Fourier Transform Infrared (FTIR) microscopy is a sensitive method for detecting difference in the morphology of biological cells. In this study FTIR spectra were obtained for uninfected cells, and cells infected with two different viruses. The spectra obtained are difficult to discriminate visually. Here we apply advanced statistical methods to the analysis of the spectra, to test if such spectra are useful for diagnosing viral infections in cells. Logistic Regression (LR) and Partial Least Squares Regression (PLSR) were used to build models which allow us to diagnose if spectral differences are related to infection state of the cells. A three-fold, balanced cross-validation method was applied to estimate the shrinkages of the area under the receiving operator characteristic curve (AUC), and specificities at sensitivities of 95%, 90% and 80%. AUC, sensitivity and specificity were used to gauge the goodness of the discrimination methods. Our statistical results shows that the spectra associated with different cellular states are very effectively discriminated. We also find that the overall performance of PLSR is better than that of LR, especially for new data validation. Our analysis supports the idea that FTIR microscopy is a useful tool for detection of viral infections in biological cells.

Fourier transform infrared (FTIR) spectroscopy, one method of electromagnetic radiation for detecting specific cellular molecular structure, can be used to discriminate different types of cells. The objective is to find the minimum time (choice among 2 hour, 4 hour and 6 hour) to record FTIR readings such that different viruses can be discriminated. A new method is adopted for the datasets. Briefly, inner differences are created as the control group, and Wilcoxon Signed Rank Test is used as the first selecting variable procedure in order to prepare the next stage of discrimination. In the second stage we propose either partial least squares (PLS) method or simply taking significant differences as the discriminator. Finally, k-fold cross-validation method is used to estimate the shrinkages of the goodness measures, such as sensitivity, specificity and area under the ROC curve (AUC). There is no doubt in our mind 6 hour is enough for discriminating mock from Hsv1, and Coxsackie viruses. Adeno virus is an exception.

Ce travail de recherche s'inscrit dans le cadre des méthodes factorielles qui permettent de décrire et prédire des données structurées en plusieurs tableaux. Les objectifs et la nature des données d'épidémiologie analytique dans le domaine vétérinaire ont amené à centrer le travail sur les méthodes de régression multibloc, qui orientent la description de plusieurs tableaux de variables vers l'explication d'un autre tableau. Un des principaux objectifs est de contribuer à la réflexion sur la sensibilité de ces méthodes à la multicolinéarité. Des méthodes statistiques existantes sont présentées et reliées dans un cadre unifié, relevant soit de critères à maximiser comparables, soit d'un continuum général les reliant. De nouvelles méthodes peu vulnérables à l'égard de la multicolinéarité, et s'appliquant au cas de données structurées en deux puis en (K+1) tableaux, sont proposées. L'intérêt de ces méthodes, ainsi que des continuums qui leur sont associés, est illustré sur la base d'études de cas réels en épidémiologie. Ce travail de recherche a permis d'appliquer les méthodes multiblocs au domaine de l'épidémiologie animale, dans lequel elles n'avaient pas encore été utilisées.

Made available in DSpace on 2015-05-14T13:21:12Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4180515 bytes, checksum: c6359ed912cde60c8848929b44dcca5c (MD5) Previous issue date: 2012-03-08 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Spectroscopy techniques combined with multivariate calibration have allowed the development of methods for analyte determinations (or other properties) in complex matrices. In this context, it can be mentioned the determinations that uses models based on PLS (Partial Least Square) regression, which is well established and consolidated in literature. Is spite of efficiency of PLS models obtained from full spectrum, some papers reported in literature show that a variable selection may improve the predictive ability of the PLS models. In the present work, it was developed an algorithm, in Matlab@, that employs the SPA (Successive Projection Algorithm), originally proposed for MLR (Multiple Linear Regression), in order to improve the predictive ability of interval PLS models. The proposed algorithm, termed iSPA-PLS, was evaluated in three case studies, namely: (i) simultaneous determination of three artificial colorants by UV-VIS spectrometry, (ii) quantification of protein contents in wheat using NIR spectrometry, and (iii) quality determination of samples of beer extract using NIR spectrometry too. The performance of iSPA-PLS was compared to the following well-established algorithms and methods: GA-PLS, PLS-Jack-Knife, iPLS e siPLS. In all applications, the results show that the iSPA-PLS presented some advantageous when compared to other algorithms used for comparison. The main advantageous include the smallest errors of prediction and the capacity of selecting a smaller number of PLS factors. / A combinação de técnicas espectroscópicas com calibração multivariada tem permitido o desenvolvimento de métodos para determinação de analitos (ou outras propriedades) em matrizes complexas. Nesse contexto, destacam-se as determinações usando modelos baseados na regressão PLS (Partial Least Square), bem difundida e consolidada na literatura. Apesar da eficácia dos modelos PLS obtidos a partir de espectros completos, alguns trabalhos da literatura têm mostrado que a seleção de variáveis pode melhorar a capacidade preditiva dos modelos PLS. No presente trabalho, desenvolve-se um algoritmo, em MatLab@, que utiliza o Algoritmo das Projeções Sucessivas-APS, proposto originalmente para MLR (Multiple Linear Regression), a fim de melhorar a capacidade preditiva de modelos PLS obtidos por intervalos. O algoritmo proposto, denominado Algoritmo das projeções sucessivas em intervalos para regressão PLS (iSPA-PLS), foi avaliado em três estudos de caso, a saber: (i) determinação simultânea de três corantes alimentícios em amostras sintéticas usando espectrometria UV-Vis, (ii) quantificação do teor de proteínas em trigo por espectrometria NIR e (iii) determinação da qualidade de amostras de extrato de cervejas usando também espectrometria NIR. O desempenho do iSPA-PLS foi comparado ao dos seguintes algoritmos e modelos bem estabelecidos na literatura: GA-PLS, PLS-Jack-Knife, iPLS e siPLS. Os resultados das três aplicações atestam as vantagens do iSPA-PLS frente aos demais algoritmos. Entre elas, destacam-se os menores erros de predição e a capacidade de selecionar um número menor de fatores PLS.

