Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling.

The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions.

Neurophysiological Changes in the First Year After Cell Transplantation in Sub-acute Complete Paraplegia.

Neurophysiological testing can provide quantitative information about motor, sensory, and autonomic system connectivity following spinal cord injury (SCI). The clinical examination may be insufficiently sensitive and specific to reveal evolving changes in neural circuits after severe injury. Neurophysiologic data may provide otherwise imperceptible circuit information that has rarely been acquired in biologics clinical trials in SCI. We reported a Phase 1 study of autologous purified Schwann cell suspension transplantation into the injury epicenter of participants with complete subacute thoracic SCI, observing no clinical improvements. Here, we report longitudinal electrophysiological assessments conducted during the trial. Six participants underwent neurophysiology screening pre-transplantation with three post-transplantation neurophysiological assessments, focused on the thoracoabdominal region and lower limbs, including MEPs, SSEPs, voluntarily triggered EMG, and changes in GSR. We found several notable signals not detectable by clinical exam. In all six participants, thoracoabdominal motor connectivity was detected below the clinically assigned neurological level defined by sensory preservation. Additionally, small voluntary activations of leg and foot muscles or positive lower extremity MEPs were detected in all participants. Voluntary EMG was most sensitive to detect leg motor function. The recorded MEP amplitudes and latencies indicated a more caudal thoracic level above which amplitude recovery over time was observed. In contrast, further below, amplitudes showed less improvement, and latencies were increased. Intercostal spasms observed with EMG may also indicate this thoracic "motor level." Galvanic skin testing revealed autonomic dysfunction in the hands above the injury levels. As an open-label study, we can establish no clear link between these observations and cell transplantation. This neurophysiological characterization may be of value to detect therapeutic effects in future controlled studies.

Development of a fast and reliable method for long- and short-term wine age prediction.

Wine age prediction based on its intrinsic characteristics can provide significant assistance to oenologists' quality evaluations, concerning wine ageing process control and wine quality assurance. Simpler, faster, cheaper and affordable analytical procedures would be greatly welcome to establish such a practice. In this study, we present a new and reliable strategy to predict wine age, in the long and short-term, centered on the use of wine UV-vis absorbance data, coupled with proper chemometric techniques. The strategy followed consists essentially in first pre-processing the UV-vis data, secondly to carry out variable selection over such pre-processed data sets, and finally to use the set of selected variables for developing a PLS model focused on wine age prediction. We tested different data pre-processing methodologies, namely first and second derivatives, multiplicative scatter correction, standard normal variate and orthogonal signal correction, as well as different variable selection approaches, specifically interval partial least squares, VIPS, genetic algorithms and the wavelet transformation combined with a genetic algorithm. In both case studies, regarding long and short-term ageing periods, we have found out that it is indeed possible to predict wine ages, in our case Madeira wine ages, with an accuracy of 1.4 years for longer ageing periods, and of 3 months for wines of an age comprised in the first two years of ageing. The genetic algorithm revealed to be very useful for proper wavelet coefficients selection, leading to the most parsimonious model among all those analyzed, which also presents the best predictive performance found.

Environmental monitoring study of linear alkylbenzene sulfonates and insoluble soap in Spanish sewage sludge samples.

In this work we present a monitoring study of linear alkylbenzene sulfonates (LAS) and insoluble soap performed on Spanish sewage sludge samples. This work focuses on finding statistical relations between LAS concentrations and insoluble soap in sewage sludge samples and variables related to wastewater treatment plants such as water hardness, population and treatment type. It is worth to mention that 38 samples, collected from different Spanish regions, were studied. The statistical tool we used was Principal Component Analysis (PC), in order to reduce the number of response variables. The analysis of variance (ANOVA) test and a non-parametric test such as the Kruskal-Wallis test were also studied through the estimation of the p-value (probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true) in order to study possible relations between the concentration of both analytes and the rest of variables. We also compared LAS and insoluble soap behaviors. In addition, the results obtained for LAS (mean value) were compared with the limit value proposed by the future Directive entitled "Working Document on Sludge". According to the results, the mean obtained for soap and LAS was 26.49 g kg(-1) and 6.15 g kg(-1) respectively. It is worth noting that LAS mean was significantly higher than the limit value (2.6 g kg(-1)). In addition, LAS and soap concentrations depend largely on water hardness. However, only LAS concentration depends on treatment type.

