Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning
Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulteran...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9393967/ |
| _version_ | 1818871144217837568 |
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| author | N. Sowmya Vijayakumar Ponnusamy |
| author_facet | N. Sowmya Vijayakumar Ponnusamy |
| author_sort | N. Sowmya |
| collection | DOAJ |
| description | Adulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost, portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet (UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%, 90%, 90%, 91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure milk sample. |
| first_indexed | 2024-12-19T12:18:15Z |
| format | Article |
| id | doaj.art-d76a4ee9a93a4cc3a073304ed1dc49b1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T12:18:15Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d76a4ee9a93a4cc3a073304ed1dc49b12022-12-21T20:21:54ZengIEEEIEEE Access2169-35362021-01-019539795399510.1109/ACCESS.2021.30705589393967Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine LearningN. Sowmya0https://orcid.org/0000-0002-9888-6078Vijayakumar Ponnusamy1https://orcid.org/0000-0002-3929-8495Department of ECE, SRM Institute of Science and Technology, Chennai, IndiaDepartment of ECE, SRM Institute of Science and Technology, Chennai, IndiaAdulteration in milk is a common scenario for gaining extra profit, which may cause severe harmful effects on humans. The qualitative spectroscopic technique provides a better solution for detecting the toxic contents of milk and foodstuffs. All the available spectroscopic methods for milk adulterant detection are based on laboratory-based with costly equipment. This laboratory-based detection takes a long time and is more expensive, which may not be afforded by a common man. To overcome this issue, this research work involves the design and development of a low-cost, portable, multispectral, AI-based, non-destructive spectroscopic sensor system that can be used to detect the milk adulterant in real-time. The designed sensor system uses the spectroscopic method with wavelength ranges from (410-940nm) which consists of three different bands Ultraviolet (UV), visible, and Infra-Red(IR) spectrum to improve the accuracy of detection. The sensor system is connected to the internet via the developed IoT application module, which displays the detected adulterant results in a dedicated web page designed for this purpose. This IoT application enables the adulterant detected results published on the internet immediately with location information for bringing transparency. Adulterant detection problem is formulated as a classification problem and solved by machine learning algorithms of a decision tree, Naive Bayes, linear discriminant analysis, support vector machine and neural network model. The average accuracy of linear discriminant analysis, support vector machine, Naive Bayes, decision tree and neural network model are obtained as 88.1%, 90%, 90%, 91.7% and 92.7% respectively. Genetic algorithm framework is formulated for hyperparameter tuning of neural network model which improved the accuracy from 92.7% to 100%. The model is trained for five different classes of four adulterants, namely Sodium Salicylate, Dextrose, Hydrogen Peroxide, Ammonium Sulphate, and one pure milk sample.https://ieeexplore.ieee.org/document/9393967/Back propagation algorithmK-means clusteringmachine learningmilk adulterationmultispectral spectroscopyneural network |
| spellingShingle | N. Sowmya Vijayakumar Ponnusamy Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning IEEE Access Back propagation algorithm K-means clustering machine learning milk adulteration multispectral spectroscopy neural network |
| title | Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning |
| title_full | Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning |
| title_fullStr | Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning |
| title_full_unstemmed | Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning |
| title_short | Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning |
| title_sort | development of spectroscopic sensor system for an iot application of adulteration identification on milk using machine learning |
| topic | Back propagation algorithm K-means clustering machine learning milk adulteration multispectral spectroscopy neural network |
| url | https://ieeexplore.ieee.org/document/9393967/ |
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