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Prior attempts at forming theoretical predictions regarding the quality of binary odor mixtures have failed to find any consistent predictor for overshadowing of one component in a binary mixture by the other. We test here the hypothesis that trigeminality contributes to overshadowing effects in binary mixture perception. Most odorants stimulate the trigeminal nerve in the nasal sensory epithelium. In the current study we test rats’ ability to detect component odorants in four binary odor sets chosen for their relative trigeminality. We predicted that the difference in trigeminal intensity would predict the degree of overshadowing by boosting or suppressing perceptual intensity of these odorants during learning or during mixture perception. We used a two-alternative choice (TAC) task in which rats were trained to recognize the two components of each mixture and tested on a range of mixtures of the two without reinforcement. We found that even though odorant concentrations were adjusted to balance volatility, all odor sets produced asymmetric psychometric curves. Odor pairs with the greatest difference in trigeminality showed overshadowing by the odorant with weaker trigeminal properties. Odor sets with more evenly matched trigeminal properties also showed asymmetry that was not predicted by either small differences in volatility or trigeminality. Thus, trigeminal properties may influence overshadowing in odor mixtures, but other factors are also likely involved. These mixed results further support the need to test each odor mixture to determine its odor quality and underscore recent results at the level of olfactory receptor neurons that show massive and unpredictable inhibition among odorants in complex mixtures.

The collective properties of a binary mixture of A- and B-type self-steering particles endowed with visual perception are studied by computer simulations. Active Brownian particles (ABPs) are employed with an additional steering mechanism, which enables them to adjust their propulsion direction relative to the instantaneous positions of neighboring particles, depending on the species, either steering toward or away from them. Steering can be nonreciprocal, in particular between the A- and B-type particles. The underlying dynamical and structural properties of the system are governed by the strength and polarity of the maneuverabilities (i.e. maximum reorientation torques) associated with the vision-induced steering. The model predicts the emergence of a large variety of nonequilibrium behaviors, which we systematically characterize for all nine principal sign combinations of AA, BB, AB and BA maneuverabilites. In particular, we observe the formation of multimers, encapsulated aggregates, honeycomb lattices, and predator-prey pursuit. Notably, for a predator-prey system, the maneuverability and vision angle employed by a predator significantly impacts the spatial distribution of the surrounding prey particles. For systems with electric-charge-like interactions (i.e. like-particles repel, unlike attract) and nonstoichiometric composition (i.e. small number excess of one component), we obtain at intermediate activity levels an enhanced diffusion compared to non-steering ABPs.

Accurate, real-time analysis of binary liquid mixtures is essential in chemical sensing, especially for miniaturized, low-cost applications. We present a complementary metal-oxide-semiconductor (CMOS)-based platform—ITEMS (Integrated Time-of-Evaporation Measurement System)—designed to monitor binary mixtures via high-resolution capacitive sensing of evaporation dynamics. ITEMS employs an integrated capacitive sensor to detect time-resolved dielectric changes during droplet evaporation under controlled temperatures. By extracting features such as intermediate evaporation time (Δt₂), total evaporation time (ToE), and capacitance variation (ΔCap), ITEMS provides multidimensional insights into solvent composition. We validated the system across ethanol–water, methanol–water, and methanol–ethanol mixtures, with concentrations from 0 to 100% and temperatures between 25 °C and 60 °C. Our analysis reveals that evaporation time and dielectric response exhibit nonlinear dependencies on solvent concentration, particularly at elevated temperatures. Comparative modeling using linear regression and LOESS confirms LOESS’s superiority in capturing these trends, yielding lower Root Mean Square Error (RMSE) values across all datasets. The CMOS integration enables compact packaging, low sample volume requirements (< 1 μL), and direct digital interfacing via a microcontroller and graphical user interface (GUI). These results establish ITEMS as a robust, scalable platform for high-sensitivity, real-time chemical composition monitoring in environmental, biomedical, and industrial applications.

