Detection & Classification of Electronic Nose System

2021 ◽  
Author(s):  
V J Pandurangi ◽  
Manjunath Managuli ◽  
Sudha Salakhe ◽  
Sadhana Bangarshetti ◽  
Pavan N. Kunchur
Keyword(s):  
Electronic Nose ◽  
Electronic Nose System

scholarly journals Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

2020 ◽  
Vol 66 (No. 3) ◽  
pp. 97-103
Author(s):  
Farel Ahadyatulakbar Aditama ◽  
Lalu Zulfikri ◽  
Laili Mardiana ◽  
Tri Mulyaningsih ◽  
Nurul Qomariyah ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Poor Quality ◽  
Backpropagation Algorithm ◽  
The Poor ◽  
Quality Classification ◽  
Electronic Nose System ◽  
System Prototype

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality agarwood has an output of [–1 1].

Portable Electronic Nose System for Aroma Classification of Black Tea

2008 ◽  
Author(s):  
Santi Sankar Chowdhury ◽  
Bipan Tudu ◽  
Rajib Bandyopadhyay ◽  
Nabarun Bhattacharyya
Keyword(s):  
Electronic Nose ◽  
Black Tea ◽  
Electronic Nose System

Quality Evaluation of Dairy Products Based on Electronic Nose

2013 ◽  
Vol 475-476 ◽  
pp. 524-527
Author(s):  
Xiu Ying Ma ◽  
Yun Xiang Liu ◽  
Wan Jun Yu
Keyword(s):  
Quality Control ◽  
Signal Processing ◽  
Electronic Nose ◽  
Dairy Products ◽  
Quality Evaluation ◽  
Flavor Quality ◽  
Signal Processing Methods ◽  
Electronic Nose System

The construction of Electronic Nose system and associated signal processing methods were introduced .Then special references to applications to dairy products, such as the classification of different milk, the milk with different shelf-lives, flavor quality evaluation, antibiotics resedues detection and quality control were discussed. The results show that the quality of dairy products can be evaluated effectively using Electronic Nose system. The development trends of Electronic Nose are presented.

scholarly journals Electronic-Nose System for Classification of Fruits and Freshness Measurement using K-NN Algorithm

2019 ◽  
Vol 8 (6S4) ◽  
pp. 641-644 ◽  
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Fruit Juice ◽  
Ph Value ◽  
Manufacturing Industries ◽  
Sensor Data ◽  
Food Manufacturing ◽  
Different Types ◽  
Electronic Nose System

The main objective of this work is to design electronic nose system (E-Nose) which detects the odour and freshness of different fruits such as mango, pineapple, orange which are mainly used in food manufacturing industries. E-Nose system consists of sensor array of two with each sensor respond to different types of odours. These sensor data is analyzed with the K-nearest neighbour algorithm (K-NN Algorithm) using MATLAB for identification of different fruits. Freshness of fruit juice is determined by the measurement of pH value of juice by using pH electrode

scholarly journals Electronic nose system for rancidity and insect monitoring of brown rice

2020 ◽  
Vol 187 ◽  
pp. 04015
Author(s):  
Natawut Neamsorn ◽  
Viboon Changrue ◽  
Yaowalak Chanbang ◽  
Kaewalin Kunasakdakul ◽  
Parichat Theanjumpol ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Brown Rice ◽  
Processing System ◽  
Classification Model ◽  
System Response ◽  
Sensors Array ◽  
Insect Monitoring ◽  
Data Acquisition And Processing ◽  
Electronic Nose System

Electronic nose system was designed and fabricated for classification of rancidity and pest damages in brown rice. The electronic nose system was included gas handling system, sensors array and data acquisition and processing system. Response signal from sensors array was recorded and processed. The results showed that the E-nose could classify normal and rancid brown rice (KDML105) and the classification model had Rv2 = 0.92 and SEP = 0.14. The model also gave satisfactory result for classification of brown rice which was damaged by insects (Tribolium castaneum) with Rv2 =0.98 and SEP = 0.06. It was possible to use electronic nose as the quality monitoring system during storage of KDML105 brown rice.

scholarly journals Development of a Portable Electronic Nose System for the Detection and Classification of Fruity Odors

Sensors ◽  
2010 ◽  
Vol 10 (10) ◽  
pp. 9179-9193 ◽  
Author(s):  
Kea-Tiong Tang ◽  
Shih-Wen Chiu ◽  
Chih-Heng Pan ◽  
Hung-Yi Hsieh ◽  
Yao-Sheng Liang ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Electronic Nose System

scholarly journals Non-Invasive Method to Detect Infection with Mycobacterium tuberculosis Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 584
Author(s):  
Kelvin de Jesús Beleño-Sáenz ◽  
Juan Martín Cáceres-Tarazona ◽  
Pauline Nol ◽  
Aylen Lisset Jaimes-Mogollón ◽  
Oscar Eduardo Gualdrón-Guerrero ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Wild Boar ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Bovine Tuberculosis ◽  
Human Beings ◽  
Non Invasive ◽  
Testing Phase ◽  
Volatile Organic ◽  
Electronic Nose System

More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12–24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations.

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