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. The best PLS model captured the necessary structural information on relative hydrophobic surface area, heteroatoms with higher number of multiple bonds, hydrogen atoms connected to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp) fragments, electron-richness, C–O atom pairs at the topological distance 10 and surface weighted charged partial negative surface areas for explaining the odorant properties of the constituents present in black tea. On the other hand, C–S atom pairs at the topological distance 1, C–C atom pairs at the topological distance 5, donor atoms like N and O for hydrogen bonds, hydrogen atoms connected to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp) fragments and R–CX–X fragments (where, R represents any group linked through carbon and X represents any heteroatom (O, N, S, P, Se, and halogens)) are the key structural components captured by the PLS model developed from the constituents present in coffee. The developed models can thus be successfully utilized for in silico prediction of odorant properties of diverse classes of compounds and exploration of the structural information which creates the odor difference between black tea and coffee.
Highlights
A er water, tea is the most consumed beverage worldwide amongst the non-alcoholic drinks
We have investigated the key structural properties which make the odor difference between tea and coffee using this in silico approach
From the loading plot (Fig. 2C), we have found that the spline term descriptor hJurs-RASA-0.767i and H-049 descriptor are directly correlated with the odorant property due their closeness to the Y-variables (log(1/odor threshold (OT))) while the descriptors Jurs-WNSA-3, F10 [C–O] and ETA_BetaP_ns are inversely correlated with the odorant property of the molecules as these descriptors are situated opposite side of the Y-variable
Summary
A er water, tea is the most consumed beverage worldwide amongst the non-alcoholic drinks. In 2009, the total production of tea worldwide was approximately 3.8 million metric tons.[1] Among the total global production, China contributes 35.4% followed by India (20.6%), Kenya (8.1%), Srilanka (7.5%), Turkey (5.1%), Vietnam (4.8%), and Indonesia (4.1%).[2] Mainly three types of tea are produced such as green tea (unfermented), oolong tea (semi-fermented) and black tea (fermented). Among these three types of tea, black tea is widely used due to its avor.
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