Gas sensor array for blueberry fruit disease detection and classification
電子鼻技術用于藍莓病害檢測和分類Changying Li a,∗, Gerard W. Krewerb, Pingsheng Ji c, Harald Schermd, Stanley J. Kayse
a University of Georgia, Department of Biological and Agricultural Engineering, 2329 Rainwater Road, Tifton, GA 31794, USA
b University of Georgia, Department of Horticulture, 4604 Research Way, Tifton, GA 31794, USA
c University of Georgia, Department of Plant Pathology, 115 Coastal Way, Tifton, GA 31794, USA
d University of Georgia, Department of Plant Pathology, 2311 Miller Plant Sciences Bldg., Athens, GA 30602, USA
e University of Georgia, Department of Horticulture, 1111 Miller Plant Sciences Bldg., Athens, GA 30602, USA
a b s t r a c t
A conducting polymer gas sensor array (electronic nose Cyranose 320) 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. TenaxTM 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.
利用導電聚合物氣體傳感器陣列(電子鼻Cyranose320)對藍莓果實采后常見的三種病害進行了檢測和分類：灰霉病、炭疽病、交鏈孢霉引起的炭疽病、成熟的兔眼藍莓(牛痘)樣品的交鏈孢腐爛病。Um Virgatum。Brightwell)分別接種三種病原體中的一種或不接種，在兩個單獨的實驗中，接種后6-10 d用氣體傳感器陣列評估從水果中釋放的揮發物。揮發性成分分析顯示四個不同的組分與四種接種處理相對應。Manova對個體評估日或綜合數據的資料進行分析，證實四種治療方法有顯著差異(p<0.0001)。層次聚類分析顯示兩個超級聚類，即控制聚類(未接種的果實)與病原聚類(接種的果實)。在病原菌群中，灰霉病和交鏈孢桿菌侵染的果實比灰霉病侵染的果實更為相似。線性貝葉斯分類器對實驗1的數據實現了90%的整體正確分類。采用短程熱解吸和氣相色譜-質譜定量法對四組漿果揮發物的Tenaxtm捕集進行了表征。6種化合物[苯乙烯、1-甲基-2-(1-甲基乙基)苯、桉樹醇、十一烷、5-甲基-2-(1-甲基乙基)-和thujopsene]被鑒定為助于區分由感染引起的水果揮發物差異。建立了一個基于上述化合物相對濃度的典型判別分析模型，并成功地對四類漿果進行了分類。本研究強調了使用電子鼻進行藍莓采后質量評估和真菌病檢測的潛在可行性。
