Application of Electronic Nose to Detect Pomegranate Paste Adulteration Using Pattern Recognition Methods and Artificial Neural Network

Document Type : Research Article


1 Institute of Agricultural Education and Extension, Agricultural Research, Education and Extension Organization (AREEO),Tehran, Iran

2 Lecturer, Institute of Agricultural Education and Extension, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.


Adulteration in food products is regarded as a main challenge in food industry, which adversely affects food quality and health. Owing to pleasant taste and antioxidant properties, pomegranate paste is one of the most valuable and desirable foods in the people diets in some countries. As a luxury and expensive food, it is likely to be adulterated by some producers or distributors for the more profits. In this study development and application of machine olfaction system using array of gas sensors to detect adulteration in pomegranate paste was aimed. Principal Component Analysis (PCA), Linear Discrimination analysis (LDA) and Artificial Neural Network (ANN) methods were used to analyze response of the sensor arrays. Based on the results, PCA with two components PC1 and PC2 described 94% of total data variance. In LDA method, the classification accuracy of pomegranate paste samples was 97.65% which higher than PCA method. The values of correlation coefficient (R2) and root mean squared error (RSME) of neural network in ANN method using the structure of 6-9-7 were 0.984 and 0.0018 respectively. This study reveals that the electronic nose device can be used as a non-destructive tool to classify and detect adulteration of different classes of pomegranate paste.

Graphical Abstract

Application of Electronic Nose to Detect Pomegranate Paste Adulteration Using Pattern Recognition Methods and Artificial Neural Network


  • Machine Olfaction system development to detection of pomegranate paste adulteration  
  • Sensor response analysis to determine pattern recognition by using Chemometric method
  • Classification and detection adulteration in pomegranate paste by artificial neural network with the high accuracy


Main Subjects

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Volume 10, Issue 1
November 2022
Pages 35-48
  • Receive Date: 28 February 2022
  • Revise Date: 31 August 2022
  • Accept Date: 03 September 2022
  • First Publish Date: 03 September 2022