Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors
In this paper, a portable electronic nose system was developed and evaluated for its performance in rating pizza toppings, as compared to subjective evaluation of quality. In this study, four pizza-topping types were prepared: P1: 100% minced beef with Edam cheese, P2: 50% minced beef and 50% minced...
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Tamkang University Press
2025-01-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202508-28-08-0007 |
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| author | Aziz Amari Rachida Belloute Mohammed Diouri |
| author_facet | Aziz Amari Rachida Belloute Mohammed Diouri |
| author_sort | Aziz Amari |
| collection | DOAJ |
| description | In this paper, a portable electronic nose system was developed and evaluated for its performance in rating pizza toppings, as compared to subjective evaluation of quality. In this study, four pizza-topping types were prepared: P1: 100% minced beef with Edam cheese, P2: 50% minced beef and 50% minced Kadid (air-dried salted meat) with Edam cheese, P3: 100% minced Kadid with Edam cheese, and P4: 100% minced beef with parmesan cheese. Kadid was similar to plain meat with respect to perception and preference. The experiment was performed on 101 prepared pizza-topping samples. Our study objective was to differentiate between various pizza toppings using the developed portable E-nose. Additionally, we aimed to highlight the impact of
lemon smell as an olfactory disturbance in this differentiation. For this purpose, several procedures for feature selection, machine learning techniques were evaluated. Firstly, a Principal Component Analysis (PCA) showed a modest grouping of pizza toppings except for P4 samples (based on parmesan cheese) which were more distinct from others. By applying One-way ANOVA feature selection before performing PCA, Cluster Analysis (CA) and Support Vector Machines (SVMs), a significant improvement was observed in the identification of the four pizza toppings. Finally, the results from CA reveal that the presence of an olfactory disturbance caused by lemon scent significantly alters the order in which toppings are identified by the portable E-nose, particularly affecting cheese recognition. |
| format | Article |
| id | doaj-art-8adbefdcca814d9588f41a78e34eecb0 |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-8adbefdcca814d9588f41a78e34eecb02025-01-08T05:22:58ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-01-012881689170210.6180/jase.202508_28(8).0007Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas SensorsAziz Amari0Rachida Belloute1Mohammed Diouri2LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed V University in Rabat, Ibn Battouta Street, Rabat 10000, MoroccoBiology Department, Faculty of sciences, Moulay Ismaïl University, Zitoune Street, Meknes 11201, MoroccoBiology Department, Faculty of sciences, Moulay Ismaïl University, Zitoune Street, Meknes 11201, MoroccoIn this paper, a portable electronic nose system was developed and evaluated for its performance in rating pizza toppings, as compared to subjective evaluation of quality. In this study, four pizza-topping types were prepared: P1: 100% minced beef with Edam cheese, P2: 50% minced beef and 50% minced Kadid (air-dried salted meat) with Edam cheese, P3: 100% minced Kadid with Edam cheese, and P4: 100% minced beef with parmesan cheese. Kadid was similar to plain meat with respect to perception and preference. The experiment was performed on 101 prepared pizza-topping samples. Our study objective was to differentiate between various pizza toppings using the developed portable E-nose. Additionally, we aimed to highlight the impact of lemon smell as an olfactory disturbance in this differentiation. For this purpose, several procedures for feature selection, machine learning techniques were evaluated. Firstly, a Principal Component Analysis (PCA) showed a modest grouping of pizza toppings except for P4 samples (based on parmesan cheese) which were more distinct from others. By applying One-way ANOVA feature selection before performing PCA, Cluster Analysis (CA) and Support Vector Machines (SVMs), a significant improvement was observed in the identification of the four pizza toppings. Finally, the results from CA reveal that the presence of an olfactory disturbance caused by lemon scent significantly alters the order in which toppings are identified by the portable E-nose, particularly affecting cheese recognition.http://jase.tku.edu.tw/articles/jase-202508-28-08-0007e-nosegas sensorspizzaumamidata analysismachine learningfeature selectionanovapcacluster analysissvms |
| spellingShingle | Aziz Amari Rachida Belloute Mohammed Diouri Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors Journal of Applied Science and Engineering e-nose gas sensors pizza umami data analysis machine learning feature selection anova pca cluster analysis svms |
| title | Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors |
| title_full | Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors |
| title_fullStr | Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors |
| title_full_unstemmed | Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors |
| title_short | Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors |
| title_sort | portable e nose for enhanced pizza toppings recognition using mq gas sensors |
| topic | e-nose gas sensors pizza umami data analysis machine learning feature selection anova pca cluster analysis svms |
| url | http://jase.tku.edu.tw/articles/jase-202508-28-08-0007 |
| work_keys_str_mv | AT azizamari portableenoseforenhancedpizzatoppingsrecognitionusingmqgassensors AT rachidabelloute portableenoseforenhancedpizzatoppingsrecognitionusingmqgassensors AT mohammeddiouri portableenoseforenhancedpizzatoppingsrecognitionusingmqgassensors |