Chemosensors

Palatability has an impact on pediatric medication adherence, yet the influence of water dilution, PBS volume fraction, and salivary pH on the sensory performance of formulations is poorly characterized. This study provides the first systematic, biorelevant evaluation of how water dilution factor, PBS volume fraction, associated ionic strength change, and physiologically relevant pH affect the palatability of three commercially available hyoscine butylbromide (HBB) syrup brands (X, Y, and Z), extending earlier characterization of undiluted formulations. Samples were evaluated using an Alpha Astree II electronic tongue (ChemFET array. Water dilution (Batch B) and PBS volume fractions of 2 mL or 5 mL at pH 6.2, 6.8, and 7.4 within a fixed total measurement volume of 25 mL simulated salivary pH and ionic composition across the physiological range relevant to pediatric and adult populations. Pure HBB API solutions were tested in parallel. Principal component analysis (>85% cumulative variance) confirmed robust discrimination across all conditions. The water dilution factor increased bitterness for all brands by disrupting excipient matrix integrity, with cellulosic polymer-containing Brands X (HEC) and Y (HPMC) retaining superior taste masking over polymer-free Brand Z. The PBS volume fraction revealed pH-dependent, brand-specific palatability responses, at pH 6.2, 6.8, and 7.4, and the hierarchy was consistently Y > X > Z, confirming the superiority of the Brand Y formulation, combining HPMC with sodium cyclamate and saccharin sodium, across the full physiological salivary pH range, noting that this superiority reflects the broader excipient composition of Brand Y, rather than polymer choice alone. Notably, Brand X exhibited substantially higher ANS sweetness scores than Brand Y at 2 mL pH 7.4 PBS, reflecting differential sweetener system sensitivity to near-neutral PBS ionic conditions. Increasing the PBS volume fraction from 2 mL to 5 mL within a fixed total measurement volume of 25 mL, increased the ionic strength and pH contribution of the simulated salivary component, amplified bitterness and attenuated sourness across all brands, reflecting changes in the ionic environment at the ChemFET sensor membrane, rather than any increase in the intrinsic bitterness of HBB itself. Taste-masking strategies that rely solely on sweeteners, without viscosity-building polymers, are inadequate under biorelevant oral conditions. pH-stratified assessment is essential for comprehensive characterization of palatability in pediatric oral liquid formulations.

Nanopipette-based single-molecule sensing offers a label-free route to protein analysis, but distinguishing structurally different proteins from ionic current traces remains difficult. Here, we classified three clinically important proteins (CCL5, MMP1 and TIMP1) from their nanopipette translocation signals. Twelve biophysical features were extracted from a balanced subset of 846 events (282 per protein) drawn from 1317 recorded events (one experiment per protein). Random Forest, SVM and gradient boosting were compared by five-fold cross-validation; gradient boosting performed best (accuracy 92.1 ± 2.0%, macro ROC-AUC 0.982). Absolute current drop and relative blockage (ΔI/I₀) were the most important features (about 37% of importance), and PCA and clustering separated TIMP1 but not CCL5 and MMP1. However, with a histogram-based gradient boosting classifier on a pool of 28 experiments (7095 events), balanced accuracy fell from 90.0% under event-level cross-validation to 73.5% when whole experiments recorded with other nanopipettes were held out (mean CCL5 recall 30%), and to 47–63% without baseline-related features, whereas open-pore current alone gave 92.0% and 82.6%, indicating pipette-specific contributions. These proof-of-concept findings indicate that, within the present dataset, nanopipette signals can distinguish the three proteins, whereas generalization to independent nanopipettes, particularly for CCL5, remains to be demonstrated.

Optical acquisition geometry determines the tissue regions represented in visible–near-infrared (Vis–NIR) spectra, yet its interactions with spectral preprocessing and multimodal fusion remain insufficiently understood for intact citrus fruit. This study systematically compared diffuse reflectance (DR), diffuse transmittance (DT), and transmittance (T) for the nondestructive prediction of soluble solids content (SSC) in Shatangju mandarins. For each optical mode, 180 modeling pipelines combining six preprocessing methods, five wavelength-selection strategies, and six regression algorithms were evaluated using repeated nested cross-validation. Three multimodal fusion strategies were subsequently investigated: direct feature concatenation, sequential orthogonalized partial least squares (SO-PLS), and stacked generalization (Stacking). Marked differences in predictive performance were observed among the three optical modes. T achieved the strongest single-mode performance, followed by DT, whereas DR performed substantially worse, with mean coefficient of determination ($R^2$) values of 0.832, 0.815, and 0.448 and corresponding root mean square errors (RMSEs) of 0.808, 0.849, and 1.463 °Brix, respectively. Preprocessing effectiveness was strongly mode-dependent: Savitzky–Golay smoothing provided the greatest improvement for DT, whereas preprocessing yielded only limited gains for DR. Wavelength selection did not consistently improve prediction over the full valid wavelength range, although competitive adaptive reweighted sampling substantially reduced spectral dimensionality in some pipelines. Neither direct feature concatenation nor SO-PLS surpassed the best single-mode model. In contrast, prediction-level Stacking consistently improved predictive performance. Stacking-6, combining the two highest-ranked base models from each optical mode, achieved the best overall results, with a mean $R^2$ of 0.861, RMSE of 0.735 °Brix, mean absolute error of 0.576 °Brix, concordance correlation coefficient of 0.924, residual predictive deviation of 2.700, and ratio of performance to interquartile range of 3.931. Relative to the best T-mode model, Stacking-6 reduced mean RMSE by approximately 9.0% and achieved lower RMSE in all 10 repetitions. These findings demonstrate that acquisition geometry governs not only single-mode predictive capability but also the effectiveness of preprocessing and multimodal integration. Under the conditions evaluated, transmittance provided the most informative single-mode spectra, whereas prediction-level stacking more effectively exploited complementary information across optical modes than feature-level concatenation or SO-PLS fusion.