Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented

Nature-Inspired Adaptive Architecture for Soft Sensor Modelling

This paper gives a general overview of the challenges present in the research field of Soft Sensor building and proposes a novel architecture for building of Soft Sensors, which copes with the identified challenges. The architecture is inspired and making use of nature-related techniques for computational intelligence. Another aspect, which is addressed by the proposed architecture, are the identified characteristics of the process industry data. The data recorded in the process industry consist usually of certain amount of missing values or sample exceeding meaningful values of the measurements, called data outliers. Other process industry data properties causing problems for the modelling are the collinearity of the data, drifting data and the different sampling rates of the particular hardware sensors. It is these characteristics which are the source of the need for an adaptive behaviour of Soft Sensors. The architecture reflects this need and provides mechanisms for the adaptation and evolution of the Soft Sensor at different levels. The adaptation capabilities are provided by maintaining a variety of rather simple models. These particular models, called paths in terms of the architecture, can for example focus on different partition of the input data space, or provide different adaptation speeds to changes in the data. The actual modelling techniques involved into the architecture are data-driven computational learning approaches like artificial neural networks, principal component regression, etc

Non destructive monitoring of the yoghurt fermentation phase by an image analysis of laser-diffraction patterns: Characterization of cow s, goat s and sheep s milk

[EN] Monitoring yogurt fermentation by the image analysis of diffraction patterns generated by the laser-milk interaction was explored. Cow¿s, goat¿s and sheep¿s milks were tested. Destructive physico-chemical analyses were done after capturing images during the processes to study the relationships between data blocks. Information from images was explored by applying a spectral phasor from which regions of interest were determined in each image channel. The histograms of frequencies from each region were extracted, which showed evolution according to textural modifications. Examining the image data by multivariate analyses allowed us to know that the captured variance from the diffraction patterns affected both milk type and texture changes. When regression studies were performed to model the physico-chemical parameters, satisfactory quantifications were obtained (from R2¿=¿0.82 to 0.99) for each milk type and for a hybrid model that included them all. This proved that the studied patterns had a common fraction of variance during this processing, independently of milk type.Verdú Amat, S.; Barat Baviera, JM.; Grau Meló, R. (2019). Non destructive monitoring of the yoghurt fermentation phase by an image analysis of laser-diffraction patterns: Characterization of cow s, goat s and sheep s milk. Food Chemistry. 274:46-54. https://doi.org/10.1016/j.foodchem.2018.08.091S465427

Electronic sensor technologies in monitoring quality of tea: A review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea

Multivariate statistical data analysis of cell-free protein synthesis toward monitoring and control

The optimization and control of cell free protein synthesis (CFPS) presents an ongoing challenge due to the complex synergies and nonlinearities that cannot be fully explained in first principle models. This article explores the use of multivariate statistical tools for analyzing data sets collected from the CFPS of Cereulide monoclonal antibodies. During the collection of these data sets, several of the process parameters were modified to investigate their effect on the end‐point product (yield). Through the application of principal component analysis and partial least squares (PLS), important correlations in the process could be identified. For example, yield had a positive correlation with pH and NH3 and a negative correlation with CO2 and dissolved oxygen. It was also found that PLS was able to provide a long‐term prediction of product yield. The presented work illustrates that multivariate statistical techniques provide important insights that can help support the operation and control of CFPS processes

Fourier transform infrared spectroscopy, a powerful tool to monitor biopharmaceuticals production

