Sensory sweetness and sourness interactive response of sucrose-citric acid mixture based on synergy and antagonism

The study addresses how sweet and sour taste sensations interact—via synergy and antagonism—when sucrose and citric acid are combined. While prior work has identified general trends of enhancement or suppression between taste pairs, quantitative rules describing how perceived intensity changes with concentration for two interacting tastes are lacking. Leveraging psychophysical laws (Weber-Fechner and Stevens), the authors aim to quantify how citric acid affects perceived sweetness of sucrose and how sucrose affects perceived sourness of citric acid, and to derive mathematical models capable of predicting the interactive sensory response. Establishing such models is important for food formulation, sensory science, and applications in the beverage and additive industries.

Previous studies have reported numerous interactions among basic tastes. Umami (monosodium glutamate) can enhance sweet and salty at certain levels and suppress sour and bitter at others; sweet often suppresses other tastes at medium/high intensities and shows symmetric suppression with bitter; low salt can enhance sweet, and salt can suppress bitter; some studies reported sweet-bitter mutual suppression and sour-salty enhancement. Psychophysical frameworks (Weber-Fechner, Stevens law) have been used to relate stimulus concentration to perceived intensity for single tastes, and prior work examined ratios and indices (Weber fraction, Stevens exponent) for sweet and sour. However, a detailed quantitative rule or equation for the strength variation due to synergism/antagonism between two tastes, specifically sweet-sour interactions, has not been systematically established.

Materials: Food-grade sucrose and citric acid (Sinopharm Chemical Reagent Co., Ltd., China); solutions prepared in ultrapure water (18.2 MΩ·cm), stored at 20 °C. Panel: 32 experienced assessors (16 male, 16 female; 20–35 years), selected and trained per Chinese national standard GB/T 16291.1-2012. Training included terminology (absolute and difference thresholds), basic taste discrimination, triangle test and paired comparison methods, and intensity rating consistency (≤10% relative error). Participants abstained from eating/smoking ≥30 min before tests. Design: A “close type” question approach based on triangle tests (for absolute thresholds) and paired comparisons (for difference thresholds) was used. Sucrose samples for absolute and difference threshold testing were prepared in backgrounds containing varying citric acid concentrations (0.008%, 0.009%, 0.010%, 0.011%, 0.012%). Citric acid samples were prepared in sucrose backgrounds (0.5%, 1.0%, 2.0%, 4.0%, 6.0%). Concentrations were weight by volume. Absolute threshold determination: Triangle test determined absolute thresholds of sucrose under each citric acid background and of citric acid under each sucrose background. Difference threshold determination: For sucrose under each citric acid background, the first compared sample was set at its absolute threshold; test samples were 120%, 125%, 130%, and 135% of the compared sample. Subsequent stages used the previously determined difference threshold as the new comparison, iterating through nine difference thresholds. For citric acid under sucrose backgrounds, each test set used 105%, 110%, 115%, and 120% of the compared sample; analogous iterative procedure was followed. Modeling: Sensory difference strength curves were constructed by plotting concentration points (absolute threshold and successive difference thresholds) on the X-axis versus ordinal sensory difference strength (1–10) on the Y-axis. Empirical curves were fitted, and relationships between background concentration and (i) the target tastant’s absolute threshold and (ii) its Weber fraction were derived using the Weber-Fechner framework. Derived equations for sucrose: AT_suc = 0.399 × ln(C_ca) + 2.405; W_suc = 18.277 × ln(C_ca) + 114.49. For citric acid: AT_Ca = 0.00064 × ln(C_suc) + 0.0072; W_Ca = −2.92 × ln(C_suc) + 13.92. These were substituted into a general Weber-Fechner-based relation to produce sweet- and sour-intensity prediction models (SSTVM) under cross-backgrounds. Validation: Additional backgrounds were used for verification—citric acid at 0.0095%, 0.0105%, 0.0115% (for sucrose curves) and sucrose at 1.5%, 3.0%, 5.0% (for citric acid curves). Predicted sensory difference strength curves were compared to human evaluations; relative error and relative root mean squared error (RRMSE) were computed.

