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Sensor Array Optimization and Determination of Rhyzopertha dominica Infestation in Wheat Using Hybrid Neuro-fuzzy Assisted Electronic Nose Analysis

Abstract

High grain moisture and temperature provide favorable conditions for stored-grain insect reproduction and survival, which is a major threat in warmer regions. The lesser grain borer (Rhyzopertha dominica), a cosmopolitan insect attacks a wide variety of stored wheat causes serious quality and quantitative loss. Wheat grains artificially infested with R. dominica with various degrees of infestation and stored up to four different storage periods were evaluated by electronic nose (E-nose) on the basis of quality changes due to chemical inversions. The E-nose consists of 18 metal oxide semiconductor (MOS) sensors and the resistance of all the sensors were changes in response to the volatile organic compounds generated from the insect infested wheat grains. Hybrid adapted neuro-fuzzy interference system models (ANFIS) were used to optimize the sensor array detecting infestation and to predict the number of insects, uric acid and protein content. The ANFIS models were the best fit, and the sensor responses were fitted closely to predict the number of insects (R=0.999), uric acid (R=0.985) and protein content (R2=0.973). The classification of the infested wheat grain samples from the non-infested samples were done effectively by Principal component analysis (PCA). The classification performance was weighing up by switching off the nonsignificant sensors. Discrimination of insect infestation by E-nose analysis facilitate industries, warehouses, and exporting agencies to determine the quality of the stored wheat rapidly and systematically throughout the storage period.

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Publication details

The article was received on 01 Sep 2018, accepted on 30 Oct 2018 and first published on 02 Nov 2018


Article type: Paper
DOI: 10.1039/C8AY01921E
Citation: Anal. Methods, 2018, Accepted Manuscript
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    Sensor Array Optimization and Determination of Rhyzopertha dominica Infestation in Wheat Using Hybrid Neuro-fuzzy Assisted Electronic Nose Analysis

    G. Mishra, S. Srivastava, B. K. Panda and H. N. Mishra, Anal. Methods, 2018, Accepted Manuscript , DOI: 10.1039/C8AY01921E

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