Sex determination of silkworm pupae using VIS-NIR hyperspectral imaging combined with chemometrics

To explore an accurate and non-destructive method to discriminate the sex of silkworm pupae, the visible and near-infrared (VIS-NIR) hyperspectral imaging (HSI) technique was employed in this paper. First, a total of 520 hyperspectral images of silkworm pupae of four species were captured using a push-broom HSI system in the spectral region of 363 nm to 1026 nm and then calibrated for reflectance. The mean spectral data were extracted from the region of interest (ROI). Second, five optimal wavelengths (403, 440, 505, 533, 721 nm) were selected by successive projection algorithm (SPA). Then gray-level co-occurrence matrix (GLCM) analysis was implemented on the 500 nm image. Finally, support vector machine (SVM) and radial basis function and neutral network (RBF-NN) models were established based on full spectra, textural data, spectral data and fusion data, respectively. The SVM and RBF-NN models using fusion data reached the most satisfactory performance with a high correct classification rate of 98.75%. Furthermore, the built SVM model based on fusion data could be promoted to identify the sex of another two species of silkworm pupae with accuracy of 97% and 96%, indicating that HSI technology can be served as a new method to differentiate the sex of silkworm pupae.