Predicting pungency and understanding the pungency mechanism of capsaicinoids using TOPS-MODE approach

ABSTRACT

Quantitative structure–property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training set. The predictive power of the model was validated through a test set and several external validation parameters. This showed that the TOPS-MODE descriptors weighted by bond dipole moments, van der Waals atomic radii, and the total solute hydrogen bond basicity affected pungency. The contributions of certain bonds and fragments to pungency were used to understand the pungency mechanism of capsaicinoids. The selected model can more accurately predict pungency of capsaicinoids compared than those found in the literature, and especially bring insights into the structural features and chemical factors related to pungency.

KEYWORDS:

• Quantitative structure–property relationship
• pungency
• capsaicinoids
• topological sub-structural molecular descriptors
• multiple linear regression

Acknowledgements

The authors are grateful to MODESLAB 1.5 software owners for delivering a free copy of such program.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed at: 10.1080/1062936X.2020.1777583.

Additional information

Funding

This work was supported by the Key Scientific Research Project of Colleges and Universities in Henan Province (Grant No. 19A330006, 20A430006, and 18A330005), the special project of Henan Province’s key r&d and promotion project (scientific and technological breakthrough) in 2018, the Excellent Youth Science Foundation Development Project of Henan University (Grant No. yqpy20170071).