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

机译：基于PLS回归的红茶和咖啡各种化学成分的气味特性的化学计量学建模

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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.
机译：茶和咖啡是世界上使用最吸引人的非酒精饮料，因为这些饮料中存在各种成分的气味。这项工作的目的是使用基于回归的化学计量模型研究调节红茶和咖啡中成分的气味特性的关键结构特征。我们还研究了在茶和咖啡之间产生异味的关键结构特性。在开发最终的偏最小二乘（PLS）模型之前，我们采用了不同的变量选择策略来提取最相关的变量。使用不同的验证指标对模型进行了广泛验证，结果证明了所开发的预测PLS模型的可靠性和实用性。最佳PLS模型捕获了必要的结构信息，包括相对疏水性表面积，具有更多键的杂原子，与C 3 （sp 3 ）/ C <小> 2 （sp 2 ）/ C <小> 3 （sp 2 ）/ C 3 （sp ）碎片，电子富集，拓扑距离为10的C–O原子对和表面加权的带负电的部分负表面积，用于解释红茶中存在的成分的气味特性。另一方面，在拓扑距离1处的C–S原子对在拓扑距离5处的C–C原子对，氢原子的供体原子（如N和O），氢原子与C 3 < / sup> （sp 3 ）/ C 2 （sp 2 ）/ C 3 （sp 2 ）/ C 3 （sp）片段和R–CX–X片段（其中，R表示通过碳连接的任何基团，X表示任何杂原子（O，N，S，P，Se和卤素））是由PLS模型捕获的，由咖啡中存在的成分形成的关键结构组分。因此，开发的模型可以成功地用于 in silico 预测各种化合物的气味特性，并探索构成红茶和咖啡之间气味差异的结构信息。

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