Streaming blir dagligen en allt mer populär form av musikkonsumtion. Denna typ av medium har gjort det allt mer enkelt för individer att lyssna på musik. Diskussioner om de villkor som kreatörerna som bidrar med värde till dessa medium påpekar dock tuffa arbetsförhållanden. Kreatörer tenderar att få låg ersättning och dålig översikt över den data som relaterar till artistens lyssnare. I och med Web3:s framfart har det påbörjats en diskussion om decentraliserade musikplattformar som ett alternativ till de traditionella streamingtjänsterna. Det är inom detta område som studien ämnar att utforska acceptansen kreatörerna har av detta alternativa sätt att distribuera musik. Utifrån ramverket TAM (Technology acceptance model) skapades en modell. För att testa denna modell genomfördes sedan en enkätstudie. Det insamlade materialet användes sedan för att utföra en univariat, bivariat samt multivariat analys med hjälp av frekvenstabeller, Spearmans korrelationskoefficient och PLS-SEM-verktyg. Resultatet visade på möjliga problemområden, bland annat validiteten hos enkätfrågorna, men även lämpligheten hos studiens ramverk i förhållande till den typen av system som faktiskt undersöktes. Det var dock tydligt att urvalets användning av plattformen i fråga var en direkt effekt av hur deras avsikt till att använda densamma såg ut. Denna användaravsikt berodde sedan på deras attityd till plattformen som i sin tur berodde på den nytta de upplevde att plattformen genererade. / Music streaming is growing in popularity on a daily basis. This type of medium has made listening to music more accessible to its users. Discussions regarding tough working conditions for the creators that bring value to the platforms are becoming more common. Creators tend to get low compensation and insufficient data regarding its listeners. In regards to the growing interest with Web3 technology, discussions are being held about decentralized music platforms potential of being an alternative to the traditional streaming platforms. It’s within this field the study aims to shed some light in regards to the acceptance creators have toward this alternative way of distributing music. With regards to the theoretical framework TAM (Technology acceptance model), a model was constructed. In order to evaluate this model a survey was conducted. The data collected was then used to perform a univariate, bivariate and multivariate analysis. These were possible with the help of frequency tables, Spearmans correlation coefficient as well as PLS-SEM tools. The results brought light to several possible problem areas, amongst these the validity of the questionnaire, but also the adequacy of the theoretical framework with regards to the a type of system the study actually concerns itself with. It was, however, clear that the creators’ actual use of the platform was a direct product of their intention to use it. This intention was a result of their attitude towards the platform, which in turn depended on the usefulness the creator perceived the platform to generate.

Contamination of peanuts by secondary metabolites of certain fungi, namely aflatoxins present a great health hazard when exposed either at low levels for prolonged times (carcinogenic) or at high levels at once (poisonous). It is important to develop an accurate and rapid measurement technique to trace the aflatoxin and/or source fungi presence in peanuts. Thus, current research focused on development of vibrational spectroscopy based methods for detection and separation of contaminated peanut samples. Aflatoxin incidence, as a chemical contaminant in peanut paste samples, was investigated, in terms of spectral characteristics using FTIR-ATR. The effects of spectral pre-processing steps such as mean-centering, smoothing the 1st derivative and normalizing were studied. Logarithmic method was the best normalization technique describing the exponentially distributed spectral data. Spectral windows giving the best correlation with respect to increasing aflatoxin amount led to selection of fat associated spectral bands. Using the multivariate analysis tools, structural contributions of aflatoxins in peanut matrix were detected. The best region was decided as 3028-2752, 1800-1707, 1584-1424, and 1408-1127 cm-1 giving correlation coefficient for calibration (R2C), root mean square error for calibration (RMSEC) and root mean square error for prediction (RMSEP) of 98.6%, 7.66ppb and 19.5ppb, respectively. Applying the constructed partial least squares model, 95% of the samples were correctly classified while the percentage of false negative and false positive identifications were 16% and 0%, respectively. Aspergillus species of section Flavi and the black fungi, A. niger are the most common colonists of peanuts in nature and the majority of the aflatoxin producing strains are from section Flavi. Seed colonization by selected Aspergillus spp. was investigated by following the chemical alterations as a function of fungal growth by means of spectral readouts. FTIR-ATR was utilized to correlate spectral characteristics to mold density, and