Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

Estakhroyeh, Hossein Rezaei; Rashedi, Esmat; Mehran, M.

doi:10.1007/s10846-017-0759-3

Cited by 18 publications

(12 citation statements)

References 79 publications

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“…This suggests that the response time and the purification time depend on the odor profile composition and may change with the changing proportions in the profile. Similar studies on the use of metal oxide semiconductor sensors for detection of the odor profile have been conducted by many authors [ 14 ], who have analyzed signals to determine the presence of mold on bread or supplementation additives [ 24 ]. Similarly, Lippolis et al, (2014) [ 21 ] used an electronic nose to classify wheat in terms of fungal infection in the material.…”

Section: Resultsmentioning

confidence: 99%

“…There are hundreds of volatile substances responsible for the composition of fragrance, but usually, only several main compounds present in the largest amounts are the responsible aromas. The so-called electronic nose is used for the analysis of aromas [ 14 ]. The device consists of many chemically sensitive sensors detecting the main volatile compounds.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Supplementation of Flour with Fruit Fiber on the Volatile Compound Profile in Bread

Rusinek

Gawrysiak‐Witulska

Siger

et al. 2021

Sensors

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This paper presents the analyses of the effect of fiber additives on volatile organic compounds in bread. The bread was baked from wheat flour with the addition of 3% of fruit fiber, following common procedures. After baking, volatile organic compounds contained in the control bread and breads supplemented with cranberry, apple, and chokeberry fiber were determined. The SPME/GC-MS technique was used for the identification of the odor profile, and the electronic nose Agrinose (e-nose) was used to assess the intensity of the aroma. The results of the analyses revealed the profile of volatile organic compounds in each experimental variant, which was correlated with responses of the electronic nose. The results indicate that the volatile compound profile depends on the bread additives used and influences the intensity of bread aroma. Moreover, the profile of volatile organic compounds in terms of their amount and type, as well as the intensity of their interaction with the active surface of the electrochemical sensors, was specific exclusively for the additive in each case.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Supplementation of Flour with Fruit Fiber on the Volatile Compound Profile in Bread

Rusinek

Gawrysiak‐Witulska

Siger

et al. 2021

Sensors

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“…These systems were designed, taking inspiration from the anatomy of the biological nose. The odor emission rate is a factor that points to the source of odor emission and shows the amount of odor emitted per unit of time (21). Generating airflow between samples is one of the most popular types of odor handling, and delivery systems called the sample flow system.…”

Section: Odor Management and Delivery Systemmentioning

confidence: 99%

Electronic Nose, Tongue and Eye: Their Usefulness for the Food Industry

Ordoñez-Araque

Rodríguez-Villacres

Urresto-Villegas

2020

Vitae

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Background: The electronic nose, tongue, and eye are futuristic technologies that have been used for many years; they have been gaining market in different types of industries and can increasingly be found in the food area; their function is to determine sensory characteristics (smell, aroma, and flavor) and objective visuals, without the subjectivity that can be represented by sensory analysis by people (the study that can complement the analysis of machines, without being exclusive). Objectives: Find the main generalities of these mechanisms, their sensors, software, mechanism of action, and applications within the food industry. Methods: A search was carried out in the main databases of indexed articles, with terms that allowed collecting the necessary information, and 89 articles were used that met different inclusion criteria. Results: The main outcomes were to understand the operation of each of these technologies, what their main components are, and how they can be linked in the beer, wine, oil, fruit, vegetable, dairy, etc. industry to determine their quality, safety, and fraud. Conclusions: The use of electronic nose, tongue, and eye is found in more food industries every day. Its technology continues to evolve; the future of sensory analysis will undoubtedly apply these mechanisms due to the reliability, speed, and reproducibility of the results.

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“…Therefore, the evaluation of the discriminative characteristics that distinguish different teas is not straightforward. Many studies focused on improving the performance of the E-nose system by selecting sensitive material [ 26 ], optimizing the sensor array [ 27 ], or enhancing the feature extraction methods and algorithms [ 28 , 29 ]. The feature extraction method is a crucial aspect of the optimization methods, and it is important to extract useful information from the sensor signals [ 30 ] To the best of our knowledge, most researchers employing E-noses used steady-state or transient-state features (maximum values [ 31 , 32 ], stable values [ 33 , 34 ], integrals [ 35 , 36 ], and derivatives [ 36 , 37 ]).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

2021

Foods

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Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

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Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

Cited by 18 publications

References 79 publications

Effect of Supplementation of Flour with Fruit Fiber on the Volatile Compound Profile in Bread

Effect of Supplementation of Flour with Fruit Fiber on the Volatile Compound Profile in Bread

Electronic Nose, Tongue and Eye: Their Usefulness for the Food Industry

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

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