Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose
Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose
复制标题
基于电子鼻的柑橘类橘小实蝇（Hendel）侵染快速检测与分类
DOI：
10.1016/j.postharvbio.2018.09.017
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发表时间：
2019-01-01
影响因子：
7
通讯作者：
Peng, Hailong
中科院分区：
文献类型：
--
作者：
Wen, Tao;Zheng, Lizhang;Peng, Hailong
利用自行研制的扫频电子鼻系统（SENS）对柑橘果实中桔小实蝇（Bactrocera dorsalis（Hendel））的早期侵染进行检测。应用主成分分析（PCA）和线性判别分析（LDA）对不同处理（入侵期和潜伏期）的柑橘果实进行了分析。结果表明，SENS可以成功地检测到早期感染的B。柑橘类水果的背。主成分分析和线性判别分析可以有效地区分柑橘果实中不同的处理类型。同时，LDA模型能较好地识别处理期内柑橘果实的具体侵染时间，正确识别率为98.21%。重要的是，优化的传感器阵列实现了更好的性能，在分类和歧视比非优化。该研究表明，电子鼻技术在市场条件下用于柑橘采后虫害现场检测的潜在可行性。
A sweeping electronic nose system (SENS) was self-developed to detect the presence of early infestation by Bactrocera dorsalis (Hendel) in citrus fruits. Principal component analysis (PCA) and linear discriminate analysis (LDA) were applied to analyze citrus fruits that were subjected to different types of treatments (invasion and incubation stage) caused infestation. The results indicated that the SENS could successfully detect the presence of early infestation by B. dorsalis in citrus fruits. The different types of treatments in citrus fruits could be effectively classified by PCA and LDA, respectively. Meanwhile, the specific infestation time of citrus fruits within treatment stage could be satisfactorily identified by LDA model with correct recognition rate of 98.21%. Importantly, an optimized sensor array achieved better performance in classification and discrimination than that of the non-optimized. This study showed the potential feasibility of the electronic nose technology for in-filed detection of postharvest pest infestation citrus fruits under market conditions.