Aroma ageing trends in GC/MS profiles of liqueur wines.

Madeira wine has been studied with the main goal of acquiring a better understanding about the evolution of its properties over time. For that purpose, flexible and reliable data analysis tools were employed to characterize wines at different ageing stages, using flavour chromatography measurements. In this paper we present the results from such a study, where the main differences in the aroma profiles and their development in different types of aged Madeira wines are analyzed and evaluated according to their discriminating power. An exploratory multivariate data analysis was conducted using two different tools, namely biplots and contributions plots obtained through principal component analysis (PCA). In order to take advantage of the maximum amount of information provided by the chromatography data sets, a new approach that incorporates samples variability in the analysis of the statistical significance of contributions estimates, was developed and tested. In this way, it was possible to analyze which volatile compounds have statistically significant and/or similar contributions regarding the observed separation of wine samples from different groups. Furthermore, since several chemical compounds are expected to change together as a result of the ageing-related chemical reactions, they were clustered according to a similarity criterion relative to their importance in the trends observed in the scores space. Results obtained provide a sound basis for the differentiation and characterization of the ageing process followed by Madeira wines.

Analysis and assessment of Madeira wine ageing over an extended time period through GC-MS and chemometric analysis.

Wine is one of the world's higher value agricultural products. The present work is centred on Madeira wine, a fine and prestigious example among Portuguese liqueur wines,with the main goal to deepen our understanding of relevant phenomena going on during the winemaking process, in particular during ageing of "Malmsey" Madeira wine. In this paper we present the results obtained from the chemical characterization of how its aroma composition evolves during ageing, and the development of a robust framework for analyzing the identity of aged Madeira wines. An extended ageing period was considered, covering a time frame of twenty years, from which several samples were analyzed in terms of their aromatic composition. The multivariate structure of this chemical information was then processed through multivariate statistical feature extraction techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), in order to identify the relevant patterns corresponding to trends associated with wine ageing. Classification methodologies for age prediction were developed, using data from the lower dimensional sub-spaces obtained after projecting the original data to the latent variable spaces provided by PCA or PLS-DA. Finally, the performance for each classification methodology developed was evaluated according to their error rates using cross-validation methodologies (Leave-One-Out and k-fold Monte Carlo). Results obtained so far show that quite interesting classification performances can indeed be achieved, despite the natural variability present in wine products. These results also provide solid bases which can be used to build up available frameworks which assist quality monitoring and identity assurance tasks.

A theoretical model for transdermal drug delivery from emulsions and its dependence upon formulation.

This article presents a theoretical model of transdermal drug delivery from an emulsion-type vehicle that addresses the vehicle heterogeneity and incorporates the prediction of drug transport parameters as function of the vehicle composition. The basic mass transfer model considers interfacial and diffusion resistances within the emulsion and partition/diffusion phenomena across two skin compartments in series. Drug transport parameters are predicted as follows: partition coefficients are derived from regular solutions theory, drug diffusivity in the continuous phase is computed from a free volume theory with segmental motion, and permeability of the surfactant layer around droplets is estimated based on a free surface area model. These relationships are incorporated within the basic mass transfer model, so that the overall model is able to predict temporal profiles of drug release from the vehicle and of drug concentration in plasma, as a function of vehicle composition. In this way, the proposed model provides a sound physicochemical basis to support the development of new formulations and the planning of experiments. A simulated case study regarding a nitroglycerin ointment is presented in detail, illustrating how thermodynamic and kinetic factors inherent to the emulsion vehicle can modulate drug release and subsequent systemic absorption.