Abstract: In order to accurately detect citrus infected by Bactrocera dorsalis (Hendel) using visible/nearinfrared spectroscopy, this study designed a multipath nondestructive detection grading system for citrus infected by Bactrocera dorsalis (Hendel) to address the unknown location of infection. Semitransmissive spectral information from four detection paths was collected and partial least squares discriminant analysis was used to establish and compare classification models for a single detection path and a combination of four detection paths. The results showed that the model combining all detection paths achieved better classification results. Using 47 feature wavelengths selected by the competitive adaptive reweighted sampling method to build a model, the accuracy and specificity of the best prediction set among the four detection paths reached 93.5% and 95.2%, respectively. The PLSDA hybrid classification model established by using citrus samples from four detection paths, combined with the CARS algorithm for effective feature wavelength variable selection, can improve the accuracy of Bactrocera dorsalis (Hendel) infected citrus classification model and accurately classify Bactrocera dorsalis (Hendel) infected citrus. The research results can provide a reference for online detection of citrus infestation by Bactrocera dorsalis (Hendel).

Key words: citrus, non-destructive detection, Bactrocera dorsalis (Hendel), visble/near-infrared spectroscopy, partial least squares discriminant analysis

摘要： 为实现可见/近红外光谱对橘小实蝇侵染柑橘的准确检测。针对侵染位置的未知问题，设计一套橘小实蝇侵染柑橘多光路无损检测分级系统。采集四个检测光路的半透射光谱信息，利用偏最小二乘判别法建立并比较单一检测光路和混合四个检测光路的分类模型。结果显示，混合所有检测光路的模型取得较优的分类结果。利用竞争性自适应重加权采样法筛选的47个特征波长变量建模，四个检测光路中分类效果最佳地预测集的准确率和特异性分别达到93.5%和95.2%。利用四个检测光路的柑橘样本建立的PLSDA混合分类模型，结合CARS算法进行有效特征波长变量筛选，可提高橘小实蝇侵染柑橘分类模型的精度，实现橘小实蝇侵染柑橘的准确分类。研究结果可为在线检测橘小实蝇侵染柑橘提供参考。

关键词: 柑橘, 无损检测, 橘小实蝇, 可见/近红外光谱, 偏最小二乘判别