Gas sensor array for blueberry fruit disease detection and classification |
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| Authors: | Changying Li Gerard W Krewer Pingsheng Ji Harald Scherm Stanley J Kays |
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| Institution: | 1. University of Georgia, Department of Biological and Agricultural Engineering, 2329 Rainwater Road, Tifton, GA 31794, USA;2. University of Georgia, Department of Horticulture, 4604 Research Way, Tifton, GA 31794, USA;3. University of Georgia, Department of Plant Pathology, 115 Coastal Way, Tifton, GA 31794, USA;4. University of Georgia, Department of Plant Pathology, 2311 Miller Plant Sciences Bldg., Athens, GA 30602, USA;5. University of Georgia, Department of Horticulture, 1111 Miller Plant Sciences Bldg., Athens, GA 30602, USA;1. Department of Agricultural Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Indonesia;2. Department of Electrical Engineering, Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember, Indonesia;1. Key Laboratory for Forest Resources Conservation and Utilisation in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, PR China;2. Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, PR China |
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| Abstract: | A conducting polymer gas sensor array (electronic nose) was evaluated for detecting and classifying three common postharvest diseases of blueberry fruit: gray mold caused by Botrytis cinerea, anthracnose caused by Colletotrichum gloeosporioides, and Alternaria rot caused by Alternaria sp. Samples of ripe rabbiteye blueberries (Vaccinium virgatum cv. Brightwell) were inoculated individually with one of the three pathogens or left non-inoculated, and volatiles emanating from the fruit were assessed using the gas sensor array 6–10 d after inoculation in two separate experiments. Principal component analysis of volatile profiles revealed four distinct groups corresponding to the four inoculation treatments. MANOVA, conducted on profiles from individual assessment days or from combined data, confirmed that the four treatments were significantly different (P < 0.0001). A hierarchical cluster analysis indicated two super-clusters, i.e., control cluster (non-inoculated fruit) vs. pathogen cluster (inoculated fruit). Within the pathogen cluster, fruit infected by B. cinerea and Alternaria sp. were more similar to each other than to fruit infected by C. gloeosporioides. A linear Bayesian classifier achieved 90% overall correct classification for data from experiment 1. Tenax? trapping of volatiles with short-path thermal desorption and quantification by gas chromatography–mass spectrometry was used to characterize volatile compounds emanated from the four groups of berries. Six compounds styrene, 1-methyl-2-(1-methylethyl) benzene, eucalyptol, undecane, 5-methyl-2-(1-methylethyl)-2-cyclohexen-1-one, and thujopsene] were identified as contributing most in distinguishing differences in the volatiles emanating from the fruit due to infection. A canonical discriminant analysis model using the relative concentration of each of these compounds was developed and successfully classified the four categories of berries. This study underscores the potential feasibility of using a gas sensor array for blueberry postharvest quality assessment and fungal disease detection. |
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