Using a Low-Cost Components e-Nose for Basic Detection of Different Foodstuffs

A low-cost handheld e-nose unit for assessing composition and quality of food in the field or for automatic discrimination of different foodstuffs would have significant commercial advantages and provide a positive impact on consumer safety and confidence. Whilst many studies have focused on the fre...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2022-07, Vol.22 (14), p.13872-13881
Main Authors: Oates, Martin J., Gonzalez-Teruel, Juan D., Ruiz-Abellon, Maria Carmen, Guillamon-Frutos, Antonio, Ramos, Jose A., Torres-Sanchez, Roque
Format: Article
Language:English
Subjects:
Classification
Cooking
Costs
discrete Fourier transform (DFT)
Discrete Fourier transforms
Discriminant analysis
discriminant analysis (DA)
E-nose
Electronic noses
Food
food identification
Fourier transforms
Gas detectors
Gas sensors
Gases
Low cost
Machine learning
Parameters
Pork
principal component analysis (PCA)
Quality assessment
random forest
Salmon
Sensor arrays
Sensors
Temperature sensors
Waveforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A low-cost handheld e-nose unit for assessing composition and quality of food in the field or for automatic discrimination of different foodstuffs would have significant commercial advantages and provide a positive impact on consumer safety and confidence. Whilst many studies have focused on the freshness of specific foodstuffs, the preliminary results presented in this report indicate that a low-cost (circa 30 euro) e-nose, based on MQ series gas sensors, using sinusoidally varying heater voltages, can successfully differentiate between samples of unspoiled lamb, hake, salmon, beef, pork and chicken, even using default, non-optimized control parameters, which could be applied in food adulteration and fraudulent labelling detection or in smart appliances to automatically select cooking parameters. Samples were exposed to the e-nose for five cycles of two minutes each and, by subjecting the response waveforms to Discrete Fourier Transform (DFT) analysis, harmonic component amplitudes were extracted. These data items were then fed into two different Machine Learning supervised classification methods (discriminant analysis and random forest) to determine which foodstuff was present. The relevance of the different DFT harmonic components and MQ sensors in the foodstuffs classification was quantified. Classification accuracies in excess of 95% were demonstrated.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3181513