to separate Aspergillus at section, species and strain levels, threshold mold density values were established. Even far before the organoleptic quality changes became visually observable (~10,000 mold counts), FTIR distinguished the species of same section. Besides, the analogous secondary metabolites produced increased the similarity within the spectra even their spectral contributions were mostly masked by bulk peanut medium; and led to grouping of species producing the same mycotoxins together. Aflatoxigenic and non-aflatoxigenic strains of A. flavus and A. parasiticus were further studied for measurement capability of FTIR-ATR system in discriminating the toxic streams from just moldy and clean samples. Owing to increased similarity within the collected spectral data due to aflatoxin presence, clean samples (having aflatoxin level lower than 20 ppb, n=44), only moldy samples (having aflatoxin level lower than 300 ppb, n=28) and toxic samples (having aflatoxin level between 300-1200 ppb, n=23) were separated into appropriate classes (with a 100% classification accuracy). Photoacoustic spectroscopy (PAS) is a non-invasive technique and offers many advantages over more traditional ATR system, specifically, for in-field measurements. Even though the sample throughput time is longer compared to ATR measurements, intact seeds can be directly loaded into sample compartment for analysis. Compared to ATR, PAS is more sensitive to high moisture in samples, which in our case was not a problem since peanuts have water content less than 10%. The spectral ranges between: 3600-2750, 1800-1480, 1200-900 cm-1 were assigned as the key bands and full separation between Aspergillus spp. infected and healthy peanuts was obtained. However, PAS was not sensitive as ATR either in species level classification of Aspergillus invasion or toxic-moldy level separation. When run for separation of aflatoxigenic versus non-aflatoxigenic batches of samples, 7 out of 54 contaminated samples were misclassified but all healthy peanuts were correctly identified (15 healthy/ 69 total peanut pods). This study explored the possibility of using vibrational spectroscopy as a tool to understand chemical changes in peanuts and peanut products to Aspergillus invasion or aflatoxin contamination. The overall results of current study proved the potential of FTIR, equipped with either ATR or PAS, in identification, quantification and classification at varying levels of mold density and aflatoxin concentration. These results can be used to develop quality control laboratory methods or in field sorting devices. / Ph. D.

Natural archives such as lake sediments allow us to assess contemporary ecosystem responses to climate and environmental changes in a long-term context beyond the few decades to at most few centuries covered by monitoring or historical data. To achieve a comprehensive view of the changes preserved in sediment records, multi-proxy studies – ideally in high resolution – are necessary. However, this combination of including a range of analyses and high resolution constrains the amount of material available for analyses and increases the analytical costs. Infrared spectroscopic methods are a cost-efficient alternative to conventional methods because they offer a) a simple sample pre-treatment, b) a rapid measurement time, c) the non- or minimal consumption of sample material, and d) the potential to extract quantitative and qualitative information about organic and inorganic sediment components from a single measurement. The main objective of this doctoral thesis was twofold. The first part was to further explore the potential of Fourier transform infrared (FTIR) and visible-near infrared (VNIR) spectroscopy in paleolimnological studies as a) an alternative tool to conventional methods for quantifying biogenic silica (bSi) – a common proxy of paleoproductivity in lakes – in sediments and b) as a tool to infer past lake-water total organic carbon (TOC) levels from sediments. In a methodological study, I developed an independent application of FTIR spectroscopy and PLS modeling for determining bSi in sediments by using synthetic sediment mixtures with known bSi content. In contrast to previous models, this model is independent from conventional wet-chemical techniques, which had thus far been used as the calibration reference, and their inherent measurement uncertainties. The second part of the research was to apply these techniques as part of three multi-proxy studies aiming to a) improve our understanding of long-term element cycling in boreal and arctic landscapes in response to climatic and environmental changes, and b) to assess ongoing changes, particularly in lake-water TOC, on a centennial to millennial time scale. In the first applied study, high-resolution FTIR measurements of the 318-m long sediment record of Lake El’gygytgyn provided a detailed insight into long-term climate variability in the Siberian Arctic over the past 3.6 million years. Highest bSi accumulation occurred during the warm middle Pliocene (3.6-3.3 Ma), followed by a gradual