Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Queensland, Australia.
Parasit Vectors. 2020 Mar 30;13(1):160. doi: 10.1186/s13071-020-04031-3.
Practical, field-ready age-grading tools for mosquito vectors of disease are urgently needed because of the impact that daily survival has on vectorial capacity. Previous studies have shown that near-infrared spectroscopy (NIRS), in combination with chemometrics and predictive modeling, can forecast the age of laboratory-reared mosquitoes with moderate to high accuracy. It remains unclear whether the technique has utility for identifying shifts in the age structure of wild-caught mosquitoes. Here we investigate whether models derived from the laboratory strain of mosquitoes can be used to predict the age of mosquitoes grown from pupae collected in the field.
NIRS data from adult female Aedes albopictus mosquitoes reared in the laboratory (2, 5, 8, 12 and 15 days-old) were analysed against spectra from mosquitoes emerging from wild-caught pupae (1, 7 and 14 days-old). Different partial least squares (PLS) regression methods trained on spectra from laboratory mosquitoes were evaluated on their ability to predict the age of mosquitoes from more natural environments.
Models trained on spectra from laboratory-reared material were able to predict the age of other laboratory-reared mosquitoes with moderate accuracy and successfully differentiated all day 2 and 15 mosquitoes. Models derived with laboratory mosquitoes could not differentiate between field-derived age groups, with age predictions relatively indistinguishable for day 1-14. Pre-processing of spectral data and improving the PLS regression framework to avoid overfitting can increase accuracy, but predictions of mosquitoes reared in different environments remained poor. Principal components analysis confirms substantial spectral variations between laboratory and field-derived mosquitoes despite both originating from the same island population.
Models trained on laboratory mosquitoes were able to predict ages of laboratory mosquitoes with good sensitivity and specificity though they were unable to predict age of field-derived mosquitoes. This study suggests that laboratory-reared mosquitoes do not capture enough environmental variation to accurately predict the age of the same species reared under different conditions. Further research is needed to explore alternative pre-processing methods and machine learning techniques, and to understand factors that affect absorbance in mosquitoes before field application using NIRS.
由于每日生存对病媒蚊传播能力的影响，我们急需实用的、现场就绪的年龄分级工具来鉴定病媒蚊。先前的研究表明，近红外光谱（NIRS）结合化学计量学和预测建模，可以中等至高度准确地预测实验室饲养的蚊子的年龄。目前尚不清楚该技术是否可用于识别野外捕获的蚊子的年龄结构变化。在这里，我们研究了是否可以使用从实验室品系蚊子中得出的模型来预测从野外捕获的蛹中生长的蚊子的年龄。
分析了在实验室中饲养的成年雌性白纹伊蚊（2、5、8、12 和 15 天大）的 NIRS 数据，与从野外捕获的蛹（1、7 和 14 天大）中出现的蚊子的光谱进行对比。评估了基于实验室蚊子光谱训练的不同偏最小二乘（PLS）回归方法，以了解它们对来自更自然环境的蚊子的年龄预测能力。
基于实验室饲养材料的光谱训练的模型能够以中等准确度预测其他实验室饲养的蚊子的年龄，并且能够成功区分所有 2 天和 15 天大的蚊子。用实验室蚊子得出的模型无法区分野外衍生的年龄组，1 至 14 天的年龄预测相对无法区分。对光谱数据进行预处理并改进 PLS 回归框架以避免过度拟合可以提高准确性，但对在不同环境中饲养的蚊子的预测仍然很差。主成分分析证实，尽管两者均来自同一岛屿种群，但实验室和野外衍生的蚊子之间存在明显的光谱差异。
用实验室蚊子训练的模型能够很好地预测实验室蚊子的年龄，具有良好的敏感性和特异性，但无法预测野外衍生的蚊子的年龄。本研究表明，实验室饲养的蚊子没有捕获足够的环境变化，无法准确预测在不同条件下饲养的同一物种的年龄。需要进一步研究探索替代预处理方法和机器学习技术，并在野外应用 NIRS 之前了解影响蚊子吸收率的因素。
