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

Cao, Huaisheng; Jia, Pengfei; Xu, Duo; Jiang, Yingjie; Qiao, Siqi

doi:10.1109/jsen.2019.2961135

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…Yoo et al proposed an information encoding and decoding method combined with CNN, which was applied to actual engineering gas detection and achieved good results [21]. Cao et al proposed an improved cellular neural network to achieve feature extraction and recognition of different liquor gas information [22]. Shi et al proposed a lightweight interleaved residual dense network for efficient classification of industrial polypropylene gas [23].…”

Section: Jinst 17 P08016mentioning

confidence: 99%

A deep learning method combined with electronic nose to identify the rice origin

Yan

Liu

2022

J. Inst.

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The rice quality is directly affected by the natural environment in which it is grown. It is common to pass off poor quality as good quality rice sold in rice markets. Therefore, an effective rice quality detection method should be proposed. This work proposes a deep learning method combined with an electronic nose (e-nose) to realize the classification of rice origin, and then realize the detection of rice quality. Firstly, channel-spatial collaborative attention (CSCA) is proposed to adaptively focus on key features of gas information. Secondly, three shallow convolutional neural network (CNN) structures are introduced to achieve gas information classification. Finally, CSCA and three CNN structures are combined to construct a classification network for rice gas recognition. The results show that in the comparison of multi-structure models, LeNet5-CSCA has the best classification performance, with a classification accuracy of 97.70%, a precision of 97.45%, a recall of 97.67%, and a F1-score of 97.56%.

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Section: Jinst 17 P08016mentioning

confidence: 99%

A deep learning method combined with electronic nose to identify the rice origin

Yan

Liu

2022

J. Inst.

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“…RTIFICIAL Olfaction (AO) refers to identifying and discriminating different odors using electronic instruments like an Electronic Nose System (ENS). The ENS has a broad range of applications including the food industry [1]- [3], health sector [4], [5], environment monitoring [6], oil and gas industry [7], and security purposes [8], [9], among others. An ENS is comprised of two units, a sensing unit and a recognition unit.…”

Section: Introductionmentioning

confidence: 99%

Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose

et al. 2021

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Long term sensors drift is a challenging problem to solve for instruments like an Electronic Nose System (ENS). These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. Moreover, the response of an electronic nose system depends on the variable response of the sensors and a delay is expected in reaching a steady state by the sensors. In this paper, these two problems of 'sensors long term drift' and 'delayed response' are solved simultaneously to propose a robust and fast electronic nose system, with following merits: (i) only initial transient state features are used in the proposed system without waiting for the sensors to reach a steady state, (ii) a modified boxplot approach is used to handle noisy/drifted data points as a preprocessing step before the classification setup, (iii) a heuristic tree classification approach with optimized transient features is proposed, (iv) the proposed approach only relies on adapted ML methods contrary to the traditional approaches like system recalibration or sensors replacement for handling sensors drift, and (v) the proposed ML model does not require any target domain data and uses only the source domain data for learning the classifier, opposed to the other ML solutions available in the existing literature. The proposed method is tested using a large scale gas sensors drift benchmark dataset available freely on UCI Machine Learning repository and is found better than the existing state-of-the art approaches with an overall accuracy of 87.34%.

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“…In this article, we use the electronic nose (E-nose) for the quality identification of juice. At present, the feasibility of the identification of juice quality by the E-nose has been verified [1,2]. E-nose is an intelligent system composed of sensor arrays and pattern recognition algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Classification for Electronic Nose Based on Broad Learning System

Wang¹,

Peng²,

Cui³

et al. 2020

Fuzzy Systems and Data Mining VI

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The odor of citrus juice changes during the storing process. We use an electronic nose (E-nose) to detect the volatile odors released by citrus juice and use the detect results to classify citrus juices from different storage periods. In this article, a novel classifier of E-nose, namely broad learning system (BLS) is introduced. BLS is different from traditional classifier. It has a simple network model, which can greatly reduce the training time of the model. BLS can effectively combine feature extraction and classification recognition to make the model more efficient. We apply BLS to the analysis of valencia citrus juice data. The experimental results show that BLS can effectively identify the current stage of the stored valencia citrus juice. Compared with traditional classifier such as radical basis function neural network (RBFNN) and linear discriminant analysis (LDA), the results show that BLS has better performance for the storage period classification of valencia citrus juice.

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

Cited by 15 publications

References 36 publications

A deep learning method combined with electronic nose to identify the rice origin

A deep learning method combined with electronic nose to identify the rice origin

Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose

A Classification for Electronic Nose Based on Broad Learning System

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