PLS regression-based chemometric modeling of odorant properties of diverse chemical constituents of black tea and coffee.

ABSTRACT: Tea and coffee are the most attractive non-alcoholic beverages used worldwide due to the odorant properties of diverse components present in these beverages. The aim of this work is to investigate the key structural features which regulate the odorant properties of constituents present in black tea and coffee using regression-based chemometric models. We have also investigated the key structural properties which create the odor difference between tea and coffee. We have employed different variable selection strategies to extract the most relevant variables prior to development of final partial least squares (PLS) models. The models were extensively validated using different validation metrics, and the results justify the reliability and usefulness of the developed predictive PLS models. Th

SUBMITTER: Ojha PK 

PROVIDER: S-EPMC9092630 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature