Water quality monitoring is crucial globally due to the vital role of freshwater in providing drinking water, irrigation, and ecosystem services. Highly polluted water poses risks to both ecosystems and human health. Current water quality monitoring methods deployed in the field are often expensive, labor-intensive, and invasive. To overcome these issues, this degree project investigated the use of remote sensing to assess critical water quality parameters in the Swedish river Indalsälven. The research questions focus on determining the accuracy of predicting chemical oxygen demand (COD), river color, turbidity, and total phosphorus (TP) using satellite data and machine learning algorithms. The findings revealed that COD can be predicted with a cross-validated coefficient of determination (R²CV) of 0.7, indicating a robust predictive capability. The study suggests that while approximate quantitative prediction of COD in oligotrophic rivers is feasible using Sentinel-2 imagery, predictions for the other parameters remain challenging in the context of Indalsälven. Improvements in prediction accuracy were achieved through optimized band combinations, reduced datasets encompassing satellite data collected within two days of field measurements, and suitable pre-processing methods. / Airborne Monitoring of Water Quality in Remote Regions

The present Ph.D. thesis is devoted to study, develop and apply approaches commonly used in chemometrics to the emerging field of systems biology. Existing procedures and new methods are applied to solve research and industrial questions in different multidisciplinary teams. The methodologies developed in this document will enrich the plethora of procedures employed within omic sciences to understand biological organisms and will improve processes in biotechnological industries integrating biological knowledge at different levels and exploiting the software packages derived from the thesis. This dissertation is structured in four parts. The first block describes the framework in which the contributions presented here are based. The objectives of the two research projects related to this thesis are highlighted and the specific topics addressed in this document via conference presentations and research articles are introduced. A comprehensive description of omic sciences and their relationships within the systems biology paradigm is given in this part, jointly with a review of the most applied multivariate methods in chemometrics, on which the novel approaches proposed here are founded. The second part addresses many problems of data understanding within metabolomics, fluxomics, proteomics and genomics. Different alternatives are proposed in this block to understand flux data in steady state conditions. Some are based on applications of multivariate methods previously applied in other chemometrics areas. Others are novel approaches based on a bilinear decomposition using elemental metabolic pathways, from which a GNU licensed toolbox is made freely available for the scientific community. As well, a framework for metabolic data understanding is proposed for non-steady state data, using the same bilinear decomposition proposed for steady state data, but modelling the dynamics of the experiments using novel two and three-way data analysis procedures. Also, the relationships between different omic levels are assessed in this part integrating different sources of information of plant viruses in data fusion models. Finally, an example of interaction between organisms, oranges and fungi, is studied via multivariate image analysis techniques, with future application in food industries. The third block of this thesis is a thoroughly study of different missing data problems related to chemometrics, systems biology and industrial bioprocesses. In the theoretical chapters of this part, new algorithms to obtain multivariate exploratory and regression models in the presence of missing data are proposed, which serve also as preprocessing steps of any other methodology used by practitioners. Regarding applications, this block explores the reconstruction of networks in omic sciences when missing and faulty measurements appear in databases, and how calibration models between near infrared instruments can be transferred, avoiding costs and time-consuming full recalibrations in bioindustries and research laboratories. Finally, another software package, including a graphical