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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/7789 |
| _version_ | 1797470017312260096 |
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| author | Kranthi Kumar Pulluri Vaegae Naveen Kumar |
| author_facet | Kranthi Kumar Pulluri Vaegae Naveen Kumar |
| author_sort | Kranthi Kumar Pulluri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:30:46Z |
| format | Article |
| id | doaj.art-82229664ce124fa7bab65b5852550c71 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:46Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-82229664ce124fa7bab65b5852550c712023-11-24T02:26:00ZengMDPI AGSensors1424-82202022-10-012220778910.3390/s22207789Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-NoseKranthi Kumar Pulluri0Vaegae Naveen Kumar1School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaFood 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.https://www.mdpi.com/1424-8220/22/20/7789electronic nosefoodadulterationartificial neural networksupport vector machinebeef |
| spellingShingle | Kranthi Kumar Pulluri Vaegae Naveen Kumar Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose Sensors electronic nose food adulteration artificial neural network support vector machine beef |
| title | Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose |
| title_full | Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose |
| title_fullStr | Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose |
| title_full_unstemmed | Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose |
| title_short | Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose |
| title_sort | qualitative and quantitative detection of food adulteration using a smart e nose |
| topic | electronic nose food adulteration artificial neural network support vector machine beef |
| url | https://www.mdpi.com/1424-8220/22/20/7789 |
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