Feature Extraction of Citrus Juice During Storage for Electronic Nose Based on Cellular Neural Network

Background: The identification of Fructus Crataegi processed products manually is inefficient and unreliable. Therefore, how to identify the Fructus Crataegis processed products efficiently is important. Objective: In order to efficiently identify Fructus Grataegis processed products with different odor characteristics, a new method based on an electronic nose and convolutional neural network is proposed. Methods: First, the original smell of Fructus Grataegis processed products is obtained by using the electronic nose and then preprocessed. Next, feature extraction is carried out on the preprocessed data through convolution pooling layer Results: The experimental results show that the proposed method has higher accuracy for the identification of Fructus Grataegis processed products, and is competitive with other machine learning based methods. Conclusion: The method proposed in this paper is effective for the identification of Fructus Grataegi processed products.

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Evaluation of Semanotus bifasciatus (Motschulsky) Infestation in Platycladus orientalis Plants Using E-Nose and GC-MS

Transactions of the ASABE ◽

10.13031/trans.13145 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1629-1637

Author(s):

Zhenhe Wang ◽

Yubing Sun ◽

Jun Wang ◽

Yongwei Wang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

Electronic Nose ◽

Support Vector ◽

List Type ◽

Linear Discriminant ◽

Platycladus Orientalis ◽

Volatile Organic

HighlightsE-nose was employed for evaluation of Semanotus bifasciatus infestation based on four time-domain features.Plant VOCs were analyzed by GC-MS, and the results proved the feasibility of E-nose detection.PNN, BPNN, SVM, and PLSR were introduced to classify and predict Semanotus bifasciatus infestation numbers.Abstract. Trunk-boring insects such as Semanotus bifasciatus (Motschulsky) are difficult to detect because the larvae are hidden inside the trunks. In this study, the variation of volatile organic compounds (VOCs) in Platycladus orientalis after S. bifasciatus infestation was evaluated using an electronic nose (E-nose). VOCs from sample plants were observed with gas chromatography - mass spectrometry (GC-MS), and the results indicated that uninfected and infected groups differed both qualitatively and quantitatively, which proves the feasibility of E-nose evaluation. To extract features of the E-nose response signals, four feature extraction methods were applied, and their performances were compared based on linear discriminant analysis (LDA). Three classification models, including back-propagation neural network (BPNN), support vector machine (SVM), and probabilistic neural network (PNN), were established to identify the severity of infestation based on the optimal feature extraction method (75th second value). The classification results of BPNN, PNN, and SVM based on the calibration set were 96.43%, 91.07%, and 100%, respectively, and the results based on the validation set were 91.67%, 91.67%, and 100%, respectively. In addition, partial least squares regression (PLSR) and BPNN were used to predict the larvae density and achieved highly reliable results. It can be concluded that combining E-nose with GC-MS is a potential technique for evaluating trunk-borer infestation and can be used for pest management. Keywords: Electronic nose, Feature extraction, Pest evaluation, Semanotus bifasciatus, Volatile organic compounds.

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Hand veins feature extraction based on morphology and Cellular Neural Network

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5622355 ◽

2010 ◽

Author(s):

Jinpeng Deng ◽

Yanzhi Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Cellular Neural Network

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The brain mimicking Visual Attention Engine: An 80×60 digital Cellular Neural Network for rapid global feature extraction

2008 IEEE Symposium on VLSI Circuits ◽

10.1109/vlsic.2008.4585938 ◽

2008 ◽

Cited By ~ 4

Author(s):

Seungjin Lee ◽

Kwanho Kim ◽

Minsu Kim ◽

Joo-Young Kim ◽

Hoi-Jun Yoo

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Visual Attention ◽

Cellular Neural Network ◽

Global Feature ◽

Digital Cellular ◽

The Brain

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Identifikasi Tahu Berformalin dengan Electronic Nose Menggunakan Jaringan Syaraf Tiruan Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.15330 ◽

2016 ◽

Vol 6 (2) ◽

pp. 211

Author(s):

Wida Astuti ◽

Danang Lenono ◽

Faizah Faizah

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Volatile Compounds ◽

Electronic Nose ◽

Back Propagation ◽

Extraction Process ◽

Back Propagation Neural Network ◽

Differential Method ◽

Gas Sensor Array ◽

Network Training

During this time to identify pure and formalin tofu based on color and aroma involving human taster. But this tofu tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of tofu is determined by volatile compounds such as heksanal, ethanol, and 1-hexanol, while aroma of formalin tofu is determined by volatile compounds such as OH, CO, and hydrocarbon. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the back propagation neural network training to obtain optimal parameters. The parameters have been optimized is then tested on a random tofus. Based on test results, ANN-BP can identify samples with 100% accuracy rate so that the identification of a pure tofu and tofu formalin with electronic nose using back propagation neural network analysis has been successfully carried out.

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