Near-infrared spectroscopy discriminates mass-reared sterile and wild tsetse flies

Abstract

Background: Monitoring the efficacy of the sterile insect technique (SIT) programs, it is desirable to discriminate between wild and sterile tsetse males captured in monitoring traps. Currently, this is primarily achieved by marking sterile males with fluorescent dye powder before release, and identifying them using a fluorescence camera and/or microscope. However, the accuracy of this method is limited due to defective marking and wild flies contaminated with a few dye particles in the monitoring traps. Molecular techniques have been developed to discriminate doubtful flies, but they are expensive for endemic countries.

Methodology/principal findings: Here, we investigate the ability of a new generation monitoring tool, Near-Infrared Spectroscopy (NIRS), to discriminate between laboratory-reared Glossina palpalis gambiensis males and their field counterparts. NIRS was able to discriminate wild males from laboratory-reared males with 86% accuracy. Notably, the prediction accuracy improved to 88% when the laboratory-reared flies had been irradiated.

Conclusions/significance: These findings suggest that NIRS can successfully identify tsetse flies even when UV camera identification is inconclusive. However, further studies are needed to expand the training dataset and include additional environmental variables before validating NIRS as a complementary method for future tsetse eradication programs.

Fig 1. Average spectra of laboratory sterile-unmarked flies (n = 114) and wild-caught flies (n = 143), each being sampled at discrete wavelengths in the interval [500, 2400 nm].

Fig 2. NIRS ability to discriminate between sterile unmarked and wild-caught flies.

(a) Receiver operating characteristic (ROC) curve illustrating the diagnostic ability of the best-fit model. Overall performance is given by the average area under the ROC curve (AUC). A theoretical perfect diagnostic would be in the top left corner. The average ROC curve shown by the solid line with boxplots shows the variability for 100 randomizations of the training, validation and testing. (b) Coefficient functions for the best-fit model for each of the 100-dataset randomizations (grey lines) and the overall average (black line). (c) Histogram of the estimated linear predictor for the test observations, color-coded by the true class: marked irradiated flies (light blue colored bars) or Wild-caught flies (green bars). Vertical solid black line indicates the best threshold for differentiating marked irradiated flies and wild-caught flies. Darker blue bars indicate where the two distributions overlap and misclassified flies’ status. Misclassified wild-caught Glossina are shown to the left of the optimal classification threshold line and misclassified sterile unmarked Glossina to the right. Inset shows the confusion matrix illustrating the different error rates: true negative rate (tnr) for the sterile unmarked flies correctly classified; false negative rate (fnr) for the misclassified field flies; false positive rate (fpr) for the misclassified sterile unmarked flies; and true positive rate (tpr) corresponding to the field flies correctly classified.

Fig 3. NIRS accuracy in predicting laboratory-reared and wild-caught Glossina.

a: Marking effect (sterile-unmarked vs sterile-marked); b: Irradiation effect (fertile unmarked vs sterile unmarked); c: effect (sterile marked vs wild); d: Origin effect (laboratory fertile and unmarked vs wild).