Descriptive Sensory Prediction of Lime‐Type Flavours

Latest studies on the development of data science‐driven sensory prediction tools offer promising new approaches for the flavour and fragrance industry. Data‐driven techniques like regression analysis enabled new workflows that combine molecular sensory, chemical attributes and sensory experiments and predict sensory profiles of mixtures and potential citrus flavouring solutions. In this study, results from omission and spiking experiments on a lime‐derived reference mixture were used to construct a partial least squares regression (PLSR) model for predicting the sensory profiles of lime‐type flavours. The findings demonstrate the feasibility of defining synergistic effects in lime‐type flavours and integrating the results into predictive models. This approach lays the groundwork for sensory prediction of lime‐type flavours and has the potential for further investigations into the prediction of synergistic effects.