DOI: 10.1101/490326Dec 7, 2018Paper

Age Grading An. Gambiae and An. Arabiensis Using Near Infrared Spectra and Artificial Neural Networks

BioRxiv : the Preprint Server for Biology
Masabho P MilaliRichard J Povinelli

Abstract

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. Based on ten-fold Monte Carlo cross-validation, an ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the acc...Continue Reading

Related Concepts

Classification
Computer Systems
Laboratory
Malaria
Culicidae
Regression Analysis
Plant Roots
Infrared Spectroscopy
Spectroscopy, Near-Infrared
Cross Validation

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