used for testing the network, as an unseen environment. The developed genetic algorithm–artificial neural network (GANN) which included one hidden layer with 3–17 neurons successfully predicted color and textural values with correlation coefficient, R 2, of >0.9 and root mean square error (RMSE) of <1.6. The redness (a*) and cohesiveness took the longest training time and highest number of generations, as compared to the other parameters. Percent relative importance of input variables to output variables indicated that TR, FR, and storage time were the most important variables for the prediction of color and texture parameters. The results are compared with multiple linear regression (MLR) and ANN trained with backpropagation (BP) algorithm. The results indicate that the GANN model shows much better prediction, as compared to MLR and BP with smallest RMSE and highest R 2.

Keywords

Frozen shrimp Freezing rate Thawing rate Storage temperature GANN Genetic algorithm Artificial neural networks 

Nomenclature and abbreviations

ANN

Artificial Neural Network

FR

Freezing rate (°C min−1)

TR

Thawing rate (°C min−1)

T1

Initial product temperature (°C)

T2

Final product temperature (°C)

t1

Starting time for storage (min)

t2

Ending time for storage (min)

L*a*b*

CIE color parameters

TH

Thickness (m)

W

Width (m)

L

Length (m)

H

Hardness

Co

Cohesiveness

Ch

Chewiness

GA

Genetic Algorithm

MSE

Mean squared error

RMSE

Root mean squared error

x

Input neurons

y

Output neurons

bij

Bias

w

Weights

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Imran Ahmad
    • 1
  • Chawalit Jeenanunta
    • 1
  • Pisit Chanvarasuth
    • 1
  • Somrote Komolavanij
    • 2
  1. 1.Management Technology Program, Sirindhorn International Institute of TechnologyThammasat UniversityPathum ThaniThailand
  2. 2.Panyapiwat Institute of ManagementNonthaburiThailand

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