Sex classification of silkworm pupae from different varieties by near infrared spectroscopy combined with chemometrics

Abstract

The accuracy of sex classification of silkworm pupae is directly related to the quality of crossbred silkworm pupae and silkworm silk. Manual selection as the traditional method for sex identification of silkworm pupae is labor-intensive and time-consuming. In addition, there are hundreds of silkworm pupae varieties. Currently, the widely used near infrared spectroscopy（NIR) technology showed unsatisfactory accuracy in distinguishing the sex of different species of silkworm pupae. Thus, we developed a method combining NIR and contrastive principal component analysis(cPCA) to achieve high precision sex classification of different silkworm pupae by introducing the background data set to eliminate the influence of silkworm pupae varieties. The experimental results showed that the cPCA analysis showed better performance (98.34%) compared with the accuracy of PCA analysis (71.36%). After characteristic wavenumber selection by iPLS, the accuracy could be improved to 99.58% by using the cPCA algorithm. The combination of NIR spectroscopy with cPCA provides a method for developing portable devices to quickly identify the sex of silkworm pupae.