A new process analytical technology soft sensor based on electrical tomography for real-time monitoring of multiphase systems.

BACKGROUND: Electrical tomography is widely recognized for its high time resolution and low cost. However, the implementation of electrical tomographic solutions has been hindered by the high computational overhead associated, which causes delays in the analysis, and numerical instability, that results in unclear reconstructed images. Therefore, it has been mostly applied offline, for qualitative tasks and with some delay. Applications requiring fast response times and quantification have been hindered or ruled out. RESULTS: In this article, we propose a new process analytical technology soft sensor that maps directly electrical tomography signals to the relevant parameter to be monitored. The data acquisition and estimation steps occur almost instantaneously, and the final accuracy is very good (R2 = 0,994). SIGNIFICANCE AND NOVELTY: The proposed methodology opens up good prospects for real-time quantitative applications. It was successfully tested on a pilot piping installation where the target property is the interface height between two immiscible fluids.

Evaluation of the Microbiological Effectiveness of Three Accessible Mask Decontamination Methods and Their Impact on Filtration, Air Permeability and Physicochemical Properties.

The need to secure public health and mitigate the environmental impact associated with the massified use of respiratory protective devices (RPD) has been raising awareness for the safe reuse of decontaminated masks by individuals and organizations. Among the decontamination treatments proposed, in this work, three methods with the potential to be adopted by households and organizations of different sizes were analysed: contact with nebulized hydrogen peroxide (H2O2); immersion in commercial bleach (NaClO) (sodium hypochlorite, 0.1% p/v); and contact with steam in microwave steam-sanitizing bags (steam bag). Their decontamination effectiveness was assessed using reference microorganisms following international standards (issued by ISO and FDA). Furthermore, the impact on filtration efficiency, air permeability and several physicochemical and structural characteristics of the masks, were evaluated for untreated masks and after 1, 5 and 10 cycles of treatment. Three types of RPD were analysed: surgical, KN95, and cloth masks. Results demonstrated that the H2O2 protocol sterilized KN95 and surgical masks (reduction of >6 log10 CFUs) and disinfected cloth masks (reduction of >3 log10 CFUs). The NaClO protocol sterilized surgical masks, and disinfected KN95 and cloth masks. Steam bags sterilized KN95 and disinfected surgical and cloth masks. No relevant impact was observed on filtration efficiency.

0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems.

Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.

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.

Presence of N-acetylneuraminic acid in the lung during postnatal development.

Sialic acids, particularly N-acetylneuraminic acid (Neu5Ac), are present as terminal components of rich and complex oligosaccharide chains, which are termed glycans, and are exhibited on the cell surfaces, especially on epithelial cells. Crucial in the 'social behavior' of the cell, sialic acids play vital roles in many physiological and pathological phenomena. The aim of the present study was to separate, identify, and quantify Neu5Ac in purified lung membranes from 4-, 14-, and 21-day-old animals, followed by the statistical analysis of these results with our previously reported data (0-day-old and adult results). Complementary, ultrastructural methodologies were used. The differences in the Neu5Ac values obtained across the examined postnatal-lung development relevant ages studied were found to be statistically significant. A substantial increase in the mean level of this compound was found during the period of 'bulk' alveolarization, which takes place from postnatal day 4 to 14 (P4-P14). The comparison of the mean levels of Neu5Ac, during microvascular maturation (mainly between P12 and P21), reveals that the difference, although statistically significant, is the least significant difference among all the pair-wise differences between the developmental stages. The presence of sub-terminal N-acetylgalactosamine (GalNAc)/Galactose (Gal) residues with terminal sialic acids on the bronchioloalveolar cell surfaces was confirmed using lung ultra-thin sections of adult and 0-day-old animals. These results showed that, although Neu5Ac levels increase throughout postnatal lung development, this sialic acid was substantially added to epithelial cell surfaces during the "bulk" alveolarization period, while its presence was less important during the microvascular maturation period. Bearing in mind that sialic acids are negatively charged and create charge repulsions between adjacent cells, we hypothesized that they can substantially contribute to postnatal alveolar formation and maturation.

