Apiculture Division, Faculty of Animal Bioengineering, University of Warmia and Mazury in Olsztyn, Sloneczna 48, 10-957 Olsztyn, Poland.
Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Sloneczna 48, 10-957 Olsztyn, Poland.
Sensors (Basel). 2020 Jul 19;20(14):4014. doi: 10.3390/s20144014.
Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class-empty chamber (13 objects), 2nd class-fragments of combs containing brood sick with varroosis (19 objects), and 3rd class-fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)-with default settings in RSES tool-was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments-in multiple Monte Carlo Cross Validation model-using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context.
瓦螨病是一种危险且难以诊断的疾病，它正在使蜜蜂群大量减少。这项研究旨在探讨电子鼻是否可以通过检查密封的幼虫样本，成为有效检测这种疾病的工具。在该研究中，测试了来自 FIGARO 的具有六个半导体气体传感器 TGS 823、TGS 826、TGS 832、TGS 2600、TGS 2602 和 TGS 2603 的多传感器气体传感器信号记录器原型。共有 42 个属于 3 个类别的对象进行了测试：第 1 类-空室（13 个对象）、第 2 类-含有患瓦螨病幼虫的巢脾碎片（19 个对象）和第 3 类-含有健康密封幼虫的巢脾碎片（10 个对象）。单个对象的检查持续 20 分钟，包括暴露阶段（10 分钟）和传感器再生阶段（10 分钟）。k-最近邻算法（kNN）-在 RSES 工具中使用默认设置-被成功用作基本分类器。分析的基础是经过基线校正的 270 秒传感器读数值。多传感器 MCA-8 气体传感器信号记录器已被证明是区分患有瓦螨病的幼虫和健康幼虫的有效工具。五次交叉验证 2 测试（5×CV2 测试）显示整体准确率为 0.832，平衡准确率为 0.834。患病幼虫类的阳性率为 0.92。为了检查在检查背景下基线校正的整体有效性，我们在多个蒙特卡罗交叉验证模型中进行了额外的系列实验，使用了一组具有不同指标的分类器。我们测试了几种 kNN 方法、朴素贝叶斯分类器和加权投票分类器的变体。我们使用统计检验验证了基线校正显著提高分类水平的假设。我们还证实，在检查的背景下，只使用 TGS2603 传感器就足够了。
