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首页> 外文期刊>IEEE Transactions on Instrumentation and Measurement >Detection of Citrus Tristeza Virus in Mandarin Orange Using a Custom-Developed Electronic Nose System
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Detection of Citrus Tristeza Virus in Mandarin Orange Using a Custom-Developed Electronic Nose System

机译:使用定制开发的电子鼻系统检测柑橘橙色柑橘Tristeza病毒

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摘要

Olfaction is one of the primary senses of a living organism and has been used as first aid for inspecting freshness, edibility, and the quality of food. This article addresses a technique where the biological olfaction process has been mimicked by electronic nose (E-Nose) to detect a pathogen named Citrus Tristeza Virus (CTV) in Khasi Mandarin Orange plants. The proposed technique may be used as an alternative to currently used traditional serological or molecular tests, which requires costly and elaborate laboratory infrastructures. In this article, leaves from 62 plants were collected and examined for viral infection using the gold-standard polymerase chain reaction test. A commercial E-Nose system, Alpha MOS Fox 3000, was used for hypothesis testing, resulting in an accuracy of 95.30% using random forest classifier. These results were used to identify the most prominent sensors and their target gases responding to the CTV infection and were considered as a base for sensor selection for custom developing a prototype. The prototype was designed based on simulations for optimum airflow in the sensor chamber. Bootstrap ensemble of k-nearest neighbors and adaptive boost ensemble of decision tree classifier distinguished the data generated from the prototype with an accuracy of 99.36% and 97.58%, respectively. The performance of the classifiers was visualized graphically using dimensionality reduction techniques. These results indicated that the E-Nose technique employed could differentiate the healthy and infected samples and the prototype could serve as an effective first-hand diagnosing tool for large-scale orchards.
机译:嗅觉是生物体的主要感觉之一,并被用作检查新鲜度,可食性和食物质量的急救。本文解决了一种技术,其中通过电子鼻子(E-鼻子)模仿生物嗅觉过程,以检测在Khasi橘子植物中被命名为柑橘Tristeza病毒(CTV)的病原体。该提出的技术可以用作当前使用的传统血清学或分子测试的替代方案,这需要昂贵和精心制作的实验室基础设施。在本文中,收集来自62株植物的叶片,并使用金标准聚合酶链反应试验检查病毒感染。商业电子鼻系统Alpha MOS Fox 3000用于假设检验,使用随机林分类器导致95.30%的精度。这些结果用于识别最突出的传感器及其响应CTV感染的目标气体,并且被认为是用于定制发育原型的传感器选择的基础。基于用于传感器室中的最佳气流的模拟设计了原型。决策树分类器的K-Collect邻居和自适应升压集合的引导集合区分了从原型产生的数据,精度分别为99.36%和97.58%。使用维数减少技术以图形方式可视化分类器的性能。这些结果表明,所采用的电子鼻技术可以区分健康和受感染的样品,原型可以作为大型果园的有效第一手诊断工具。

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