Multi-target optimization of solid phase microextraction to analyse key flavour compounds in wort and beer.

Despite the literature comprises numerous studies dealing with the analysis of wort and beer flavour-related compounds by HS-SPME followed by GC-MS quantification, no generalized consensus exists regarding the optimal conditions for the extraction procedure. The complex chemistry nature of these matrices, the number of analytes, as well as the number and interactions among parameters affecting the extraction performance, requires the adoption of optimal experimental design protocols. This aspect is often overlooked and often not properly addressed in practice. Therefore, in the present work, the optimal conditions under which a range of wort and beer analytes can be extracted and quantified were analysed. The optimal extraction conditions were presented at two levels of aggregation: global (untargeted) and key-flavour analysis. Experimental data was generated by Definitive-Screening-Design, followed by model development and optimization. Both approaches were compared and critically analysed. For vicinal-diketones group, a complete validation study for the optimal conditions is presented.

Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives.

Quality by Design (QbD) was originated in the broad domain of Quality Management and was recently adapted and formalized in specific terms for assisting pharmaceutical companies efforts towards market and operational excellence. However, despite some impressive success stories, the pharmaceutical industry have not yet fully embraced QbD, particularly in routine commercial manufacturing (Rantanen and Khinast, 2015; Puñal Peces et al., 2016). In this review, we aim to analyse the current state of implementation of QbD methodologies and tools in the pharmaceutical industry, extracting patterns and trends and identifying gaps and opportunities that may be considered to improve QbD adoption. For this purpose, a critical analysis of 60 research papers was performed, whose contents were classified, compared and summarized at different abstraction levels. Our analysis reveals the following tools as the frequently adopted for conducting each activity: Risk Assessment (RA) - Ishikawa Diagram, Failure Mode and Effects Analysis (FMEA) and Risk Estimation Matrix (REM); Screening Design of Experiments (DoE) - 2-level Full and Fractional Factorial Designs; Optimisation DoE - Central Composite Design (CCD). Emerging trends include the growing interest in quantifying and managing the impact of raw materials' attributes variability on process and product, as well as the development of Retrospective QbD approaches (rQbD) in complement to standard QbD.

Advanced predictive methods for wine age prediction: Part II - A comparison study of multiblock regression approaches.

In this article, we extend the scope of the first paper of the sequel, which was dedicated to the analysis of advanced single-block regression methods (Rendall et al., 2016) [1], to the class of multiblock regression approaches. The datasets contemplated for developing the multiblock approaches are the same as in the former publication: volatile, polyphenols, organic acids composition and the UV-Vis spectra. The context is still the prediction of the ageing time of one of finest Portuguese fortified wines, the Madeira Wine, but now the data collected from the different analytical sources is explored simultaneously, in a more structured and informative way, through multiblock methodologies. The goal of this paper is to provide a critical assessment of a rich variety of multiblock regression methods, namely: Concatenated PLS, Multiblock PLS (MBPLS), Hierarchical PLS (HPLS), Network-Induced Supervised Learning (NI-SL) and Sequential Orthogonalised Partial Least Squares (SO-PLS). A comparison of block scaling methods was also undertaken for the Concatenated PLS algorithm, and new block scaling methods were proposed that led to better prediction performances. This study explores and reveals the potential advantages of applying multiblock methods for fusing datasets from different sources, from both the predictive and interpretability perspectives.

Advanced predictive methods for wine age prediction: Part I - A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods.