but variable decline, which reflects the first onset of glacial periods and then the finally full establishment of glacial–interglacial cycles during the Quaternary. The second applied study investigated the sediment record of Torneträsk in subarctic northern Sweden also in relation to climate change, but only over the recent post-glacial period (~10 ka). By comparing responses to past climatic and environmental forcings that were recorded in this large-lake system with those recorded in small lakes from its catchment, I determined the significance and magnitude of larger-scale changes across the study region. Three different types of response were identified over the Holocene: i) a gradual response to the early landscape development following deglaciation (~10000-5300 cal yr BP); ii) an abrupt but delayed response following climate cooling during the late Holocene, which occurred c. 1300 cal yr BP – about 1000-2000 years later than in smaller lakes from the area; and iii) an immediate response to the ongoing climate change during the past century. The rapid, recent response in a previously rather insensitive lake-ecosystem emphasizes the unprecedented scale of ongoing climate change in northern Fennoscandia. In the third applied study, VNIR-inferred lake-water TOC concentrations from lakes across central Sweden showed that the ongoing, observed increase in surface water TOC in this region was in fact preceded by a long-term decline beginning already AD 1450-1600. These dynamics coincided with early human land use activities in the form of widespread summer forest grazing and farming that ceased over the past century. The results of this study show the strong impact of past human activities on past as well as ongoing TOC levels in surface waters, which has thus far been underestimated. The research in this thesis demonstrates that infrared spectroscopic methods can be an essential component in high-resolution, multi-proxy studies of past environmental and climate changes.

Extracting structure from initially incomprehensible streams of events is fundamental to a range of human abilities: from navigating in a new environment to learning a language. These skills rely on our ability to extract spatial and temporal regularities, often with minimal explicit feedback, that is known as statistical learning. Despite the importance of statistical learning for making perceptual decisions, we know surprisingly little about the brain circuits and how they change when learning temporal regularities. In my thesis, I combine behavioural measurements, Diffusion Tensor Imaging (DTI) and resting-state fMRI (rs-fMRI) to investigate the structural and functional circuits that are involved in statistical learning of temporal structures. In particular, I compare structural connectivity as measured by DTI and functional connectivity as measured by rs-fMRI before vs. after training to investigate learning-dependent changes in human brain pathways. Further, I combine the two imaging modalities using graph theory and regression analyses to identify key predictors of individual learning performance. Using a prediction task in the context of sequence learning without explicit feedback, I demonstrate that individuals adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. Importantly, I show that learning of temporal structures relates to decision strategy that varies among individuals between two prototypical distributions: matching the exact sequence statistics or selecting the most probable outcome in a given context (i.e. maximising). Further, combining DTI and rs-fMRI, I show that learning-dependent plasticity in dissociable cortico-striatal circuits relates to decision strategy. In particular, matching relates to connectivity between visual cortex, hippocampus and caudate, while maximisation relates to connectivity between frontal and motor cortices and striatum. These findings have potential translational applications, as alternate brain routes may be re-trained to support learning ability when specific pathways (e.g. memory-related circuits) are compromised by age or disease.

In the last decade there has been increasing research interest in the value of bio-sourced materials from lignocellulosic biomass. The dissolution of cellulose by ionic liquids (ILs) has led to investigations including the dissolution of cellulose, lignin, and complete biomass samples and the in situ processing of cellulose. Rapid quantitative measurement of cellulose dissolution in ILs is difficult. In this work, Fourier transform infrared spectroscopy (FTIR) spectra of cellulose dissolved in 1-ethyl-3-methylimidazolium acetate ([emim][OAc]) were subjected to partial least squares (PLS) regression to model dissolved cellulose content. PLS regression was used due to the ease in developing predictive models with this technique in addition to linear regression being ineffectual for modeling when applied to potentially thousands of variables. Applying a normalization data treatment, before regression, generated a model that estimated cellulose content within 0.533 wt%. The