Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors

Aziz  Amar; Rachida  Belloute; Mohammed  Diouri

doi:10.6180/jase.202508_28(8).0007

Portable E-nose for Enhanced Pizza Toppings Recognition using MQ Gas Sensors

Physics

Aziz Amari¹This email address is being protected from spambots. You need JavaScript enabled to view it., Rachida Belloute², and Mohammed Diouri²

¹LCS Laboratory, Physics Department, Faculty of Sciences, Mohammed V University in Rabat, Ibn Battouta Street, Rabat 10000, Morocco

²Biology Department, Faculty of sciences, Moulay Ismaïl University, Zitoune Street, Meknes 11201, Morocco

Received: February 29, 2024
Accepted: August 16, 2024
Publication Date: November 4, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202508_28(8).0007

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.

Keywords: E-nose; Gas sensors; Pizza; Umami; Data analysis; Machine learning; Feature selection; ANOVA; PCA; Cluster analysis; SVMs

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