scholarly journals Rapid evaluation of fresh chicken meat quality by electronic nose

2018 ◽  
Vol 36 (No. 5) ◽  
pp. 420-426 ◽  
Author(s):  
Raudienė Edita ◽  
Gailius Darius ◽  
Rimanté Vinauskienė ◽  
Viktorija Eisinaitė ◽  
Gintautas Balčiūnas ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Electronic Nose ◽  
Chemical Method ◽  
Volatile Fatty Acids ◽  
Chicken Meat ◽  
Sensor Selection ◽  
Rapid Evaluation ◽  
Meat Spoilage ◽  
Chemical Measurements ◽  
Metal Oxide Sensor

A prototype of electronic nose (e-nose) with the gas sensor system for evaluation of fresh chicken meat freshness was developed. In this paper a rapid, simple and not expensive system for fresh chicken meat spoilage detection was investigated that provides objective and reliable results. Quality changes in fresh chicken meat during storage were monitored by the metal oxide sensor (MOS) system and compared with the results of traditional chemical measurements. Gas sensor selection was tested for evaluation of volatile fatty acids (VFA) mainly representing meat spoilage.The study demonstrated that a correlation coefficient (R<sup>2</sup> = 0.89) between e-nose signals and traditional chemical method was high. These results prove that the developed e-nose prototype has a potential for assessing fresh chicken meat freshness and allows discriminating meat into fresh, unsafe and spoiled.

scholarly journals Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 78
Author(s):  
Jianhua Cao ◽  
Tao Liu ◽  
Jianjun Chen ◽  
Tao Yang ◽  
Xiuxiu Zhu ◽  
...  
Keyword(s):  
Active Learning ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Effective Means ◽  
Learning Paradigm ◽  
Class Label ◽  
Learning Framework ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

scholarly journals MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 209
Author(s):  
Davide Marzorati ◽  
Luca Mainardi ◽  
Giulia Sedda ◽  
Roberto Gasparri ◽  
Lorenzo Spaggiari ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Rate ◽  
Mortality Rate ◽  
Early Diagnosis ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

Pd-decorated ZnO nanoflowers as a promising gas sensor for the detection of meat spoilage

2021 ◽  
pp. 131316
Author(s):  
Jennifer Bruce ◽  
Ken Bosnick ◽  
Elham Kamali Heidari
Keyword(s):  
Gas Sensor ◽  
Meat Spoilage

A Room Temperature Hydrocarbon Electronic Nose Gas Sensor Based on Schottky and Heterojunction Diode Structures

2020 ◽  
Vol 41 (1) ◽  
pp. 163-166
Author(s):  
Cao Kun ◽  
Yanling He ◽  
Yongxiang Li ◽  
Alan Ng ◽  
Jerry Yu
Keyword(s):  
Gas Sensor ◽  
Electronic Nose ◽  
Room Temperature ◽  
Heterojunction Diode

scholarly journals Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 251 ◽  
Author(s):  
Grzegorz Łagód ◽  
Sylwia M. Duda ◽  
Dariusz Majerek ◽  
Adriana Szutt ◽  
Agnieszka Dołhańczuk-Śródka
Keyword(s):  
Wastewater Treatment ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Sensor Array ◽  
Treatment Plant ◽  
Full Scale ◽  
Oxide Semiconductor ◽  
Pollution Level ◽  
Gas Sensor Array

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.

Electronic nose system with micro gas sensor array

1996 ◽  
Vol 36 (1-3) ◽  
pp. 338-341 ◽  
Author(s):  
Hyung-Ki Hong ◽  
Hyun Woo Shin ◽  
Dong Hyun Yun ◽  
Seung-Ryeol Kim ◽  
Chul Han Kwon ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Electronic Nose ◽  
Sensor Array ◽  
Gas Sensor Array ◽  
Micro Gas Sensor ◽  
Electronic Nose System
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