user interface, is made freely available for missing data imputation purposes. The last part discusses the relevance of this dissertation for research and biotechnology, including proposals deserving future research. / Esta tesis doctoral se centra en el estudio, desarrollo y aplicación de técnicas quimiométricas en el emergente campo de la biología de sistemas. Procedimientos comúnmente utilizados y métodos nuevos se aplican para resolver preguntas de investigación en distintos equipos multidisciplinares, tanto del ámbito académico como del industrial. Las metodologías desarrolladas en este documento enriquecen la plétora de técnicas utilizadas en las ciencias ómicas para entender el funcionamiento de organismos biológicos y mejoran los procesos en la industria biotecnológica, integrando conocimiento biológico a diferentes niveles y explotando los paquetes de software derivados de esta tesis. Esta disertación se estructura en cuatro partes. El primer bloque describe el marco en el cual se articulan las contribuciones aquí presentadas. En él se esbozan los objetivos de los dos proyectos de investigación relacionados con esta tesis. Asimismo, se introducen los temas específicos desarrollados en este documento mediante presentaciones en conferencias y artículos de investigación. En esta parte figura una descripción exhaustiva de las ciencias ómicas y sus interrelaciones en el paradigma de la biología de sistemas, junto con una revisión de los métodos multivariantes más aplicados en quimiometría, que suponen las pilares sobre los que se asientan los nuevos procedimientos aquí propuestos. La segunda parte se centra en resolver problemas dentro de metabolómica, fluxómica, proteómica y genómica a partir del análisis de datos. Para ello se proponen varias alternativas para comprender a grandes rasgos los datos de flujos metabólicos en estado estacionario. Algunas de ellas están basadas en la aplicación de métodos multivariantes propuestos con anterioridad, mientras que otras son técnicas nuevas basadas en descomposiciones bilineales utilizando rutas metabólicas elementales. A partir de éstas se ha desarrollado software de libre acceso para la comunidad científica. A su vez, en esta tesis se propone un marco para analizar datos metabólicos en estado no estacionario. Para ello se adapta el enfoque tradicional para sistemas en estado estacionario, modelando las dinámicas de los experimentos empleando análisis de datos de dos y tres vías. En esta parte de la tesis también se establecen relaciones entre los distintos niveles ómicos, integrando diferentes fuentes de información en modelos de fusión de datos. Finalmente, se estudia la interacción entre organismos, como naranjas y hongos, mediante el análisis multivariante de imágenes, con futuras aplicaciones a la industria alimentaria. El tercer bloque de esta tesis representa un estudio a fondo de diferentes problemas relacionados con datos faltantes en quimiometría, biología de sistemas y en la industria de bioprocesos. En los capítulos más teóricos de esta parte, se proponen nuevos algoritmos para ajustar modelos multivariantes, tanto exploratorios como de regresión, en presencia de datos faltantes. Estos algoritmos sirven además como estrategias de preprocesado de los datos antes del uso de cualquier otro método. Respecto a las aplicaciones, en este bloque se explora la reconstrucción de redes en ciencias ómicas cuando aparecen valores faltantes o atípicos en las bases de datos. Una segunda aplicación de esta parte es la transferencia de modelos de calibración entre instrumentos de infrarrojo cercano, evitando así costosas re-calibraciones en bioindustrias y laboratorios de investigación. Finalmente, se propone un paquete software que incluye una interfaz amigable, disponible de forma gratuita para imputación de datos faltantes. En la última parte, se discuten los aspectos más relevantes de esta tesis para la investigación y la biotecnología, incluyendo líneas futuras de trabajo. / Aquesta tesi doctoral es centra en l'estudi, desenvolupament, i aplicació de tècniques quimiomètriques en l'emergent camp de la biologia de sistemes. Procediments comúnment utilizats i mètodes nous s'apliquen per a resoldre preguntes d'investigació en diferents equips multidisciplinars, tant en l'àmbit acadèmic com en l'industrial. Les metodologies desenvolupades en aquest document enriquixen la plétora de tècniques utilitzades en les ciències òmiques per a entendre