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Data from: Near-infrared spectroscopy for metabolite quantification and species identification

Citation

Aw, Wen Chyuan; Ballard, J. William (2019), Data from: Near-infrared spectroscopy for metabolite quantification and species identification, Dryad, Dataset, https://doi.org/10.5061/dryad.324ch00

Abstract

Near-infrared spectroscopy (NIRS) is a high-throughput method to analyse the near-infrared region of the electromagnetic spectrum. It detects the absorption of light by molecular bonds and can be used with live insects. In this study, we investigate the accuracy of NIRS in determining triglyceride level and species of wild caught Drosophila. We employ the chemometric approach to produce a multivariate calibration model. The multivariate calibration model is the mathematical relationship between the changes in NIR spectra and the property of interest as determined by the reference analytical method. 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 coefficients of determination of 0.73 for the calibration test set and 0.70 for the independent test set. 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%. These results suggest that NIRS has the potential to be used as a high throughput screening method to assess a live individual insect’s triglyceride level and taxonomic status.

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