methods described provided the basis for a rapid methodology to determine dissolved cellulose content. Development of rapid and facile screening techniques to determine the effectiveness of various ILs as solvents for cellulose or lignin will aid in the development of lignocellulosic based bioproducts. In this work, optical microscopy with and without the use of cross-polarized lenses, was used to monitor cellulose and lignin dissolution in two imidazolium-based and two phosphonium-based ILs as well as n,n-dimethylacetamide/lithium chloride (DMAc/LiCl), demonstrating that this technique could be applied more broadly than solely for ILs. The described optical microscopy methodology was more rapid and sensitive than more traditional techniques, such as visual inspection. The viscosity of [emim][OAc] (162 cP) is 100 times that of water at 20°C and could inhibit its use as a solvent for cellulose. There is a need for simple, low-cost and environmentally benign methods to reduce the viscosity of ILs to aid in cellulose dissolution. In this work, 4 wt% cellulose dissolved in [emim][OAc] was subjected to 50 psi CO2 and 20 psi N2, as a control environment, at both 50°C and 75°C. After 24 hours a nearly 2-fold increase in dissolved cellulose over the N2 control was demonstrated through the application of a 50 psi CO2 environment for cellulose dissolution in [emim][OAc] at 50°C. / Thesis (Master, Chemical Engineering) -- Queen's University, 2011-05-25 22:58:17.744

Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-01-21T18:47:40Z No. of bitstreams: 2 Tese - Raphael Rocha de Oliveira - 2014.pdf: 1885225 bytes, checksum: 5adb0d9c490f337d13e5335be96b08f2 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2015-01-21T18:47:50Z (GMT) No. of bitstreams: 2 Tese - Raphael Rocha de Oliveira - 2014.pdf: 1885225 bytes, checksum: 5adb0d9c490f337d13e5335be96b08f2 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-01-21T18:47:50Z (GMT). No. of bitstreams: 2 Tese - Raphael Rocha de Oliveira - 2014.pdf: 1885225 bytes, checksum: 5adb0d9c490f337d13e5335be96b08f2 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-06-28 / Near infrared reflectance spectroscopy (NIRS) has been successfully applied in the quantitative determination of the main constituents of beef but it has been presenting inconsistent results in determining characteristics relating to tenderness. In addition, the various aspects related to data processing (mathematical pre-treatments, spectral bands, sample presentation, regression method), should be constantly evaluated, since they affect the prediction cap acity of NIRS. In this context, the present study was developed to determine which spectral data-processing methods make it possible, using the PLS regression method, to obtain robust calibration models that determine the chemical composition and tenderness characteristics of beef. The accuracy of the models was determined by external validation, which has been little used in previously published studies. To develop the calibration models, three spectra were collected from each sample of the Longissimus dorsi muscle of 25 mixed-breed castrated dairy calves, divided into five treatments (five repetitions in each) based on supplying diets containing millet and including babassu mesocarp bran at proportions of 0; 12; 24; 36 and 48% in the dry matter of the total diet, comprising 75 spectra. For the external validation set, samples were used from five mixedbreed castrated dairy calves fed on a diet based on maize and soybean, totalling 15 spectra. To determine the chemical composition (fat content, protein, ash content and moisture) and the tenderness properties (water holding capacity – WHC -, total and soluble collagen, shear force, FMI and pH), 135 calibration models were developed with mathematical pre-treatments available on VISION software, version 3.1, using PLS regression, from which 37 (27.41% of the total) presented coefficients of determination considered good or excellent in their predictive capacity. The pre-treatment with “first derivatives” made it possible to develop more robust models for the chemical composition properties, except for RMF, in which “Savitzky-Golay” and “second derivatives” were more efficient, obtaining R 2 and RPD values above those available in the literature. For determining the tenderness properties in beef, the models develope d with “first and second derivatives” pre-treatments, in isolation or with “Savitzky -Golay” or “multiplicative scatter correction” smoothing methods, presented the highest values of RPD, demonstrating that themselves are efficient chemometric tools for obtaining robust calibration models. Models were obtained with limited predictive capacity only in the determination of total fats and total collagen quantification. This was probably due to the low variability presented in the samples used a nd to the low sensitivity of NIRS for total collagen. It was concluded that NIRS can be used to replace conventional methods, being a fast and precise technique, as well as allowing simultaneous analysis of beef quality