Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions
Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions
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DOI：
10.1186/s13071-020-04031-3
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发表时间：
2020-03-30
影响因子：
3.2
通讯作者：
Churcher, Thomas S.
中科院分区：
文献类型：
--
作者：
Ong, Oselyne T. C.;Kho, Elise A.;Churcher, Thomas S.
由于蚊子的日常生存对媒介能力的影响，迫切需要实用的、现场可用的疾病媒介蚊子年龄分级工具。以前的研究表明，近红外光谱（NIRS）结合化学计量学和预测建模，可以预测实验室饲养的蚊子的年龄，具有中等到高的准确性。目前尚不清楚该技术是否可用于识别野外捕获的蚊子年龄结构的变化。在这里，我们调查是否模型来自实验室菌株的蚊子可以用来预测蚊子的年龄从pupestrium收集在现场。方法采用近红外光谱技术对实验室饲养的白纹伊蚊雌蚊（2、5、8、12和15日龄）的近红外光谱数据和野外采集的白纹伊蚊蛹（1、7和14日龄）的近红外光谱数据进行对比分析。不同的偏最小二乘（PLS）回归方法训练的光谱从实验室蚊子的能力，以预测蚊子的年龄从更自然的环境进行了评估。在实验室饲养材料的光谱上训练的模型能够以中等准确度预测其他实验室饲养蚊子的年龄，并成功区分了所有第2天和第15天的蚊子。来自实验室蚊子的模型不能区分来自现场的年龄组，年龄预测相对难以区分的第1-14天。光谱数据的预处理和改进PLS回归框架以避免过拟合可以提高准确性，但对不同环境中饲养的蚊子的预测仍然很差。主成分分析证实，实验室和现场衍生的蚊子之间的光谱差异很大，尽管都来自同一个岛屿的人口。结论用实验室蚊虫训练的模型能较好地预测实验室蚊虫的年龄，但不能预测野外蚊虫的年龄，具有较好的敏感性和特异性。这项研究表明，实验室饲养的蚊子没有捕捉到足够的环境变化，无法准确预测在不同条件下饲养的同一物种的年龄。需要进一步的研究来探索替代的预处理方法和机器学习技术，并在使用NIRS现场应用之前了解影响蚊子吸光度的因素。
Background 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. Methods 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. Results 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. Conclusions 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.