el funcionament d'organismes biològics i milloren els processos en la indústria biotecnològica, integrant coneixement biològic a distints nivells i explotant els paquets de software derivats d'aquesta tesi. Aquesta dissertació s'estructura en quatre parts. El primer bloc descriu el marc en el qual s'articulen les contribucions ací presentades. En ell s'esbossen els objectius dels dos projectes d'investigació relacionats amb aquesta tesi. Així mateix, s'introduixen els temes específics desenvolupats en aquest document mitjançant presentacions en conferències i articles d'investigació. En aquesta part figura una descripació exhaustiva de les ciències òmiques i les seues interrelacions en el paradigma de la biologia de sistemes, junt amb una revisió dels mètodes multivariants més aplicats en quimiometria, que supossen els pilars sobre els quals s'assenten els nous procediments ací proposats. La segona part es centra en resoldre problemes dins de la metabolòmica, fluxòmica, proteòmica i genòmica a partir de l'anàlisi de dades. Per a això es proposen diverses alternatives per a compendre a grans trets les dades de fluxos metabòlics en estat estacionari. Algunes d'elles estàn basades en l'aplicació de mètodes multivariants propostos amb anterioritat, mentre que altres són tècniques noves basades en descomposicions bilineals utilizant rutes metabòliques elementals. A partir d'aquestes s'ha desenvolupat software de lliure accés per a la comunitat científica. Al seu torn, en aquesta tesi es proposa un marc per a analitzar dades metabòliques en estat no estacionari. Per a això s'adapta l'enfocament tradicional per a sistemes en estat estacionari, modelant les dinàmiques dels experiments utilizant anàlisi de dades de dues i tres vies. En aquesta part de la tesi també s'establixen relacions entre els distints nivells òmics, integrant diferents fonts d'informació en models de fusió de dades. Finalment, s'estudia la interacció entre organismes, com taronges i fongs, mitjançant l'anàlisi multivariant d'imatges, amb futures aplicacions a la indústria alimentària. El tercer bloc d'aquesta tesi representa un estudi a fons de diferents problemes relacionats amb dades faltants en quimiometria, biologia de sistemes i en la indústria de bioprocessos. En els capítols més teòrics d'aquesta part, es proposen nous algoritmes per a ajustar models multivariants, tant exploratoris com de regressió, en presencia de dades faltants. Aquests algoritmes servixen ademés com a estratègies de preprocessat de dades abans de l'ús de qualsevol altre mètode. Respecte a les aplicacions, en aquest bloc s'explora la reconstrucció de xarxes en ciències òmiques quan apareixen valors faltants o atípics en les bases de dades. Una segona aplicació d'aquesta part es la transferència de models de calibració entre instruments d'infrarroig proper, evitant així costoses re-calibracions en bioindústries i laboratoris d'investigació. Finalment, es proposa un paquet software que inclou una interfície amigable, disponible de forma gratuïta per a imputació de dades faltants. En l'última part, es discutixen els aspectes més rellevants d'aquesta tesi per a la investigació i la biotecnologia, incloent línies futures de treball. / Folch Fortuny, A. (2016). Chemometric Approaches for Systems Biology [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/77148 / Premios Extraordinarios de tesis doctorales

In an era of increasingly complex data, the balance between detailed information presentation and maintaining an overview during visualisation is crucial. This thesis investigates the application of the ´magic lens' technique to network visualisations, specifically focusing on visualising dynamic network attributes such as temporal node changes and evolving network topology. Traditional network visualisation methods often struggle to reveal underlying multivariate characteristics, necessitating more innovative approaches like focus plus context visualisations. The lens is also to be used for comparison between different parts of the network, where multivariate node attributes and dynamic network topology is integrated. Our research aims to enhance the usability and effectiveness of network visualisations by integrating a magic lens for dynamic data exploration. This study re-implements and extends the Network Lens tool using modern web technologies (Cytoscape.js and D3.js) and conducts a user study to evaluate its design and functionality. The