DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF
A low-cost, easy-to-use e-nose is developed to detect the spoilage of ground meat. E-nose consists of hardware, software and data processing components. The main elements of hardware component are gas sensors sensitive to hydrogen sulfide (H2S) and ammonia (NH3). Using MIT App Inventor 2 an Android...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Bursa Uludag University
2023-04-01
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| Series: | Uludağ University Journal of The Faculty of Engineering |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/2450490 |
| _version_ | 1797777635654238208 |
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| author | Simge Özalp Kemal Eren Kızıl |
| author_facet | Simge Özalp Kemal Eren Kızıl |
| author_sort | Simge Özalp |
| collection | DOAJ |
| description | A low-cost, easy-to-use e-nose is developed to detect the spoilage of ground meat. E-nose consists of hardware, software and data processing components. The main elements of hardware component are gas sensors sensitive to hydrogen sulfide (H2S) and ammonia (NH3). Using MIT App Inventor 2 an Android application is developed to run the hardware component, retrieve the data, preprocess and send it to Google Sheets. Classification model is developed, and data management is carried out in Google Colab and Google Script. Logistic regression method is used to develop classification models from the collected signals. The model classified the samples as "spoiled" and "fresh" based on the gas concentrations. The Nessler solution is used to determine the actual spoilage state. Ground beef samples stored in the refrigerator and at room temperature are used to obtain spoiled and fresh samples to develop a logistic regression model. A total of 36 samples are used to develop model. Another set of 24 samples is used to test model and prototype device performance. It is observed that all samples used in the testing phase were classified correctly. The cost of the system has been determined as approximately $100 considering January 2021 exchange rates. |
| first_indexed | 2024-03-12T23:06:39Z |
| format | Article |
| id | doaj.art-60990bcfa54b4eea989026aa8c23212f |
| institution | Directory Open Access Journal |
| issn | 2148-4155 |
| language | English |
| last_indexed | 2024-03-12T23:06:39Z |
| publishDate | 2023-04-01 |
| publisher | Bursa Uludag University |
| record_format | Article |
| series | Uludağ University Journal of The Faculty of Engineering |
| spelling | doaj.art-60990bcfa54b4eea989026aa8c23212f2023-07-18T15:47:11ZengBursa Uludag UniversityUludağ University Journal of The Faculty of Engineering2148-41552023-04-0128131733210.17482/uumfd.11221151779DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEFSimge Özalp0Kemal Eren Kızıl1İTÜ ETA Vakfı Çanakkale KolejiİTÜ ETA Vakfı Doğa Koleji Çanakkale KampüsüA low-cost, easy-to-use e-nose is developed to detect the spoilage of ground meat. E-nose consists of hardware, software and data processing components. The main elements of hardware component are gas sensors sensitive to hydrogen sulfide (H2S) and ammonia (NH3). Using MIT App Inventor 2 an Android application is developed to run the hardware component, retrieve the data, preprocess and send it to Google Sheets. Classification model is developed, and data management is carried out in Google Colab and Google Script. Logistic regression method is used to develop classification models from the collected signals. The model classified the samples as "spoiled" and "fresh" based on the gas concentrations. The Nessler solution is used to determine the actual spoilage state. Ground beef samples stored in the refrigerator and at room temperature are used to obtain spoiled and fresh samples to develop a logistic regression model. A total of 36 samples are used to develop model. Another set of 24 samples is used to test model and prototype device performance. It is observed that all samples used in the testing phase were classified correctly. The cost of the system has been determined as approximately $100 considering January 2021 exchange rates.https://dergipark.org.tr/tr/download/article-file/2450490food safetyartificial intelligencemachine learninglogistic regressionelectronic nosegıda güvenliğiyapay zekamakine öğrenmesilojistik regresyonelektronik burun |
| spellingShingle | Simge Özalp Kemal Eren Kızıl DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF Uludağ University Journal of The Faculty of Engineering food safety artificial intelligence machine learning logistic regression electronic nose gıda güvenliği yapay zeka makine öğrenmesi lojistik regresyon elektronik burun |
| title | DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF |
| title_full | DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF |
| title_fullStr | DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF |
| title_full_unstemmed | DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF |
| title_short | DEVELOPING A LOW COST ELECTRONIC NOSE FOR SPOILAGE ANALYSIS OF GROUND BEEF |
| title_sort | developing a low cost electronic nose for spoilage analysis of ground beef |
| topic | food safety artificial intelligence machine learning logistic regression electronic nose gıda güvenliği yapay zeka makine öğrenmesi lojistik regresyon elektronik burun |
| url | https://dergipark.org.tr/tr/download/article-file/2450490 |
| work_keys_str_mv | AT simgeozalp developingalowcostelectronicnoseforspoilageanalysisofgroundbeef AT kemalerenkızıl developingalowcostelectronicnoseforspoilageanalysisofgroundbeef |