In this paper we test and compare advanced predictive approaches for estimating wine age in the context of the production of a high quality fortified wine - Madeira Wine. We consider four different data sets, namely, volatile, polyphenols, organic acids and the UV-vis spectra. Each one of these data sets contain chemical information of a different nature and present diverse data structures, namely a different dimensionality, level of collinearity and degree of sparsity. These different aspects may imply the use of different modelling approaches in order to better explore the data set's information content, namely their predictive potential for wine age. This happens to be so, because different regression methods have different prior assumptions regarding the predictors, response variable(s) and the data generating mechanism, which may or may not find good adherence to the case study under analysis. In order to cover a wide range of modelling domains, we have incorporated in this work methods belonging to four very distinct classes of approaches that cover most applications found in practice: linear regression with variable selection, penalized regression, latent variables regression and tree-based ensemble methods. We have also developed a rigorous comparison framework based on a double Monte Carlo cross-validation scheme, in order to perform the relative assessment of the performance of the various methods. Upon comparison, models built using the polyphenols and volatile composition data sets led to better wine age predictions, showing lower errors under testing conditions. Furthermore, the results obtained for the polyphenols data set suggest a more sparse structure that can be further explored in order to reduce the number of measured variables. In terms of regression methods, tree-based methods, and boosted regression trees in particular, presented the best results for the polyphenols, volatile and the organic acid data sets, suggesting a possible presence of a nonlinear relationship between predictors and response. Regarding the UV-vis data set, penalized regression methods (ridge regression, LASSO and elastic nets) presented the best results, albeit methods such as partial least squares (PLS) or principal component regression (PCR) are often the practitioners' preferred choice.

Assessment of co-composting process with high load of an inorganic industrial waste.

This study aims to investigate the co-composting of an inorganic industrial waste (eggshell - ES) in very high levels (up to 60% w/w). Since composting is a process in which solid, liquid and gaseous phases interact in a very complex way, there is a need to shed light on statistical tools that can unravel the main relationships structuring the variability associated to this process. In this study, PCA and data visualisation were used with that purpose. The co-composting tests were designed with increasing quantities of ES (0, 10, 20, 30 and 60%ES w/w) mixed with industrial potato peel and rice husks. Principal component analysis showed that physical properties like free air space, bulk density and moisture are the most relevant variables for explaining the variability due to ES content. On the other hand, variability in time dynamics is mostly driven by some chemical and phytoxicological parameters, such as organic matter decay and nitrate content. Higher ES incorporation (60% ES) enhanced the initial biological activity of the mixture, but the higher bulk density and lower water holding capacity had a negative effect on the aerobic biological activity as the process evolved. Nevertheless, pathogen-killing temperatures (>70°C for 11h) were attained. All the final products obtained after 90days were stable and non-phytotoxic. This work proved that valorisation of high amounts of eggshell by co-composting is feasible, but prone to be influenced by the physical properties of the mixtures.

Optimal design of experiments applied to headspace solid phase microextraction for the quantification of vicinal diketones in beer through gas chromatography-mass spectrometric detection.

Vicinal diketones, namely diacetyl (DC) and pentanedione (PN), are compounds naturally found in beer that play a key role in the definition of its aroma. In lager beer, they are responsible for off-flavors (buttery flavor) and therefore their presence and quantification is of paramount importance to beer producers. Aiming at developing an accurate quantitative monitoring scheme to follow these off-flavor compounds during beer production and in the final product, the head space solid-phase microextraction (HS-SPME) analytical procedure was tuned through experiments planned in an optimal way and the final settings were fully validated. Optimal design of experiments (O-DOE) is a computational, statistically-oriented approach for designing experiences that are most informative according to a well-defined criterion. This methodology was applied for HS-SPME optimization, leading to the following optimal extraction conditions for the quantification of VDK: use a CAR/PDMS fiber, 5 ml of samples in 20 ml vial, 5 min of pre-incubation time followed by 25 min of extraction at 30 °C, with agitation. The validation of the final analytical methodology was performed using a matrix-matched calibration, in order to minimize matrix effects. The following key features were obtained: linearity (R(2) > 0.999, both for diacetyl and 2,3-pentanedione), high sensitivity (LOD of 0.92 µg L(-1) and 2.80 µg L(-1), and LOQ of 3.30 µg L(-1) and 10.01 µg L(-1), for diacetyl and 2,3-pentanedione, respectively), recoveries of approximately 100% and suitable precision (repeatability and reproducibility lower than 3% and 7.5%, respectively). The applicability of the methodology was fully confirmed through an independent analysis of several beer samples, with analyte concentrations ranging from 4 to 200 g L(-1).

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.