research questions address essential design requirements for a comparison lens, methods for integrating temporal network comparisons within the magic lens, and user preference for different visual representations. Through careful study and implementation, we identify key design requirements for effective comparison lenses, including intuitive node selection, multi-selection comparison, and longitudinal observation capabilities. The dynamic network topology was visualised using a timeline representation, positioned adjacent to the main lens to maintain clarity and ease of use. User evaluations highlighted the practicality of dual-lens setups for side-by-side comparison, although initial usability of the dynamic display suggests room for refinement. Three visual representations - bar charts, parallel coordinates plots, and star plots - were integrated within the magic lens to enhance multivariate data exploration. User feedback indicated a preference for bar charts due to their straightforward interpretation, despite their limitations in displaying small relative values. Parallel coordinates plot were favoured at second place for their ability to maintain visual clarity across a range of values, while star plots, though less preferred, were recognised for their potential in displaying numerous dimensions. Future work should extend the tool's application to real-world datasets, incorporate dynamic nominal data, and explore alternative dynamic display methods. Broader user studies across various domains will further validate andrefine the tool, ensuring its effectiveness and adaptability for diverse data visualisation needs. / I en tid av allt mer komplex data är balansen mellan detaljerad informationspresentation och upprätthållande av överblick under visualisering avgörande. Detta projekt undersöker tillämpningen av den "magiska linsen"-tekniken för nätverksvisualiseringar, speciellt med fokus på visualisering av dynamiska nätverksattribut såsom temporala nodförändringar och förändrande nätverkstopologi. Traditionella nätverksvisualiseringsmetoder har ofta besvär för att avslöja underliggande multivariata egenskaper, vilket kräver mer innovativa tillvägagångssätt som fokus och kontextvisualiseringar. Linsen ska också användas för jämförelse mellan olika delar av nätverket, där multivariata nodattribut och dynamisk nätverkstopologi är integrerade. Vår forskning syftar till att förbättra användbarheten och effektiviteten av nätverksvisualiseringar genom att integrera en magisk lins för dynamisk datautforskning. Den här studien omimplementerar och utökar verktyget Network Lens med modern webbteknologi (Cytoscape.js och D3.js) och genomför en användarstudie för att utvärdera dess design och funktionalitet. Forskningsfrågorna tar upp väsentliga designkrav för en jämförelselins, metoder för att integrera tidsmässiga nätverksjämförelser inom den magiska linsen och användarpreferenser för olika visuella representationer. Genom noggranna studier och implementering identifierar vi nyckeldesignkrav för effektiva jämförelselinser, inklusive intuitivt nodval, jämförelse med flera val och longitudinella observationsmöjligheter. Den dynamiska nätverkstopologin visualiserades med hjälp av en tidslinjerepresentation, placerad intill huvudlinsen för att bibehålla klarhet och användarvänlighet. Användarutvärderingar lyfte fram det praktiska i funktionen med dubbla linser för jämförelse sida vid sida, även om den initiala användbarheten av den dynamiska displayen antyder utrymme för förfining. Tre visuella representationer - stapeldiagram, parallella koordinater och stjärndiagram - integrerades i den magiska linsen för att förbättra multivariat datautforskning. Användarfeedback indikerade en preferens för stapeldiagram på grund av deras enkla tolkning, trots deras begränsningar i att visa små relativa värden. Parallella koordinater gynnades på andra plats för dens förmåga att bibehålla visuell klarhet över en rad värden, medan stjärndiagram, även om de var mindre föredragna, erkändes för sin potential i att visa många dimensioner. Framtida arbete bör utvidga verktygets tillämpning till datauppsättningar i verkliga världen, inkludera dynamiska nominella data och utforska alternativa dynamiska visningsmetoder. Bredare användarstudier över olika domäner kommer att ytterligare validera och förfina verktyget, vilket säkerställer dess effektivitet och anpassningsförmåga för olika datavisualiseringsbehov.