characteristics. / A espectroscopia de reflectância no infravermelho próximo (NIRS) tem sido aplicada com êxito na determinação quantitativa dos principais constituintes da carne bovina, mas tem apresentado resultados inconsistentes na determinação das características relacionadas à maciez. Além disso, os diferentes aspectos relacionados ao processamento dos dados (pré-tratamentos matemáticos, faixas espectrais, apresentação das amostras, método de regressão), devem ser avaliados constantemente, já que afetam a capacidade de predição do NIRS. Assim sendo, o presente estudo foi desenvolvido para determinar quais métodos de processamento de dados espectrais possibilitam, com o método de regressão PLS, a obtenção de modelos de calibração robustos para a determinação d a composição química e das características de maciez da carne bovina, sendo a acurácia dos modelos determinada por validação externa. Para o desenvolvimento dos modelos de calibração, foram coletados três espectros de cada amostra do músculo Longissimus dorsi de 25 novilhos mestiços leiteiros castrados, divididos em cinco tratamentos, cinco repetições em cada, com base no fornecimento de dietas contendo milheto e inclusão de farelo do mesocarpo do babaçu nas proporções de 0; 12; 24; 36 e 48% na matéria seca da dieta total, totalizando 75 espectros. Para o conjunto de validação externa, foram utilizadas amostras de cinco novilhos mestiços leiteiros castrados submetidos à dieta à base de milho e soja, totalizando 15 espectros. Para a determinação da composição química (lipídios totais, proteína, resíduo mineral fixo e umidade ) e de propriedades de maciez (capacidade de retenção de água, colágeno total e solúvel, força de cisalhamento, IFM e pH), foram desenvolvidos 135 modelos de calibração com os pré-tratamentos matemáticos disponíveis no software VISION, versão 3.1, utilizando a regressão PLS, dos quais 37 (27,41% do total) apresentaram valores de coeficientes de determinação considerados como boa ou excelente capacidade preditiva. O pré-tratamento com “primeira derivada” possibilitou o desenvolvimento de modelos mais robustos para as propriedades de composição química, exceto para RMF, em que “Savitzky-Golay” e “segunda derivada” foram mais eficientes, obtendo valores de R 2 e RPD superiores aos disponíveis na literatura. Para a determinação das propriedades de maciez em carne bovina, os modelos desenvolvidos com os pré-tratamentos com “primeira e segunda derivadas”, isoladamente ou com a utilização dos métodos de suavização “Savitzky-Golay” ou “correção multiplicativa de sinal”, apresentaram os maiores valores de RPD, demonstrando ser ferramentas quimiométricas eficientes para a obtenção de modelos de calibração robustos. Foram obtidos modelos com capacidade preditiva limitada apenas para a determinação de lipídios totais e quantificação do colágeno total, provavelmente, devido à baixa variabilidade apresentada nas amostras utilizadas e à baixa sensibilidade do NIRS para o colágeno total. Conclui-se, que a espectroscopia de reflectância no infravermelho próximo pode s er utilizada em substituição aos métodos convencionais, por ser uma técnica rápida, precisa, sensível e que permite a análise simultânea das características de qualidade da carne bovina.

Ce travail concerne les analyses visant à étudier les données où les individus sont structurés en différents groupes (données multi-groupes). La thèse aborde la question des données multi-groupes ayant une structure en un seul tableau, plusieurs tableaux, trois voies et deux blocs (régression). Cette thèse présente plusieurs méthodes d'analyse de données multi-groupes dans le cadre de l'analyse factorielle. Notre travail comporte trois parties. La première partie traite de l'analyse de données multi-groupes (un bloc de variables divisé en sous-groupes d'individus). Le but est soit descriptif (analyse intra-groupes) ou prédictif (analyse discriminante ou analyse inter-groupe). Nous commençons par une description exhaustive des méthodes multi-groupes. En outre, nous proposons deux méthodes : l'Analyse Procrustéenne duale et l'Analyse en Composantes Communes et Poids Spécifiques duale. Nous exposons également de nouvelles propriétés et algorithmes pour l'Analyse en Composantes Principales multi-groupes. La deuxième partie concerne l'analyse multi-blocs et multi-groupes et l'analyse trois voies et multi-groupes. Nous présentons les méthodes existantes. Par ailleurs, nous proposons deux méthodes, l'ACP multi-blocs et multi-groupes et l'ACP multi-blocs et multi-groupes pondérée, vues comme des extensions d'Analyse en Composantes Principales multi-groupes. L'analyse en deux blocs et multi-groupes est prise en compte dans la troisième partie. Tout d'abord, nous présentons des méthodes appropriées pour trouver la relation entre un ensemble de données explicatives et un ensemble de données à expliquer, les deux tableaux présentant une structure de groupe entre les individus. Par la suite, nous proposons quatre méthodes pouvant être vues comme des extensions de la régression PLS au cas multi-groupes, et parmi eux, nous en