Predicting tactile sensory attributes of personal care emulsions based on instrumental characterizations: A review.

Emulsions in the form of creams, lotions, gels or foams are the most widely used personal care formulations to improve the condition and feel of the skin. Achieving an optimal balance between their performance, effectiveness and sensory profile is essential, with the sensory profile being a key factor in consumer satisfaction and the success of these products in the market. Well-established methods using highly trained and semi-trained panels (e.g. Spectrum descriptive analysis, Flash Profile method, Quantitative Descriptive Analysis method and 'Check-all-that-apply') are available and commonly used for the sensory assessment of personal care products. Nevertheless, a common drawback among all these methods is their inherent cost, both in terms of financial resources and time requirements. In recent years, research studies have emerged to address this limitation by investigating potential correlations between tactile sensory attributes and instrumental data associated with the physical characteristics of topical formulations. In other words, significant efforts have been invested in the development of robust instrumental methods specifically designed to accurately predict the sensory description that a panel of assessors could establish. These methods are not only faster, cheaper and more objective compared to traditional sensory testing, but they can also be applied to formulations that have not undergone extensive safety and toxicological testing. This review summarizes the most relevant findings, trends and current challenges in predicting tactile sensory attributes of personal care emulsions based on instrumental parameters.

Les émulsions sous forme de crèmes, lotions, gels ou mousses sont les formulations de soins personnels les plus largement utilisées pour améliorer l'état et la sensation de la peau. Il est essentiel de parvenir à un équilibre parfait entre leur performance, leur efficacité et leur profil sensoriel, ce dernier étant un facteur clé de la satisfaction des consommateurs et du succès de ces produits sur le marché. Des méthodes bien établies utilisant des panels hautement qualifiés et semi­qualifiés sont disponibles et couramment utilisées pour l'évaluation sensorielle des produits de soins personnels. Néanmoins, un inconvénient commun à toutes ces méthodes est leur coût inhérent, tant en termes de ressources financières que de temps. Ces dernières années, nous avons assisté à l'émergence d'études de recherche tentant de remédier à ces limites en étudiant les corrélations potentielles entre les descripteurs sensoriels tactiles et les données instrumentales associées aux caractéristiques physiques des formulations topiques. En d'autres termes, des efforts importants ont été déployés dans le développement de méthodes instrumentales robustes spécifiquement conçues pour prédire avec précision la description sensorielle qu'un panel d'évaluateurs pourrait établir. Ces méthodes sont non seulement plus rapides, moins coûteuses et plus objectives par rapport aux tests sensoriels traditionnels, mais elles peuvent également être appliquées à des formulations qui n'ont pas été entièrement testées en termes de sécurité et de profils toxicologiques. La présente revue résume les résultats, tendances et défis actuels les plus pertinents dans la prédiction des attributs sensoriels tactiles des émulsions de soins personnels à partir de paramètres instrumentaux.

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks.

The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in the industry. Recently, classical quantitative structure-property relationship (QSPR) and graph neural networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the temperature dependence of the CMC, which is highly relevant to practical applications. We herein develop a GNN model for the temperature-dependent CMC prediction of surfactants. We collected about 1400 data points from public sources for all surfactant classes, i.e., ionic, nonionic, and zwitterionic, at multiple temperatures. We test the predictive quality of the model for the following scenarios: (i) when CMC data for surfactants are present in the training of the model in at least one different temperature and (ii) CMC data for surfactants are not present in the training, i.e., generalizing to unseen surfactants. In both test scenarios, our model exhibits a high predictive performance of R2 ≥ 0.95 on test data. We also find that the model performance varies with the surfactant class. Finally, we evaluate the model for sugar-based surfactants with complex molecular structures, as these represent a more sustainable alternative to synthetic surfactants and are therefore of great interest for future applications in the personal and home care industries.

Development of enteric polymer-based microspheres by spray-drying for colonic delivery of Lactobacillus rhamnosus GG.

Antibiotics are well-known disruptive elements of the intestinal microbiota and antibiotic-associated diarrhea appeared as the most common complication related with post-antibiotic dysbiosis. Lactobacillus rhamnosus GG (LGG) strain is very effective in preventing antibiotic-associated diarrhea in children and adults. However, as any probiotics, it is concerned by the loss of viability during storage and gastrointestinal transit. The aim of this study was to develop an encapsulation system suitable for the specific colonic delivery of LGG strain after oral administration. For this purpose, spray-dried Eudragit® S100 microparticles encapsulating LGG bacteria were developed by using an aqueous based spray-drying approach, avoiding the use of organic solvents. Carbohydrates were added to the formulation since they are widely used as protective agents of bacteria against the harmful effect of dehydration stress. Here, both Surface Enhanced Raman Scattering (SERS) and conventional plate count methods showed that carbohydrates increased the survival ratio of bacteria after spray-drying from 3 to more than 50%. Moreover, these protective agents ensured low residual moisture content thus providing great stability of the cells in the spray-dried powder during storage. Significant improvement of the cell viability in simulated gastro intestinal fluid (SGIF) was observed for encapsulated cells as compared with free LGG bacteria for which no viable cell was detectable after 1 h incubation in gastric fluid only. As a consequence, 4.5 × 107 CFU/g of encapsulated LGG were found viable after incubation of microparticles 1 h in Simulated Gastric Fluid followed by 6 h in Simulated Intestinal Fluid, corresponding to less than 3 log reduction of viable cells during the 7 h incubation in Simulated Gastro Intestinal Fluid. These results attested that the developed encapsulation system is suitable for its use as a bacteria carrier for specific colonic delivery.

Development of uncoated near-spherical gold nanoparticles for the label-free quantification of Lactobacillus rhamnosus GG by surface-enhanced Raman spectroscopy.

The Surface-enhanced Raman spectroscopy (SERS) method based on gold nanoparticles as SERS substrate was investigated for the label-free detection and quantification of probiotic bacteria that are widely used in various pharmaceutical formulations. Indeed, the development of a simple and fast SERS method dedicated to the quantification of bacteria should be very useful for the characterization of such formulations in a more convenient way than the usually performed tedious and time-consuming conventional counting method. For this purpose, uncoated near-spherical gold nanoparticles were developed at room temperature by acidic treatment of star-like gold nanoparticle precursors. In this study, we first investigated the influence of acidic treatment conditions on both the nanoparticle physicochemical properties and SERS efficiency using Rhodamine 6G (R6G) as "model" analyte. Results highlighted that an effective R6G Raman signal enhancement was obtained by promoting chemical effect through R6G-anion interactions and by obtaining a suitable aggregation state of the nanoparticles. Depending on the nanoparticle synthesis conditions, R6G SERS signals were up to 102-103-fold greater than those obtained with star-like gold nanoparticles. The synthesized spherical gold nanoparticles were then successfully applied for the detection and quantification of Lactobacillus rhamnosus GG (LGG). In that case, the signal enhancement was especially due to the combination of anion-induced chemical enhancement and nanoparticle aggregation on LGG cell wall consecutive to non-specific interactions. Both the simplicity and speed of the procedure, achieved under 30 min, including nanoparticle synthesis, sample preparation, and acquisition of SERS spectra, appeared as very relevant for the characterization of pharmaceutical formulations incorporating probiotics. Graphical abstract.