Atualmente, no meio jurídico, há um reconhecimento implícito de que as provas materiais necessitam de embasamento científico para alcançar a autenticidade imprescindível ao estabelecimento da convicção dos magistrados. A natureza de determinados exames, como a classificação de substâncias proibidas, demandam a utilização de técnicas e saberes oriundos das ciências naturais e tecnológicas. O trabalho pericial deve ser pautado pela cientificidade, com a aplicação de conhecimentos de diversas áreas, dentre as quais está incluída a estatística forense. Neste trabalho foram utilizadas ferramentas estatísticas para avaliar a qualidade e a contribuição dos laudos periciais para os casos envolvendo substâncias ilícitas e correlacionar o conteúdo destes documentos com a sentença judicial. Numa primeira etapa foram analisadas as informações contidas em laudos toxicológicos de drogas, com o intuito de quantificar a qualidade e importância que eles poderiam fornecer em um processo. Para isso foram analisados 1008 documentos oficiais de diversas jurisdições, divididos em 504 conjuntos de laudos preliminares e definitivos do mesmo caso forense A intenção foi apreciar um conjunto heterogêneo de documentos para possibilitar uma melhor análise. A quantificação foi apreciada através de equações empíricas elaboradas. A validação do método ocorreu por análise de dados multivariados. A metodologia empregada demonstrou-se bastante robusta. A segunda fase do trabalho foi aplicar o resultado dos exames da etapa precedente e correlacionar com a decisão judicial. Para tanto, foram esmiuçadas 167 sentenças proferidas em primeira instância e que continham os laudos elencados na primeira fase. A ferramenta utilizada foi a inferência Bayesiana. Os resultados apontaram que os laudos periciais sempre foram essenciais neste tipo de procedimento julgatório. A qualidade dos documentos produzidos encontrava-se entre boa e ótima, avalizada pelo parâmetro \"relevância do laudo\". Alguns aspectos nos documentos poderiam ser aperfeiçoados, como, por exemplo, a inserção de fotografias do material apreendido e/ou imagens alusivas às análises laboratoriais. Estes estudos permitiram estabelecer um valor de corte para a quantificação da qualidade dos laudos, a partir do qual houve 100% de concordância entre o laudo direcionado e a sentença, para casos de condenação onde o suspeito foi considerado traficante. Por fim, a metodologia proposta apresentou potencial promissor e possibilidade de ser utilizada em outros tipos de casos forenses, como, por exemplo, homicídios, suicídios e outros. / There is an implicit recognition in the current legal scenario that material evidences require scientific support in order to achieve the authenticity that the magistrates need for making decisions. The nature of certain exams, such as classification of prohibited substances, requires the use of techniques and knowledge from natural sciences and technology. The forensic work must rely on scientific methods and apply knowledge from several areas, including forensic statistics. The present work used statistic tools to evaluate the quality and the contribution of forensic reports about illegal substances; the goal is to correlate the content of these documents with the court ruling. In the first part we analyzed the information from toxicology reports on drugs, aiming at the quantification of the importance they might bear to court proceedings. We have parsed 1008 official documents from several jurisdictions, divided into 504 sets of preliminary and final reports from the same case. The objective was to evaluate a heterogeneous document set for a better analysis. The quantification was determined from elaborate empiric equations. The validation of the method was performed by multivariate data analysis. The methodology used in the present work has proved very robust. The second part was the application of the results from the previous part and correlation to the court ruling. We have thoroughly examined 167 rulings at first instance that contained the reports cited in the first part. We have used Bayesian inference, and the results indicated that forensic reports were always required in this type of court proceeding. The quality of the documents was considered good or excellent, as stated in the parameter \"relevance of the report\". Some aspects could be improved, for instance, images of collected material evidence or laboratory analytical procedures could be included. These studies allowed establishing a cut-off value for the quantification of the report quality, from which a 100% agreement between the report and the court decision was achieved, in cases where the suspect was found guilty. Finally, the proposed methodology in this work showed a good potential and could be used in other kinds of forensic cases, such as homicide, suicide and other forensic investigations.

Loading of data from slow persistent memory (disk storage) to main memory represents a bottleneck for current interactive visual data exploration applications, especially when applied to huge volumnes of data. Semantic caching of queries at the client-side is a recently emerging technology that can significantly improve the performance of such systems, though it may not in all cases fully achieve the near real-time responsiveness required by such interactive applications. We hence propose to augment the semantic caching techniques by applying prefetching. That is, the system predicts the user's next requested data and loads the data into the cache as a background process before the next user request is made. Our experimental studies confirm that prefetching indeed achieves performance improvements for interactive visual data exploration. However, a given prefetching technique is not always able to correctly predict changes in a user's navigation pattern. Especially, as different users may have different navigation patterns, implying that the same strategy might fail for a new user. In this research, we tackle this shortcoming by utilizing the adaptation concept of strategy selection to allow the choice of prefetching strategy to change over time both across as well as within one user session. While other adaptive prefetching research has focused on refining a single strategy, we instead have developed a framework that facilitates strategy selection. For this, we explored various metrics to measure performance of prefetching strategies in action and thus guide the adaptive selection process. This work is the first to study caching and prefetching in the context of visual data exploration. In particular, we have implemented and evaluated our proposed approach within XmdvTool, a free-ware visualization system for visually exploring hierarchical multivariate data. We have tested our technique on real user traces gathered by the logging tool of our system as well as on synthetic user traces. Our results confirm that our adaptive approach improves system performance by selecting a good combination of prefetching strategies that adapts to the user's changing navigation patterns.

