Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Near Infrared Spectroscopy
  • Vol. 3,
  • Issue 2,
  • pp. 89-95
  • (1995)

Differentiation of Male, Female and Dead Silkworms While in the Cocoon by near Infrared Spectroscopy

Not Accessible

Your library or personal account may give you access

Abstract

The feasibility of using a non-destructive method to differentiate male (M), female (F) and dead (D) worm cocoons of silkworms has been studied with near infrared (NIR) spectroscopy over the region 680–1235 nm on a sample set of 205 fresh cocoons. On the basis of the spectral data of the first and second derivatives, characteristic vectors were extracted by multiple linear regression and a Bayes critical function was established. This function was used to test 375 samples and the success rate was 95.7%. This method of sex judging is thus preferable to the conventional use of the weight and size of a cocoon which achieved a success rate of only 82.9% on the same samples. Experiments showed that the differences between NIR spectra of M and F cocoons mainly result from the property of silkworm cocoons rather than their incunabulum.

© 1995 NIR Publications

PDF Article
More Like This
Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk

Rahat Ullah, Saranjam Khan, Samina Javaid, Hina Ali, Muhammad Bilal, and Muhammad Saleem
Biomed. Opt. Express 9(2) 844-851 (2018)

Optical penetration-based silkworm pupa gender sensor structure

Sarun Sumriddetchkajorn and Chakkrit Kamtongdee
Appl. Opt. 51(4) 408-412 (2012)

Noise reduction and accuracy improvement in optical-penetration-based silkworm gender identification

Chakkrit Kamtongdee, Sarun Sumriddetchkajorn, Sataporn Chanhorm, and Watcharapong Kaewhom
Appl. Opt. 54(7) 1844-1851 (2015)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.