Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Near Infrared Spectroscopy
  • Vol. 21,
  • Issue 2,
  • pp. 141-148
  • (2013)

Fourier Transform near Infrared Spectroscopy Prediction of trans and cis Fats in Ground Cereal Products at Different Resolutions

Not Accessible

Your library or personal account may give you access

Abstract

In this study, Fourier transform near infrared (FT-NIR) spectroscopy was investigated for use as a tool to determine the trans and cis fat contents in ground cereal products without the need for further oil extraction. To compare the calibration results obtained at different resolutions, the near infrared (NIR) spectra of samples were obtained using an FT-NIR spectrometer at resolutions of 4 cm−1, 8 cm−1 and 16 cm−1. Fat contents of samples were determined using a gas chromatography method. Generally, higher resolution provides better predictions for all types of fats. Each type of fat had its own optimum resolution: 4 cm−1 for trans and 8 cm−1 for cis fat models. At optimal resolution, the models predicted trans and cis fat contents with a SEP and r2 of 0.75% and 0.96, and 0.70% and 0.96, respectively. The results indicated that the trans and cis fat content of cereal products could be determined in minutes without the need for oil extraction within the accuracy required for sample screening (RPD=4.3 or 4.8).

© 2013 IM Publications LLP

PDF Article
More Like This
High-Resolution Fourier Transform Spectroscopy in the Far-Infrared

P. L. Richards
J. Opt. Soc. Am. 54(12) 1474-1484 (1964)

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.