Effluents containing reactive dyes are globally generated in significant quantities as result of dyeing process applied to cellulosic fibers, frequently exhibiting elevates levels of dyes and inorganic salts. These textile effluents pose a substantial environmental concern due to their potential to induce eutrophication, impede photosynthesis, and even possess carcinogenic proprieties. This research endeavor aimed to address the treatment of intricate bichromatic effluents derived from an industrial dyeing process, which encompassed reactive dyes such as blue 19 (B-19), red 198 (R-198), and yellow 15 (Y-15) using two bio-based adsorbents: (1) yeast waste obtained from the ethanol industry after β-glucan removal from yeast biomass (YW), and (2) a nanomagnetic composite produced with YW and magnetite nanoparticles (YW-MNP). The concentrations of dyes in each binary mixture were quantified through the utilization of UV–Vis spectrophotometry and multivariate calibration, both prior and following the adsorption process. To evaluated the predictive capability partial least squares (PLS) and multivariate linear regression with variable selection via a successive projection algorithm (SPA-MLR), were employed, with PLS demonstrating the best predictive capacity. Langmuir isotherms yielded the best fit for YW and YW-MNP, respectively. Generally, YW exhibited higher dye removal compared to YW-MNP, attaining a maximum of 32% dye removal for bichromatic effluents under the physical–chemical conditions characteristic of effluent production.

Aggressive driving is common across the world. While most aggressive driving is conscious, some aggressive driving behavior may be unconscious on part of motor vehicle drivers. Perceptual bias of aggressive driving behavior is one of the main causes of traffic accidents. This paper focuses on identifying impact factors related to aggressive driving perceptual bias. Questionnaire data from 690 drivers, collected from a drivers’ retraining course administered by the Traffic Management Bureau in Nanjing, China, were used to collect drivers’ socioeconomic characteristics, personality traits, and external environment data. Actual penalty points were considered as an objective indicator and Gaussian mixture model (GMM) was used to cluster an objective indicator into different levels. The driving anger expression (DAX) was used to measure drivers’ self-assessment of aggressive driving behavior and then to identify perceptual biases. Then a binary logistic model was estimated to explore the influence of different factors on drivers’ perceptual bias of aggressive driving behavior. Results showed that bus drivers were less likely to have perceptual bias of aggressive driving behavior. Truck drivers, drivers with an extraversion characteristic, and drivers who have dissatisfaction with road infrastructure and actual work were likely to have a perceptual bias. The findings are potentially beneficial for proposing targeted countermeasures to identify dangerous drivers and improve drivers’ safety awareness.

This study investigated the impact of subthreshold‐level mixtures of KCl and MgSO4 on the sensory preference of drinking water and identified key sensory drivers. Absolute thresholds were first determined, followed by sensory evaluation of individual and blended salt solutions using a 9‐point hedonic scale and CATA test. Design of Experiments (DOE) approach with quadratic polynomial regression optimized the binary mixtures. Results showed samples near the absolute thresholds (0.95 mg/L KCl; 0.65 mg/L MgSO4) were most preferred. Beyond these thresholds, mineral salt concentration negatively correlated with hedonic rating. Sweetness was identified as a key positive driver, while astringency was a strong negative driver. The significant model revealed a nonadditive effect with a negative KCl × MgSO4 interaction (antagonism), with a maximum predicted liking of 6.34 at 0.92 mg/L KCl and 0.60 mg/L MgSO4. These findings demonstrate that subthreshold‐level mixtures of mineral salts within a narrow concentration window can enhance water sensory quality by promoting sweetness and suppressing astringency, providing a foundation for developing premium bottled water. A distinct preference plateau was identified, with liking maximized near the salts' absolute thresholds. Sweetness promotion and astringency suppression were the primary sensory mechanisms driving preference in these mineral salt mixtures. A predictive model pinpointed the optimal blend (0.92 mg/L KCl; 0.60 mg/L MgSO4), providing a practical, data‐driven framework for superior‐tasting drinking water.

Sweeteners are commonly blended to exploit synergistic effects, enabling the desired sweetness to be attained while reducing total usage. However, establishing a quantitative relationship between mixed sweeteners’ concentration and sweetness intensity remains a key challenge. This study developed a sensory evaluation–machine learning approach to construct prediction models for binary/ternary mixtures of five sweeteners (sucrose, glucose, fructose, mannitol, and sorbitol). After feature selection of molecular descriptors and comparison of seven machine learning regression models, the Multilayer Perceptron achieved superior performance for the binary mixtures (R2 = 0.9828), while the Support Vector Regression exhibited optimal performance for the ternary mixtures (R2 = 0.9825). Concentration–sweetness intensity curves of mixed sweeteners at specific concentrations were generated using these two optimal prediction models. Results showed that at low concentrations, ternary blends of one sugar and two polyols (mannitol and sorbitol) exhibited stronger synergism than binary mixtures in the same concentration range. Specifically, blending the composite system of 1% mannitol and 2% sorbitol with 1% sucrose, 1% glucose, and 1% fructose separately increased the sweetness intensity by 39.6%, 42.8%, and 37.4%, respectively. This work confirms that machine learning can establish a quantitative relationship between multi-component sweeteners’ concentration and sweetness intensity, reveal their complex interactions, and provide a novel approach for intelligent sensory assessment and formulation design.

