System.IO.FileNotFoundException: Could not find file 'D:\inetpub\wwwroot\ir\wcu\f\Edmonds2021.pdf'. File name: 'D:\inetpub\wwwroot\ir\wcu\f\Edmonds2021.pdf' at System.IO.__Error.WinIOError(Int32 errorCode, String maybeFullPath) at System.IO.FileInfo.get_Length() at ir.Funct.getfilesize(String file) at listing.ItemList(Int32 _mySrchID) Wild caught mosquito species identification using IR spectroscopy and chemometrics, WCU NC DOCKS (North Carolina Digital Online Collection of Knowledge and Scholarship)

Wild caught mosquito species identification using IR spectroscopy and chemometrics

WCU Author/Contributor (non-WCU co-authors, if there are any, appear on document)
Harrison O'Neal Edmonds (Creator)
Western Carolina University (WCU )
Web Site:
Scott Huffman

Abstract: At its current state, mosquito control is all but reliant on the work of the entomologist. The entomologist and other mosquito control personnel are society’s first line of defense against harmful vector-borne diseases that have caused mosquitoes to be named the most deadly animal on earth. An accurate and rapid way of accessing a mosquito population is critical to combat mosquito-borne disease. Current methods of accessing an adult mosquito species rely almost exclusively on microscopic identification by highly trained personnel. This process is both very tedious and labor-intensive. This process is also subject to a series of operator and or laboratory errors. Therefore, there is a need for rapid and nondestructive adult mosquito species identification techniques that can be used on an ecologically, economically, and epidemiologically meaningful scale. Our current research aims to develop biochemical discrimination methods between multiple wild species of mosquitoes using infrared spectroscopy. Infrared spectroscopy is a sensitive, information-rich technique capable of detecting a wide range of molecular signals, ranging from subtle changes in protein secondary structure to transmembrane protein-lipid interactions. The resulting data, when coupled with numerical analysis (chemometric) methods such as principal component analysis, linear discriminate analysis, and partial least squares, may be used to classify mosquito species. Herein, we have applied Fourier transform infrared (FT-IR) microspectroscopy to identify a subset of wild mosquito species, including Culex quinquefasciatus and Aedes triseriatus), using a chemometric model trained by laboratory-reared mosquitoes of the same species. When viii trained using laboratory-reared mosquitoes of varying ages, this method can yield up to 96.2% accuracy when predicting Culex quinquefasciatus and Aedes triseriatus. This method, which is rapid and easy to use, can decrease labor cost and time associated with species identification. Further development coupled with process automation may provide operationally practical methods for rapid species identification of many mosquitoes and other distinguishable mosquito features.

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