Identification of Four Moth Larvae Based on Near-Infrared Spectroscopy Technology

Abstract

Identification of larvae is of great importance in the field of biosafety. Near-infrared spectroscopy (NIRS) was applied in the identification of larvae, and 200 larvae samples of Heliocoverpa armigera Hubner (cotton bollworm), Spodoptera exigua Hiibner (beet armyworm), Prodenia litura Fabriicus, and Ostrinia nubilalis Hubern (corn borer) were selected, from which the spectra of the 4000-7000 cm⁻¹ waveband were obtained for analysis. The results showed that the identification accuracy of the prediction larvae sets predicted by the model of PLS-DA (partial least squares-discriminant analysis) was 100%; the correlation coefficient between the NIR-predicted category variable value and the true value was above 0.90; and the identification accuracy of the prediction larvae sets predicted by the Mahalanobis distance method and correlation coefficient method was above 90%. NIRS provides a promising approach for early category identification of pests in agriculture and forestry.

Keywords:

• category identification
• correlation coefficient
• mahalanobis distance
• moth larvae
• NIRS
• PLS-DA

Notes

RMSEC, root mean square errors of calibration; RMSEP, root mean square error of predictions

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