- Under citric acid backgrounds (0.008–0.012%), sucrose sensory difference strength curves remained logarithmic with high fit (R^2 > 0.99), consistent with the Weber-Fechner law. - Citric acid increased sucrose’s absolute threshold from 0.424% (no CA) to 0.624% (at 0.011–0.012% CA) and increased sucrose’s Weber fraction from 20.5% to 33.0%, indicating reduced detectability of initial sweetness and reduced sensitivity to incremental sucrose increases under acidic backgrounds. - Fitted relationships for sucrose under citric acid background: AT_suc = 0.399 × ln(C_ca) + 2.405; W_suc = 18.277 × ln(C_ca) + 114.49. The sucrose sensory strength model incorporated these into a Weber-Fechner formulation (Eq. 4). - Under sucrose backgrounds (0.5–6.0%), citric acid sensory difference strength curves were also logarithmic (R^2 > 0.99). Sucrose increased citric acid’s absolute threshold from 0.0057% (no sucrose) to 0.0082% (≥4% sucrose) and decreased citric acid’s Weber fraction from 17.96% to as low as ~9.1–9.53%, indicating reduced detectability of initial sourness but increased sensitivity to incremental citric acid increases in sweet backgrounds. - Fitted relationships for citric acid under sucrose background: AT_Ca = 0.00064 × ln(C_suc) + 0.0072; W_Ca = −2.92 × ln(C_suc) + 13.92; integrated into the citric acid model (Eq. 7). - The Sweet-Sour Taste Sensory strength Variation Models (SSTVM) quantitatively predicted interactive responses. Validation showed close agreement between predictions and human evaluations. Reported metrics included an overall relative error of 1.02% (abstract) and, in detailed tests, average relative errors of 0.47% (sucrose in CA) and 1.56% (citric acid in sucrose), with average relative RMSE values of 0.16% and 4.1%, respectively. - The results support that sweet-sour interactions involve both antagonism (elevated absolute thresholds for the other taste) and synergy-like sensitivity shifts (Weber fraction changes), depending on direction and concentration ranges.

The study quantitatively elucidates how sucrose and citric acid interact perceptually. Citric acid elevates sucrose’s absolute threshold and Weber fraction, meaning initial sweetness is harder to detect and changes in sweetness with added sucrose are less noticeable in acidic matrices. Conversely, sucrose elevates citric acid’s absolute threshold but lowers its Weber fraction, making initial sourness harder to detect yet increasing sensitivity to sourness changes with added acid. These bidirectional effects align with potential peripheral mechanisms in taste buds, where type II (sweet) and type III (sour) taste cells communicate via paracrine neurotransmitters (ATP, adenosine, acetylcholine; GABA, serotonin), enabling inhibitory and excitatory interactions that could underlie the observed antagonism and sensitivity modulation. By embedding background-dependent absolute thresholds and Weber fractions into Weber-Fechner-based equations, the SSTVM provides a predictive framework for sweet-sour interactions. The strong agreement with human data indicates the models can inform formulation decisions in foods and beverages, guide health-related applications (e.g., managing sweetness/sourness perception), and support development/calibration of taste sensors and electronic tongues.

This work establishes quantitative Sweet-Sour Taste Sensory strength Variation Models (SSTVM) that predict how sucrose and citric acid interact perceptually via changes in absolute thresholds and Weber fractions under cross-backgrounds. Both interactions adhere to the Weber-Fechner law, and the fitted models accurately reproduce human sensory curves with low relative errors. The models offer practical utility for optimizing sweetness and sourness in product development, health management, and intelligent sensory systems. Potential future research could extend this modeling approach to other taste pairings and more complex multi-taste mixtures, explore wider concentration ranges, and integrate mechanistic insights from cellular and neural taste processing.