<p>New techniques and approaches for organic synthesis, purification and biological testing are enabling pharmaceutical industries to produce and test increasing numbers of compounds every year. Surprisingly, this has not led to more new drugs reaching the market, prompting two questions – why is there not a better correlation between their efforts and output, and can it be improved? One possible way to make the drug discovery process more efficient is to ensure, at an early stage, that the tested compounds are diverse, representative and of high quality. In addition the biological evaluation systems have to be relevant and reliable. The diversity of the tested compounds could be ensured and the reliability of the biological assays improved by using Design Of Experiments (DOE) more frequently and effectively. However, DOE currently offers insufficient options for these purposes, so there is a need for new, tailor-made DOE strategies. The aim of the work underlying this thesis was to develop and evaluate DOE approaches for diverse compound selection and efficient assay optimisation. This resulted in the publication of two new DOE strategies; D-optimal Onion Design (DOOD) and Rectangular Experimental Designs for Multi-Unit Platforms (RED-MUP), both of which are extensions to established experimental designs.</p><p>D-Optimal Onion Design (DOOD) is an extension to D-optimal design. The set of possible objects that could be selected is divided into layers and D-optimal selection is applied to each layer. DOOD enables model-based, but not model-dependent, selections in discrete spaces to be made, since the selections are not only based on the D-optimality criterion, but are also biased by the experimenter’s prior knowledge and specific needs. Hence, DOOD selections provide controlled diversity.</p><p>Assay development and optimisation can be a major bottleneck restricting the progress of a project. Although DOE is a recognised tool for optimising experimental systems, there has been widespread unwillingness to use it for assay optimisation, mostly because of the difficulties involved in performing experiments according to designs in 96-, 384- and 1536- well formats. The RED-MUP framework combines classical experimental designs orthogonally onto rectangular experimental platforms, which facilitates the execution of DOE on these platforms and hence provides an efficient tool for assay optimisation.</p><p>In combination, these two strategies can help uncovering the crossroads between biology and chemistry in drug discovery as well as lead to higher information content in the data received from biological evaluations, providing essential information for well-grounded decisions as to the future of the project. These two strategies can also help researchers identify the best routes to take at the crossroads linking biological and chemical elements of drug discovery programs.</p>

There is an ever increasing need of new technologies for identification of molecular markers for early diagnosis of fatal diseases to allow efficient treatment. In addition, there is great value in finding patterns of metabolites, proteins or genes altered in relation to specific disease conditions to gain a deeper understanding of the underlying mechanisms of disease development. If successful, scientific achievements in this field could apart from early diagnosis lead to development of new drugs, treatments or preventions for many serious diseases.  Metabolites are low molecular weight compounds involved in the chemical reactions taking place in the cells of living organisms to uphold life, i.e. metabolism. The research field of metabolomics investigates the relationship between metabolite alterations and biochemical mechanisms, e.g. disease processes. To understand these associations hundreds of metabolites present in a sample are quantified using sensitive bioanalytical techniques. In this way a unique chemical fingerprint is obtained for each sample, providing an instant picture of the current state of the studied system. This fingerprint or picture can then be utilized for the discovery of biomarkers or biomarker patterns of biological and clinical relevance. In this thesis the focus is set on evaluation and application of strategies for studying metabolic alterations in human tissues associated with disease. A chemometric methodology for processing and modeling of gas chromatography-mass spectrometry (GC-MS) based metabolomics data, is designed for developing predictive systems for generation of representative data, validation and result verification, diagnosis and screening of large sample sets. The developed strategies were specifically applied for identification of metabolite markers and metabolic pathways associated with prostate cancer disease progression. The long-term goal was to detect new sensitive diagnostic/prognostic markers, which ultimately could be used to differentiate between indolent and aggressive tumors at diagnosis and thus aid in the development of personalized treatments. Our main finding so far is the detection of high levels of cholesterol in prostate cancer bone metastases. This in combination with previously presented results suggests cholesterol as a potentially interesting therapeutic target for advanced prostate cancer. Furthermore we detected metabolic alterations in plasma associated with metastasis development. These results were further explored in prospective samples attempting to verify some of the identified metabolites as potential prognostic markers.

