Using electronic nose to recognize fish spoilage with an optimum classifier

  • Meisam Vajdi
  • Mohammad J. VaridiEmail author
  • Mehdi Varidi
  • Mohebbat Mohebbi
Original Paper


For automatic, rapid, accurate and objective classification of fish freshness under cold storage an electronic nose using seven metal dioxide gas sensors was developed to detect fish volatiles. Total viable count and Total volatile base nitrogen analyses were conducted simultaneously to indicate fish quality status. By sampling fish headspace, patterns were obtained during 15 storage days. 35 appropriate odor parameters were selected from each test. Principle component analysis was applied to reduce the 35-dimensional vectors to 5-dimensional vectors and clustered samples into fresh, semi fresh and spoiled. With 5-dimensional vectors as input, multilayer perceptron neural network modeled fish spoilage based on these three classes with 96.87 percent correct rate of test data. We found that the newly introduced hyper disk models maximum margin optimum classifier yielded 100 percent correct rate that could be successfully applied in industry for the diagnosis of fish spoilage.

Graphical abstract


Electronic nose Hyper disk models Neural network Pattern recognition Quadratic programming Real-time data acquisition 



Colony forming unit


Electronic nose


Exponential function


Sensors baseline response, Volt


Sensor response while sampling fish headspace by carrier nitrogen, Volt


Response only due to fish headspace gases, Volt


Average of the conductance in the interval [0–900] s


Steady-state conductance from the average of the conductance during the last 300 s

\(\left( {{\raise0.7ex\hbox{${dG}$} \!\mathord{\left/ {\vphantom {{dG} {dt}}}\right.\kern-0pt}\!\lower0.7ex\hbox{${dt}$}}} \right)\)

Slope of the dynamic conductance from the conductance curve within the interval [300–1500] s


Difference between first and last conductance.


Hyper disk of a class


Hyper disk models maximum margin optimum classifier


Multilayer perceptron neural network


Mean-squared error


Principal component analysis


Printed circuit board


Quadratically constrained quadratic optimization problem


Radial basis function


Radius that were calculated by solving a quadratic program


Center of bounding hyper sphere


Second order cone programming


Support vector machines


Area below the conductance curve between 300 and 1800s calculated by the ‘trapezoidal’ integration method


Taguchi gas sensors


Total volatile base nitrogen


Total viable count


Volatile organic compounds

\({\alpha _i}\)

Lagrange multipliers for the center

\({\left\| {x_{i}^{T} - {x_j}} \right\|^2}\)

Squared Euclidean distance between the two vectors


Sigma values



This work was supported at Ferdowsi University of Mashhad [Grant Number 18108]. The authors gratefully acknowledge emeritus Professor Ali Jabari Azad of Physics Department for constructive collaboration.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Food Science and TechnologyFerdowsi University of MashhadMashhadIran

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