Near-Infrared Spectroscopy for metabolite quantification and species identification
The aim of the study was to investigate the accuracy of near-infrared spectroscopy (NIRS) in determining triglyceride level and species of wild caught Drosophila. NIRS is a remote sensing method that uses the near-infrared region of the electromagnetic spectrum. It detects the absorption of light by molecular bonds and can be used with live insects. We employ the chemometric approach to combine spectra and reference data from a known sample to produce a multivariate calibration model. Once the calibration model was developed, we used an independent set to validate the accuracy of the calibration model. The optimized calibration model for triglyceride quantification yielded an accuracy of 73%. Simultaneously, we used NIRS to discriminate two species of Drosophila. Flies from independent sets were correctly classified into D. melanogaster and D. simulans with accuracy higher than 80%. Finally, we show that the biological interpretations derived from reference data and the NIRS predictions do not differ. These results suggest that NIRS has the potential to be used as a high throughput screening method to assess a live individual insects triglyceride level and taxonomic status.