Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteratio...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-10, Vol.22 (20), p.7789
Main Authors: Pulluri, Kranthi Kumar, Kumar, Vaegae Naveen
Format: Article
Language:English
Subjects:
Accuracy
adulteration
Analysis
artificial neural network
Beef
Classification
Consumers
Contamination
Data collection
Datasets
Design
Discriminant analysis
electronic nose
Electronic noses
food
Food contamination
Food products
Gases
Mean square errors
Meat industry
Methodology
Pattern recognition
Pattern recognition systems
Pork
Qualitative analysis
Quantitative analysis
Ratios
Sensors
Signal processing
support vector machine
Support vector machines
VOCs
Volatile organic compounds
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Summary:Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22207789