IoT integrated fuzzy classification analysis for detecting adulterants in cow milk
Internet of Things (IoT) and Artificial Intelligence (AI) are two of the emerging techniques used in creating more significant opportunities in smart dairy farming (SDF). Currently, the demand for milk is continuously increasing due to the world's growing population. Thus, some suppliers are in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Sensing and Bio-Sensing Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214180422000150 |
| _version_ | 1818547958131458048 |
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| author | Prashant P. Lal Avishay A. Prakash Aneesh A. Chand Kushal A. Prasad Utkal Mehta Mansour H. Assaf Francis S. Mani Kabir A. Mamun |
| author_facet | Prashant P. Lal Avishay A. Prakash Aneesh A. Chand Kushal A. Prasad Utkal Mehta Mansour H. Assaf Francis S. Mani Kabir A. Mamun |
| author_sort | Prashant P. Lal |
| collection | DOAJ |
| description | Internet of Things (IoT) and Artificial Intelligence (AI) are two of the emerging techniques used in creating more significant opportunities in smart dairy farming (SDF). Currently, the demand for milk is continuously increasing due to the world's growing population. Thus, some suppliers are inclined towards adopting fraudulent practices such as introducing adulterants into milk to eliminate the demand and supply gap. Conventional detection techniques require specific chemicals and equipment to determine the presence of adulterants in milk. Though effective, this technique has the downsides of producing qualitative results that are laborious, time-consuming and the same milk sample cannot be further analyzed for other adulterants. Hence, this paper presents an IoT-based solution to detect adulterants in milk by measuring its pH and electrical conductivity (EC) parameters. To achieve this, a fuzzy logic system was designed in MATLAB® using the Fuzzy Logic Toolbox™ and implemented on an arduino mega microcontroller to analyze the impurities present in milk samples through hardware implemented. This research revealed that milk's pH and EC values with no adulteration range from 6.45 to 6.67 and 4.65 mS/cm to 5.26 mS/cm, respectively. Finally, the collected data is stored in the cloud using the ThingSpeak™ web platform, interconnected with an IoT (ESP8266 Wi-Fi module). |
| first_indexed | 2024-12-12T08:13:35Z |
| format | Article |
| id | doaj.art-72b1b6d9844f4ac1990023ae66352a71 |
| institution | Directory Open Access Journal |
| issn | 2214-1804 |
| language | English |
| last_indexed | 2024-12-12T08:13:35Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sensing and Bio-Sensing Research |
| spelling | doaj.art-72b1b6d9844f4ac1990023ae66352a712022-12-22T00:31:42ZengElsevierSensing and Bio-Sensing Research2214-18042022-06-0136100486IoT integrated fuzzy classification analysis for detecting adulterants in cow milkPrashant P. Lal0Avishay A. Prakash1Aneesh A. Chand2Kushal A. Prasad3Utkal Mehta4Mansour H. Assaf5Francis S. Mani6Kabir A. Mamun7School of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, FijiSchool of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, FijiSchool of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, Fiji; Corresponding author.School of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, FijiSchool of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, FijiSchool of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, FijiSchool of Agriculture, Geography, Environment, Ocean and Natural Sciences (SAGEONS), Suva, FijiSchool of Information Technology, Engineering, Mathematics and Physics (STEMP), Suva, FijiInternet of Things (IoT) and Artificial Intelligence (AI) are two of the emerging techniques used in creating more significant opportunities in smart dairy farming (SDF). Currently, the demand for milk is continuously increasing due to the world's growing population. Thus, some suppliers are inclined towards adopting fraudulent practices such as introducing adulterants into milk to eliminate the demand and supply gap. Conventional detection techniques require specific chemicals and equipment to determine the presence of adulterants in milk. Though effective, this technique has the downsides of producing qualitative results that are laborious, time-consuming and the same milk sample cannot be further analyzed for other adulterants. Hence, this paper presents an IoT-based solution to detect adulterants in milk by measuring its pH and electrical conductivity (EC) parameters. To achieve this, a fuzzy logic system was designed in MATLAB® using the Fuzzy Logic Toolbox™ and implemented on an arduino mega microcontroller to analyze the impurities present in milk samples through hardware implemented. This research revealed that milk's pH and EC values with no adulteration range from 6.45 to 6.67 and 4.65 mS/cm to 5.26 mS/cm, respectively. Finally, the collected data is stored in the cloud using the ThingSpeak™ web platform, interconnected with an IoT (ESP8266 Wi-Fi module).http://www.sciencedirect.com/science/article/pii/S2214180422000150Milk adulterantsInternet of Things (IoT)pHElectrical ConductivityIndustry 4.0 |
| spellingShingle | Prashant P. Lal Avishay A. Prakash Aneesh A. Chand Kushal A. Prasad Utkal Mehta Mansour H. Assaf Francis S. Mani Kabir A. Mamun IoT integrated fuzzy classification analysis for detecting adulterants in cow milk Sensing and Bio-Sensing Research Milk adulterants Internet of Things (IoT) pH Electrical Conductivity Industry 4.0 |
| title | IoT integrated fuzzy classification analysis for detecting adulterants in cow milk |
| title_full | IoT integrated fuzzy classification analysis for detecting adulterants in cow milk |
| title_fullStr | IoT integrated fuzzy classification analysis for detecting adulterants in cow milk |
| title_full_unstemmed | IoT integrated fuzzy classification analysis for detecting adulterants in cow milk |
| title_short | IoT integrated fuzzy classification analysis for detecting adulterants in cow milk |
| title_sort | iot integrated fuzzy classification analysis for detecting adulterants in cow milk |
| topic | Milk adulterants Internet of Things (IoT) pH Electrical Conductivity Industry 4.0 |
| url | http://www.sciencedirect.com/science/article/pii/S2214180422000150 |
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