High-speed sex identification and sorting of living silkworm pupae using near-infrared spectroscopy combined with chemometrics

Abstract An identification method of near infrared spectroscopy in combination with multivariate analysis and a high-speed automated sorting system for living silkworm chrysalis were developed. First, the factors affecting sex discrimination were examined using static spectra, including the varieties and positions of pupae. Second, modeling using dynamic spectra was investigated. The dynamic spectra contain much more noise than that of static spectra. The significant noise dominated by optical path difference (OPD) has a strong impact on the identification of male and female pupae. High-speed sex discrimination of living silkworm pupae was successfully realized by combining soft independent modeling of class analogy (SIMCA) and a preprocessing method involving angle spectra. The results indicated that this method achieved a correct identification rate of 98.0%. Finally, various varieties of living silkworm pupae were sorted with respect to sex using the high-speed sorting device. The sorting rate exceeded 7.7 pupae per second and the error rate could be controlled within 2.5%. The device has been applied successfully to sort living silkworm pupae about 1.2 tons per day in different seasons and regions, and can exert a significant effect on the development of modern sericulture.