Most beverages are complex matrices. Different taste compounds within these matrices interact, and thus affect the perception of the tastes. Sweetness and sourness have generally been known to suppress each other, but often such investigations have focused on aqueous solutions. Investigations into what happens when these known interactions are transferred to more complex solutions are scarce. In this study, we investigated the differences in taste interactions between an aqueous matrix and a cold-brewed coffee matrix. Two sub-studies were conducted. In one, six aqueous samples were evaluated by 152 naïve consumers; in the other six cold-brewed coffee samples were evaluated by 115 naïve consumers. In both studies participants tasted samples with no addition or with addition of either sucrose, citric acid, tartaric acid, or a mix of sucrose and either of the acids. Results showed that the sweetness of sucrose was suppressed by both citric acid and tartaric acid in both matrices. The sourness of both citric acid and tartaric acid was suppressed in the aqueous matrix, but only the sourness of tartaric was suppressed in the coffee matrix. Generally, the suppression was lower in the coffee matrix compared to the aqueous matrix. In conclusion, results from taste interaction studies conducted on aqueous matrices can to some extent, with caution, be interpolated to more complex matrices. Importantly, suppression effects might diminish with an increase in matrix complexity.

Alcohols and esters are core flavor-active constituents of Qingxiangxing Baijiu (QXB), yet ethanol concentration's regulatory role in their thresholds and interactions remains unclear. Physicochemical analysis showed reduced-alcohol QXB (L-QX, 42%, / ) had higher total acid (1.48 g/L) but lower total ester (1.52 g/L) than high-alcohol QXB (H-QX, 53%, / ; 1.20 g/L total acid, 2.05 g/L total ester). Sensory evaluation (0-5 scale) revealed H-QX had higher fruity (3.6 vs. 2.0), grassy (3.2 vs. 1.8), and grainy (3.0 vs. 1.9) aroma scores, while L-QX showed higher sour (2.1 vs. 1.5) and lees (1.7 vs. 1.1) notes ( < 0.05). The quantification of gas chromatography-flame ionization detection (GC-FID) determined the concentrations of eight alcohols and esters in H-QX samples and identified that most flavor compounds had higher concentrations than L-QX samples. Three alternative forced-choice tests showed 53% ethanol elevated olfactory thresholds (OTs) of five compounds, with ethyl lactate (1.53-fold) and isopentanol (1.89-fold) vs. 42%. For 16 alcohol-ester binary mixtures, 12 pairs had OT ratios (53% vs. 42%) < 1, especially 3 pairs (e.g., n-propanol-ethyl acetate) < 0.5. OAV/S curve analyses indicated all 16 mixtures had masking effects, with 11 pairs stronger at 42%. Verification validated 53% ethanol mitigated masking, enhancing fruity/grassy aromas by 38.1%/25.0%. This study provides support for QXB dealcoholization flavor regulation.

A novel odor interaction model was proposed for binary mixtures of benzene and substituted benzenes by a partial differential equation (PDE) method. Based on the measurement method (tangent-intercept method) of partial molar volume, original parameters of corresponding formulas were reasonably displaced by perceptual measures. By these substitutions, it was possible to relate a mixture’s odor intensity to the individual odorant’s relative odor activity value (OAV). Several binary mixtures of benzene and substituted benzenes were respectively tested to establish the PDE models. The obtained results showed that the PDE model provided an easily interpretable method relating individual components to their joint odor intensity. Besides, both predictive performance and feasibility of the PDE model were proved well through a series of odor intensity matching tests. If combining the PDE model with portable gas detectors or on-line monitoring systems, olfactory evaluation of odor intensity will be achieved by instruments instead of odor assessors. Many disadvantages (e.g., expense on a fixed number of odor assessors) also will be successfully avoided. Thus, the PDE model is predicted to be helpful to the monitoring and management of odor pollutions.