sélectionnons une et la développons dans une stratégie de régression. Les méthodes proposées sont illustrées sur la base de plusieurs jeux de données réels dans le domaine de la biologie. Toutes les stratégies d'analyse sont programmées sur le logiciel libre R. / This work deals with multi-group analysis, to study multi-group data where individuals are a priori structured into different groups. The thesis tackles the issue of multi-group data in a multivariate, multi-block, three-way and two-block (regression) setting. It presents several methods of multi-group data analysis in the framework of factorial analysis. It includes three sections. The first section concerns the case of multivariate multi-group data. The aim is either descriptive (within-group analysis) or predictive (discriminant analysis, between-group analysis). We start with a comprehensive review of multi-group methods. Furthermore, we propose two methods namely Dual Generalized Procrustes Analysis and Dual Common Component and Specific Weights Analysis. We also exhibit new properties and algorithms for multi-group Principal Component Analysis. The second section deals with multiblock multi-group and three-way multi-group data analysis. We give a general review of multiblock multi-group methods. In addition, we propose two methods, namely multiblock and multi-group PCA and Weighted-multiblock and multi-group PCA, as extensions of multi-group Principal Component Analysis. The two-block multi-group analysis is taken into account in the third section. Firstly, we give a presentation of appropriate methods to investigate the relationship between an explanatory dataset and a dependent dataset where there is a group structure among individuals. Thereafter, we propose four methods, namely multi-group PLS, in the PLS approach, and among them we select one and develop it into a regression strategy. The proposed methods are illustrated on the basis of several real datasets in the field of biology. All the strategies of analysis are implemented within the framework of R.

La recherche développée dans le cadre de cette thèse aborde différents aspects relevant de l’analyse statistique de données. Dans un premier temps, une analyse de trois indices d’associations entre deux tableaux de données est développée. Par la suite, des stratégies d’analyse liées à la standardisation de tableaux de données avec des applications en analyse en composantes principales (ACP) et en régression, notamment la régression PLS sont présentées. La première stratégie consiste à proposer une standardisation continuum des variables. Une standardisation plus générale est aussi abordée consistant à réduire de manière graduelle non seulement les variances des variables mais également les corrélations entre ces variables. De là, une approche continuum de régression a été élaborée regroupant l’analyse des redondances et la régression PLS. Par ailleurs, cette dernière standardisation a inspiré une démarche de régression biaisée dans le cadre de régression linéaire multiple. Les propriétés d’une telle démarche sont étudiées et les résultats sont comparés à ceux de la régression Ridge. Dans le cadre de l’analyse de plusieurs tableaux de données, une extension de la méthode ComDim pour la situation de K+1 tableaux est développée. Les propriétés de cette méthode, appelée P-ComDim, sont étudiées et comparées à celles de Multiblock PLS. Enfin, la situation où il s’agit d’évaluer l’effet de plusieurs facteurs sur des données multivariées est considérée et une nouvelle stratégie d’analyse est proposée. / The research developed in this thesis deals with several statistical aspects for analyzing datasets. Firstly, investigations of the properties of several association indices commonly used by practitioners are undergone. Secondly, different strategies related to the standardization of the datasets with application to principal component analysis (PCA) and regression, especially PLS-regression were developed. The first strategy consists of a continuum standardization of the variables. The interest of such standardization in PCA and PLS-regression is emphasized.A more general standardization is also discussed which consists in reducing gradually not only the variances of the variables but also their correlations. Thereafter, a continuum approach was developed combining Redundancy Analysis and PLS-regression. Moreover, this new standardization inspired a biased regression model in multiple linear regression. Properties related to this approach are studied and the results are compared on the basis of case studies with those of Ridge regression. In the context of the analysis of several datasets in an exploratory perspective, the method called ComDim, has certainly raised interest among practitioners. An extension of this method for the analysis of K+1 datasets was developed. Properties related to this method, called P-ComDim, are studied and compared to Multiblock PLS. Finally, for the analysis of datasets depending on several factors, a new approach based on PLS regression is proposed.

No description available.