A Escherichia coli é o microorganismo mais usado como hospedeiro para a produção de produtos recombinantes, tais como plasmídeos usados para terapia génica e vacinação de ADN. Desta forma, torna-se importante compreender as relações metabólicas complexas e a bioprodução de plasmídeo, que ocorre em ambientes de cultura dinâmicos, a fim de controlar e optimizar o desempenho do sistema de expressão recombinante. O objectivo principal deste trabalho consiste em avaliar a potencialidade da espectroscopia FT-IR para monitorizar e caracterizar a produção do plasmídeo pVAX-LacZ em culturas recombinantes de E. coli, nomeadamente para extrair informação relacionada com as variáveis críticas (biomassa, plasmídeo, fontes de carbono e acetato) e informação metabólica da célula hospedeira E. coli. Para tal, culturas de E. coli com diferentes concentrações de glucose e glicerol e diferentes estratégias de cultivo (batch e fed-batch) foram monitorizadas por espectroscopia de infravermelho perto (NIR) e de infravermelho médio (MIR). Tanto a espectroscopia NIR com a MIR permitiram extrair informação sobre as variáveis críticas do bioprocesso, através da construção de modelos de regressão por mínimos quadrados parciais, que resultaram em elevados coeficientes de regressão e baixos erros de previsão. A abordagem NIR apresenta a vantagem de aquisição em tempo real das variáveis do bioprocesso, já a abordagem MIR permite a leitura simultânea de centenas de amostras de várias culturas ao mesmo tempo através do uso multi-microplacas, sendo muito vantajosa nos casos de micro-bioreactores usados para optimização. Para além disso, como os espectros MIR apresentam mais informação do que os espectros NIR, uma vez que representam os modos de vibração fundamentais das biomoléculas, enquanto que os espectros NIR representam sobreposições e combinações de vibrações, os dados espectrais MIR também permitiram a aquisição de informação bioquímica ao longo das culturas de E. coli a partir da análise das componentes principais (PCA) bem como do estudo das características bioquímicas, tais como as reservas de glicogénio e os níveis de transcrição aparente. Portanto, a espectroscopia FT-IR apresenta assim características relevantes para a compreensão e monitorização do processo de produção de culturas recombinantes, sendo, de acordo com Quality-by-Design e Process Analytical Technology, muito importante para fins de controlo e optimização.Escherichia coli is the most used microorganism as host for the production of recombinant products, such as plasmids used for gene therapy and DNA vaccination. Therefore, it is important to understand the complex metabolic relationships and the plasmid bioproduction process occurring in dynamic culture environments, in order to control and optimize the performance of the recombinant expression system. The main goal of this work is to evaluate the potential of Fourier Transform Infrared (FT-IR) spectroscopy to monitor and characterize recombinant E. coli cultures producing the plasmid model pVAX-LacZ, namely to extract information concerning the critical variables (biomass, plasmid, carbon sources and the by-product acetate) and metabolic information regarding the host E. coli. To achieve that cultures of E. coli conducted with different mixture of glucose and glycerol and different cultivation strategies (batch and fed-batch) were monitored in-situ by a fiber optic probe in near- infrared (NIR) and of the cell pellets in at-line in high-throughput mode by mid-infrared (MIR) spectroscopy. Both NIR and MIR spectroscopy setup enabled to extract information regarding the critical variables of the bioprocess by the implementation of partial least square regression models that result in high regression coefficients and low prediction errors. The NIR setup presents the advantage of acquiring in real time the knowledge of the bioprocess variables, where the at-line measurements with the MIR setup presents more advantageous in cases of micro-bioreactors used in optimization protocols, enabling the simultaneously information acquisition of hundreds samples by using multi-microplates. Furthermore, as the MIR spectra presents more information than the NIR spectra, since it represents the fundamental vibration modes of biomolecules while the NIR spectra represents overtones and combinations of vibrations, the MIR data also enabled to acquire biochemical information along the E. coli cultures as pointed out in an principal component analysis and by the estimation of biochemical features as glycogen reserves and apparent transcriptional levels. Therefore, FT-IR spectroscopy presents relevant features towards the understanding and monitoring of the production process of recombinant cultures for control and optimization purposes, in according to the Quality-by-Design and the Process Analytical Technology

Effects of FODMAPs and gluten on irritable bowel syndrome- from self-reported symptoms to molecular profiling

Irritable bowel syndrome (IBS) is a complex disorder of gut-brain interactions. The diagnosis of IBS is based on subjective reporting of abdominal pain and altered bowel habits in the absence of any clinical alterations of the gut or other pathological conditions. Dietary regimens for symptom management include a low fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) diet and a gluten-free diet. However, scientific evidence supporting these dietary recommendations for managing IBS symptoms is weak: trials have been non-blinded and underpowered. While mechanistic understanding and objective markers of response remain scarce. Therefore, the aim of this thesis was to conduct a large double-blind study to investigate the effect of FODMAPs and gluten on symptomatic and molecular data including 16S rRNA analysis of the gut microbiota and metabolomics analyses, both at a group and subgroup (differential response) level. The resulting data served also to assess the accuracy of the Bristol Stool Form Scale (BSFS) used in IBS subtype diagnosis, and thus overcome the lack of objective evaluation of IBS symptoms.Trial data revealed that gluten caused no symptoms and FODMAPs triggered only modest symptoms of IBS, albeit with large inter-individual differences. Subjective reporting according to the BSFS conformed only modestly with stool water content in IBS, warranting caution towards IBS subtyping. FODMAPs increased saccharolytic microbial genera, phenolic-derived metabolites and 3-indolepropionate, but decreased bile acids. The genera Agathobacter, Anaerostipes, Fusicatenibacter, and Bifidobacterium correlated with increased plasma concentrations of phenolic-derived metabolites and 3-indolepropionate, i.e, metabolites related to decreased risk of incident type 2 diabetes and inflammation. Indeed, among FODMAP-related metabolites, only weak correlations to IBS symptoms were detected, as in the case of 3-indolepropionate to abdominal pain and interference with quality of life, warranting further investigation. Gluten displayed a modest effect on metabolites involved in lipid metabolism, including carnitine derivates, an acyl-CoA derivate, a medium-chain fatty acid, and an unknown lipid, but with no interpretable link to health.No molecular markers of a differential response were found, despite a comprehensive exploration with multiple analytical approaches. This could be explained by the absence of baseline variables, such as other omics layers or psychological factors, that could have determined the difference. In summary, the results indicate that gluten does not cause IBS symptoms. Moreover, the minor effect of FODMAPs on IBS symptoms must be weighed against their potential beneficial health effects. While the complexity of IBS likely explains the absence of molecular evidence for differential responses, such data analytical approach has potential where clear benefits of dietary interventions exist. Finally, the use of BSFS should include training for self-assessment, as a tool for subtyping IBS

Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen