Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

Sanaeifar, Alireza; Mohtasebi, Seyed Saeid; Ghasemi‐Varnamkhasti, Mahdi; Ahmadi, Hamed; Lozano, Jesús

doi:10.17221/113/2014-cjfs

Cited by 51 publications

(37 citation statements)

References 42 publications

(34 reference statements)

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“…The sensor has a wide detecting scope, stability and long life, fast response and high sensitivity, and it has simple drive circuit which prove to be useful in the detection of air pollutants [11].…”

Section: Mq-135 Gas Sensorsmentioning

confidence: 99%

Design and development of air quality management devices with sensors and web of things

Singh¹,

Kumar²,

Singh³

et al. 2018

IJET

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With the rapid increase in urbanization, there is a huge and urgent need of controlling the air quality so that we can breathe freely. We need oxygen to live and sustain life and for that controlling the environment is must otherwise it would create a great damage. According to a survey, pollution is claiming one life every 23 seconds in India and the figure can go to an extent of 1 billion people dying due to air pollution by 2030. If there would be something free on earth then it would be just Carbon. The earliest step we can take towards this cause is the detection pollutants present in the atmosphere after which the action can be taken against these pollutants. The detection can be done by the use of various sensors like MQ-2, MQ-135, DHT-11, PM10/PM2.5, AQM65 etc. and then obtaining the real-time data to the local servers by the means of IoT. The pollutants can be easily monitored when connected through the internet and local servers. The sensors will work upon a micro-controller which is used to store the data and transmission of data to the servers. Afterwards, the data can be easily accessible on the application developed.

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Section: Mq-135 Gas Sensorsmentioning

confidence: 99%

Design and development of air quality management devices with sensors and web of things

Singh¹,

Kumar²,

Singh³

et al. 2018

IJET

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“…Right after that, a new measurement is started as the concentration phase could start. On the computer screen, the experimental data was displayed in real-time and saved as text files on a disk for data processing [22].…”

Section: B Experimental Set-upmentioning

confidence: 99%

Application of an electronic nose system coupled with artificial neural network for classification of banana samples during shelf-life process

Sanaeifar

Mohtasebi

Ghasemi-Varnamkhasti

et al. 2014

2014 International Conference on Control, Decision and Information Technologies (CoDIT)

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In this research, an electronic nose (e-nose) system was used to discriminate the volatile odors produced by banana during shelf-life process. A measurement system, equipped with six metal oxide semiconductor (MOS) sensors, was used to generate a recognition pattern of the volatile compounds of the banana samples. For pattern classification on data obtained from the sensor array of the electronic nose system, back-propagation multilayer perceptron (BP-MLP) neural network was used. By using BP-MLP technique, 97.33 and 94.44% classification successes were achieved for ripening and senescence period of banana respectively. Sensor array ability in classification of shelflife stages using support vector machines (SVM) analysis was investigated which leaded to develop the application of a specific e-nose system by using the most effective sensors or ignoring the redundant sensors. According to the results, it is concluded that the electronic nose could be a useful tool for discriminating between shelf-life stages of banana.

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“…The quality control and the acceptance of consumers need an optimum ripening stage for bananas [2,3]. In the last decades, many types of research on the ripening assessment of banana were conducted.…”

Section: Introductionmentioning

confidence: 99%

Deep indicator for fine-grained classification of banana’s ripening stages

Zhang

Lian

Fan

et al. 2018

J Image Video Proc.

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Determining banana's ripening stages is becoming an essential requirement for standardizing the quality of commercial bananas. In this paper, we propose a novel convolutional neural network architecture which is designed specifically for the fine-grained classification of banana's ripening stages. It learns a set of fine-grained image features based on a data-driven mechanism and offers a deep indicator of banana's ripening stage. The resulted indicator can help to differentiate the subtle differences among subordinate classes of bananas in ripening state. Experimental results from 17,312 images of bananas in different ripening stages show that our deep indicator achieves an accuracy superior significantly to state-of-the-art computer vision-based systems both in rough-and fine-grained classification of ripening stages no matter the bananas bear or not severe defects.

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Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

Cited by 51 publications

References 42 publications

Design and development of air quality management devices with sensors and web of things

Design and development of air quality management devices with sensors and web of things

Application of an electronic nose system coupled with artificial neural network for classification of banana samples during shelf-life process

Deep indicator for fine-grained classification of banana’s ripening stages

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