In this thesis technical nonionic surfactants are studied using multivariate techniques. The surfactants studied were alkyl ethoxylates (AEOs) and alkyl polyglucosides (APGs). The aquatic toxicity of the surfactants towards two organisms, a shrimp and a rotifer, was examined. The specified effect was lethality, LC50, as indicated by immobilisation. In a comparative study, the LC50 values obtained were used to develop two different types of model. In the log P model the toxicity was correlated to log P alone, while in the multivariate model several physicochemical variables, including log P, were correlated to the toxicity. The multivariate model gave smaller prediction errors than the log P model. Further, the change in reactivity when a surfactant mixture was added to dissolving pulp under alkaline conditions was studied, using the amount of residual cellulose as a measure of the reactivity. Ten AEO/APG mixtures were tested, and the mixture with greatest potential was studied in more detail. An optimum in the amount of added surfactant was found that seems to coincide, according to surface tension measurements, with the CMC.

Fluidized bed dryers are used in the pharmaceutical industry for the batch drying of pharmaceutical granulate. Maintaining optimal hydrodynamic conditions throughout the drying process is essential to product quality. Due to the complex interactions inherent in the fluidized bed drying process, mechanistic models capable of identifying these optimal modes of operation are either unavailable or limited in their capabilities. Therefore, empirical models based on experimentally generated data are relied upon to study these systems.<p> Principal Component Analysis (PCA) and Partial Least Squares (PLS) are multivariate statistical techniques that project data onto linear subspaces that are the most descriptive of variance in a dataset. By modeling data in terms of these subspaces, a more parsimonious representation of the system is possible. In this study, PCA and PLS are applied to data collected from a fluidized bed dryer containing pharmaceutical granulate. <p>System hydrodynamics were quantified in the models using high frequency pressure fluctuation measurements. These pressure fluctuations have previously been identified as a characteristic variable of hydrodynamics in fluidized bed systems. As such, contributions from the macroscale, mesoscale, and microscales of motion are encoded into the signals. A multiresolutional decomposition using a discrete wavelet transformation was used to resolve these signals into components more representative of these individual scales before modeling the data. <p>The combination of multiresolutional analysis with PCA and PLS was shown to be an effective approach for modeling the conditions in the fluidized bed dryer. In this study, datasets from both steady state and transient operation of the dryer were analyzed. The steady state dataset contained measurements made on a bed of dry granulate and the transient dataset consisted of measurements taken during the batch drying of granulate from approximately 33 wt.% moisture to 5 wt.%. Correlations involving several scales of motion were identified in both studies.<p> In the steady state study, deterministic behavior related to superficial velocity, pressure sensor position, and granulate particle size distribution was observed in PCA model parameters. It was determined that these properties could be characterized solely with the use of the high frequency pressure fluctuation data. Macroscopic hydrodynamic characteristics such as bubbling frequency and fluidization regime were identified in the low frequency components of the pressure signals and the particle scale interactions of the microscale were shown to be correlated to the highest frequency signal components. PLS models were able to characterize the effects of superficial velocity, pressure sensor position, and granulate particle size distribution in terms of the pressure signal components. Additionally, it was determined that statistical process control charts capable of monitoring the fluid bed hydrodynamics could be constructed using PCA<p>In the transient drying experiments, deterministic behaviors related to inlet air temperature, pressure sensor position, and initial bed mass were observed in PCA and PLS model parameters. The lowest frequency component of the pressure signal was found to be correlated to the overall temperature effects during the drying cycle. As in the steady state study, bubbling behavior was also observed in the low frequency components of the pressure signal. PLS was used to construct an inferential model of granulate moisture content. The model was found to be capable of predicting the moisture throughout the drying cycle. Preliminary statistical process control models were constructed to monitor the fluid bed hydrodynamics throughout the drying process. These models show promise but will require further investigation to better determine sensitivity to process upsets.<p> In addition to PCA and PLS analyses, Multiway Principal Component Analysis (MPCA) was used to model the drying process. Several key states related to the mass transfer of moisture and changes in temperature throughout the drying cycle were identified in the MPCA model parameters. It was determined that the mass transfer of moisture throughout the drying process affects all scales of motion and overshadows other hydrodynamic behaviors found in the pressure signals.

