Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee

Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee

Jillella Gopala Krishna, Probir Kumar Ojha
Copyright: © 2019 |Volume: 4 |Issue: 4 |Pages: 23
ISSN: 2379-7487|EISSN: 2379-7479|EISBN13: 9781522570554|DOI: 10.4018/IJQSPR.2019100103
Cite Article Cite Article

MLA

Krishna, Jillella Gopala, and Probir Kumar Ojha. "Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee." IJQSPR vol.4, no.4 2019: pp.27-49. http://doi.org/10.4018/IJQSPR.2019100103

APA

Krishna, J. G. & Ojha, P. K. (2019). Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 4(4), 27-49. http://doi.org/10.4018/IJQSPR.2019100103

Chicago

Krishna, Jillella Gopala, and Probir Kumar Ojha. "Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee," International Journal of Quantitative Structure-Property Relationships (IJQSPR) 4, no.4: 27-49. http://doi.org/10.4018/IJQSPR.2019100103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The authors have developed an artificial neural network model using odor threshold (OT) property data for diverse odorant components present in black tea (76 components) and coffee (46 components). The models were validated in terms of both internal and external validation criteria signifying acceptable results. The authors found the significant features controlling the OT property using Mean Absolute Error (MAE)-based criteria in a backward elimination of descriptors, one in each turn. The present results well-corroborated the previously published PLS-regression based chemometric model results.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.