A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach

• Main Authors: Al Fakih, A. M., Algamal, Z. Y., Lee, M. H., Aziz, M.
• Format: Article
• Published: Taylor and Francis Ltd. 2017
• Subjects: QD Chemistry
• Online Access: http://eprints.utm.my/id/eprint/75754/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030458483&doi=10.1080%2f1062936X.2017.1375010&partnerID=40&md5=47d22807f6a4795a52fa1244310bb90b

A robust screening approach and a sparse quantitative structure–retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed...