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A framework for analysing E-Nose data based on fuzzy set multiple linear regression: Paddy quality assessment.

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Title: A framework for analysing E-Nose data based on fuzzy set multiple linear regression: Paddy quality assessment.
Authors: Baskar, Chanthini1 chanthini@sastra.ac.in, Nesakumar, Noel2 noelbeats@gmail.com, Balaguru Rayappan, John Bosco3,4 rjbosco@ece.sastra.edu, Doraipandian, Manivannan1 dmv@cse.sastra.edu
Source: Sensors & Actuators A: Physical. Nov2017, Vol. 267, p200-209. 10p.
Subjects: Gas detectors, Electronic noses, Fuzzy sets, Multiple regression analysis, Metal oxide semiconductors
Abstract: Paddy is one of the important long stored grains. It is usually stored in granaries to ensure continuous supply throughout the year, which can be subjected to deterioration due to various environmental conditions. Electronic nose (E-Nose) is used for non-destructive rapid in-situ detection of paddy for qualitative and quantitative assessment. Further, soft computing techniques are a promising solution for data analysis to enhance prediction accuracy. In this work, paddy quality assessment has been carried out using an E-Nose. Six different metal oxide semiconductor gas sensors are used for detecting simple and assorted volatile organic compounds evolving from paddy when stored over a period of time at different conditions. A Fuzzy Set based Multiple Linear Regression (FSMLR) has been proposed and implemented for paddy quality assessment. In the first stage, four fuzzified sensor data are given as input for assessment. While in the second stage, piecewise multiple linear equations were used for quality assessment and defuzzification. Quantitative assessment was carried out for nineteen multiple linear regression models built using fuzzy set theory. The proposed model achieves better performance metrics in terms of low root mean square error for cross validation and relative prediction error (RMSECV <0.028, RPE <1 × 10 −4 ) for all the models. Further, adjusted regression coefficient (R 2 = 0.99) confirms the good fit and Karl-Pearson coefficient of 0.99 confirms the prediction accuracy of the proposed model. This model can be embedded with sensor nodes for online monitoring of paddy granaries. [ABSTRACT FROM AUTHOR]
Copyright of Sensors & Actuators A: Physical is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Paddy is one of the important long stored grains. It is usually stored in granaries to ensure continuous supply throughout the year, which can be subjected to deterioration due to various environmental conditions. Electronic nose (E-Nose) is used for non-destructive rapid in-situ detection of paddy for qualitative and quantitative assessment. Further, soft computing techniques are a promising solution for data analysis to enhance prediction accuracy. In this work, paddy quality assessment has been carried out using an E-Nose. Six different metal oxide semiconductor gas sensors are used for detecting simple and assorted volatile organic compounds evolving from paddy when stored over a period of time at different conditions. A Fuzzy Set based Multiple Linear Regression (FSMLR) has been proposed and implemented for paddy quality assessment. In the first stage, four fuzzified sensor data are given as input for assessment. While in the second stage, piecewise multiple linear equations were used for quality assessment and defuzzification. Quantitative assessment was carried out for nineteen multiple linear regression models built using fuzzy set theory. The proposed model achieves better performance metrics in terms of low root mean square error for cross validation and relative prediction error (RMSECV &lt;0.028, RPE &lt;1 &#215; 10 −4 ) for all the models. Further, adjusted regression coefficient (R 2 = 0.99) confirms the good fit and Karl-Pearson coefficient of 0.99 confirms the prediction accuracy of the proposed model. This model can be embedded with sensor nodes for online monitoring of paddy granaries. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Sensors &amp; Actuators A: Physical is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.sna.2017.10.020
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 200
    Subjects:
      – SubjectFull: Gas detectors
        Type: general
      – SubjectFull: Electronic noses
        Type: general
      – SubjectFull: Fuzzy sets
        Type: general
      – SubjectFull: Multiple regression analysis
        Type: general
      – SubjectFull: Metal oxide semiconductors
        Type: general
    Titles:
      – TitleFull: A framework for analysing E-Nose data based on fuzzy set multiple linear regression: Paddy quality assessment.
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            NameFull: Baskar, Chanthini
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            NameFull: Nesakumar, Noel
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            NameFull: Balaguru Rayappan, John Bosco
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            NameFull: Doraipandian, Manivannan
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              M: 11
              Text: Nov2017
              Type: published
              Y: 2017
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              Value: 267
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            – TitleFull: Sensors & Actuators A: Physical
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