Fluidized bed dryers are used in the pharmaceutical industry for the batch drying of pharmaceutical granulate. Maintaining optimal hydrodynamic conditions throughout the drying process is essential to product quality. Due to the complex interactions inherent in the fluidized bed drying process, mechanistic models capable of identifying these optimal modes of operation are either unavailable or limited in their capabilities. Therefore, empirical models based on experimentally generated data are relied upon to study these systems.<p> Principal Component Analysis (PCA) and Partial Least Squares (PLS) are multivariate statistical techniques that project data onto linear subspaces that are the most descriptive of variance in a dataset. By modeling data in terms of these subspaces, a more parsimonious representation of the system is possible. In this study, PCA and PLS are applied to data collected from a fluidized bed dryer containing pharmaceutical granulate. <p>System hydrodynamics were quantified in the models using high frequency pressure fluctuation measurements. These pressure fluctuations have previously been identified as a characteristic variable of hydrodynamics in fluidized bed systems. As such, contributions from the macroscale, mesoscale, and microscales of motion are encoded into the signals. A multiresolutional decomposition using a discrete wavelet transformation was used to resolve these signals into components more representative of these individual scales before modeling the data. <p>The combination of multiresolutional analysis with PCA and PLS was shown to be an effective approach for modeling the conditions in the fluidized bed dryer. In this study, datasets from both steady state and transient operation of the dryer were analyzed. The steady state dataset contained measurements made on a bed of dry granulate and the transient dataset consisted of measurements taken during the batch drying of granulate from approximately 33 wt.% moisture to 5 wt.%. Correlations involving several scales of motion were identified in both studies.<p> In the steady state study, deterministic behavior related to superficial velocity, pressure sensor position, and granulate particle size distribution was observed in PCA model parameters. It was determined that these properties could be characterized solely with the use of the high frequency pressure fluctuation data. Macroscopic hydrodynamic characteristics such as bubbling frequency and fluidization regime were identified in the low frequency components of the pressure signals and the particle scale interactions of the microscale were shown to be correlated to the highest frequency signal components. PLS models were able to characterize the effects of superficial velocity, pressure sensor position, and granulate particle size distribution in terms of the pressure signal components. Additionally, it was determined that statistical process control charts capable of monitoring the fluid bed hydrodynamics could be constructed using PCA<p>In the transient drying experiments, deterministic behaviors related to inlet air temperature, pressure sensor position, and initial bed mass were observed in PCA and PLS model parameters. The lowest frequency component of the pressure signal was found to be correlated to the overall temperature effects during the drying cycle. As in the steady state study, bubbling behavior was also observed in the low frequency components of the pressure signal. PLS was used to construct an inferential model of granulate moisture content. The model was found to be capable of predicting the moisture throughout the drying cycle. Preliminary statistical process control models were constructed to monitor the fluid bed hydrodynamics throughout the drying process. These models show promise but will require further investigation to better determine sensitivity to process upsets.<p> In addition to PCA and PLS analyses, Multiway Principal Component Analysis (MPCA) was used to model the drying process. Several key states related to the mass transfer of moisture and changes in temperature throughout the drying cycle were identified in the MPCA model parameters. It was determined that the mass transfer of moisture throughout the drying process affects all scales of motion and overshadows other hydrodynamic behaviors found in the pressure signals.