The effect of mango aroma in low-sugar beverage: A sensory study on odor induced sweetness enhancement.

Excessive intake of sugar has become a public concern. However, it is challenging for food industries to decrease sugar level without sacrificing safety and sensory profile. Odor-induced sweetness enhancement (OISE) is believed to be a novel and promising strategy for sugar reduction. In order to investigate the OISE effect of mango aroma and evaluate its degree of sugar reduction in low-sugar beverages, a mathematical model was constructed through sensory evaluation in this study. The results showed that the maximum liking of low-sugar model beverages was 4.28 % sucrose and 0.57 % mango flavor. The most synergistic of OISE was at the concentration level of 2.24 % sucrose + 0.25 % mango flavor, which was equivalent to 2.96 % pure sucrose solution. With 32.14 % sugar reduction, the mango aroma was suggested to generate the OISE effect. However, the same level of garlic aroma was not able to enhance sweetness perception, suggesting that the congruency of aroma and taste is a prerequisite for the OISE effect to occur. This study demonstrated that the cross-modal interaction of mango aroma on sweetness enhancement in low-sugar model beverages could provide practical guidance for developing sugar-reduced beverages without applying sweeteners.

[Ensemble hologram quantitative structure activity relationship model of the chromatographic retention index of aldehydes and ketones].

Chromatographic retention index (RI) is an important parameter for describing the retention behavior of substances in chromatographic analysis. Experimentally determining the RI values of different aldehyde and ketone compounds in all kinds of polar stationary phases is expensive and time consuming. Quantitative structure activity relationship (QSAR) is an important chemometric technique that has been widely used to correlate the properties of chemicals to their molecular structures. Irrespective of whether the properties of a molecule have been experimentally determined, they can be calculated using QSAR models. It is therefore necessary and advisable to establish the QSAR model for predicting the RI value of aldehydes and ketones. Hologram QSAR (HQSAR) is a highly efficient QSAR approach that can easily generate QSAR models with good statistics and high prediction accuracy. A specific fragment of fingerprint, known as a molecular hologram, is proposed in the HQSAR approach and used as a structural descriptor to build the proposed QSAR model. In general, individual HQSAR models are built in QSAR researches. However, individual QSAR models are usually affected by underfitting and overfitting. The ensemble modeling method, which integrate several individual models through certain consensus strategies, can overcome the shortcomings of individual models. It is worth studying whether ensemble modeling can improve the prediction ability of the HQSAR method in order to build more accurate and reliable QSAR models. Therefore, this study investigates the QSAR model for chromatographic RI of aldehydes and ketones using ensemble modeling and the HQSAR method. Two individual HQSAR models comprising 34 compounds in two stationary phases, DB-210 and HP-Innowax, were established. The prediction ability of the two established models was assessed by external test set validation and leave-one-out cross validation (LOO-CV). The investigated 34 compounds were randomly assigned into two groups. Group Ⅰ comprised 26 compounds, and Group Ⅱ comprised 8 compounds. In the validation of the external test set, Group Ⅰ was employed to manually optimize the two fragment parameters (fragment distinction (FD) and fragment size (FS)) and build the HQSAR models. Group Ⅱ was used as the test set to assess the predictive performance of the developed models. For the DB-210 stationary phase, the optimal individual HQSAR model was obtained while setting the FD and FS to "donor/acceptor atoms (DA)" and 1-9, respectively. For the HP-Innowax stationary phase, the optimal individual HQSAR model was obtained by setting the FD and FS to "DA" and 4-7 respectively. The squared correlation coefficient of cross validation ( [Formula: see text] for predicting the RI values of the DB-210 and HP-Innowax stationary phases were 0.927 and 0.919, 0.956 and 0.979, 0.929 and 0.963, 0.927 and 0.958, and 0.935 and 0.963, respectively. Compared to the individual HQSAR models, the established ensemble HQSAR models show better robustness and accuracy, thus establishing that ensemble modeling is an effective approach. The combination of HQSAR and the ensemble modeling method is a practicable and promising method for studying and predicting the RI values of aldehydes and ketones.