Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 51,
  • Issue 11,
  • pp. 1613-1620
  • (1997)

Comparison of Near-IR, Raman, and Mid-IR Spectroscopies for the Determination of BTEX in Petroleum Fuels

Not Accessible

Your library or personal account may give you access

Abstract

We report for the first time a direct comparison of the three most common vibrational analysis techniques for the determination of individual BTEX components (benzene, toluene, ethylbenzene, ortho -xylene, meta -xylene, and para -xylene) in blended commercial gasolines. Partial least-squares (PLS) regression models were constructed for each BTEX component by using each of the three spectroscopic techniques. A minimum of 120 types of blended gasolines were used in the training set for each BTEX component. Leave-oneout validation of the training sets yields lower standard errors for Raman and mid-IR spectroscopies when compared to near-IR for all six BTEX components. In general, mid-IR has slightly lower standard errors than Raman. These trends are upheld when the models are tested by using independent test sets with a minimum of 40 types of blended gasolines (all of which differ in composition from the training set).

PDF Article
More Like This
Deep neural networks for simultaneous BTEX sensing at high temperatures

Mhanna Mhanna, Mohamed Sy, Ali Elkhazraji, and Aamir Farooq
Opt. Express 30(21) 38550-38563 (2022)

Highly sensitive and selective laser-based BTEX sensor for occupational and environmental monitoring

Mhanna Mhanna, Mohamed Sy, Ayman Arfaj, Jose Llamas, and Aamir Farooq
Appl. Opt. 63(11) 2892-2899 (2024)

Laser-based selective BTEX sensing using deep neural networks

Mhanna Mhanna, Mohamed Sy, Ayman Arfaj, Jose Llamas, and Aamir Farooq
Opt. Lett. 47(13) 3247-3250 (2022)

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.