Ahmadi Shahin, Lotfi Shahram, Hamzehali Hamideh, Kumar Parvin
Department of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University Tehran Iran
Department of Chemistry, Payame Noor University (PNU) 19395-4697 Tehran Iran.
RSC Adv. 2024 Jan 19;14(5):3186-3201. doi: 10.1039/d3ra07960k. eCollection 2024 Jan 17.
Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios. In chromatography, the identification of compounds is done by comparing obtained peaks with a reference standard. However, there are cases where reference standards are either unavailable or the chemical information of VOCs is not documented in reference libraries. To overcome these limitations, theoretical methodologies are applied to estimate the retention indices (RIs) of new VOCs. The aim of the present work is to develop a reliable QSPR model for the RIs of 273 identified VOCs of different types of pepper. Experimental retention indices were measured using comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC/qMS) using a coupled BPX5 and BP20 column system. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models using the hybrid optimal descriptor extracted from a combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3) is used. The results of the statistical parameters of each target function are compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of of the validation set. The statistical result of the best model for the validation set of split 6 computed with TF3 (WIIC = 0.5 & WCII = 0.3) is = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6). Moreover, the proposed model was used for an external validation set.
在全球范围内，由于其独特的辛辣味、香气、味道和颜色，各种类型的胡椒被用作食品添加剂。这种香料因其由生物碱胡椒碱带来的辛辣味以及挥发性精油赋予的香气而受到重视。精油由不同浓度和比例的挥发性有机化合物（VOCs）组成。在色谱分析中，通过将获得的峰与参考标准进行比较来鉴定化合物。然而，存在参考标准不可用或VOCs的化学信息未记录在参考库中的情况。为克服这些限制，应用理论方法来估计新VOCs的保留指数（RIs）。本工作的目的是为不同类型胡椒中鉴定出的273种VOCs的RIs建立一个可靠的定量结构-性质关系（QSPR）模型。使用综合二维气相色谱-四极杆质谱联用仪（GC×GC/qMS），采用BPX5和BP20柱系统联用，测量实验保留指数。使用CORAL软件内置的蒙特卡罗算法，利用从SMILES和HFG（氢填充图）组合中提取的混合最优描述符生成QSPR模型。将273种VOCs的整个数据集进行十次划分，每次划分进一步分为四组：主动训练组、被动训练组、校准组和验证组。使用具有四个目标函数TF0（WIIC = WCII = 0）、TF1（WIIC = 0.5 & WCII = 0）、TF2（WIIC = 0 & WCII = 0.3）和TF3（WIIC = 0.5 & WCII = 0.3）的相关平衡方法。比较每个目标函数的统计参数结果。同时应用相关理想度指数（IIC）和相关强度指数（CII）可提高模型的预测潜力。根据验证集的 数值判断最佳模型。使用TF3（WIIC = 0.5 & WCII = 0.3）计算的第6次划分验证集的最佳模型统计结果为 = 0.9308，CCC = 0.9588，IIC = 0.7704，CII = 0.9549， = 0.9281，RMSE = 0.544。还使用最佳模型（第6次划分）提取RI增加/减少的促进因子。此外，将所提出的模型用于外部验证集。