Summary Fresh water availability and distribution have been declining over time due to population increase, climate change and variability, emerging new demands due to economic growth, and changing consumption patterns. Spatial and temporal changes in environmental changes, such as climate and land use/cover (LULC) dynamics have an enormous impact on water availability. Food and energy security, urbanization and industrial growth, as well as climate change (CC) will pose critical challenges on water resources. Climate variability and change may affect both the supply and demand sides of the balance, and thus add to the challenges. Land-cover changes are vastly prominent in the developing countries that are characterized by agriculture-based economies and rapidly increasing human population. The consequent changes in water availability and increase in per capita water demand will adversely affect the food, water and energy security of those countries. Therefore, evaluating the response of the catchment to environmental changes is crucial in the critical part of the basin for sustainable water resource management and development. In particular, assessing the contribution of individual LULC classes to changes in water balance components is vital for effective water and land resource management, and for mitigation of climate change impacts. The dynamic water balance of a catchment is analyzed by hydrological models that consider spatio-temporal catchment characteristics. As a result, hydrological models have become indispensable tools for the study of hydrological processes and the impacts of environmental stressors on the hydrologic system. Physically-based distributed hydrological models are able to explicitly account for the spatial variability of hydrological process, catchment characteristics such as climatic parameters, and land use/cover changes. For improved illustration of physical processes in space and time, the distributed hydrological models need serially complete and homogenized rainfall and temperature data. However, observed rainfall and temperature data are neither serially complete nor homogeneous, particularly in developing countries. Using inhomogeneous climatological data inputs to hydrological models affects the output magnitude of climate and land use/cover change impacts and, hence, climate change adaptation. The Nile River Basin, one of the transboundary river flows through 11 riparian states, serves the livelihoods of millions of people in the basin (nearly 20 per cent of the African population) and covers one-tenth of the land cover of Africa. The basin is characterized by high population growth and high temporal variability in the river flow and rainfall patterns. The Blue Nile river basin, which contributes 62% of the annual main Nile flow, has faced serious land degradation. This has led to increased soil erosion and loss of soil fertility. The most overwhelming challenge that the basin faces is food insecurity caused by subsistence farming and rain-fed agriculture (over 70% of the basin’s population), together with high rainfall variability. Drought and floods are also critical issues in the Blue Nile basin, with the potential for exacerbation by environmental changes. Understanding how LULC and climate changes influence basin hydrology will therefore enable decision makers to introduce policies aimed at reducing the detrimental effects of future environmental changes on water resources. Understanding types and impacts of major environmental stressors in representative and critical regions of the basin is crucial for developing of effective response strategies for sustainable land- and water-resource management in the Eastern Nile Basin in general, and at the Tana and Beles watersheds in particular. In this study, serially completed and homogenized rainfall and temperature dataset are maintained from 1980 to 2013 to fill-in the gap which characterized previous studies on trend analyses. The new hydroclimatic data revealed that the climate the study region has become wetter and warmer. The proportional contribution of main rainy season rainfall to annual total rainfall has increased. This might result in high runoff and ultimately flooding as well as erosion and sedimentation in the source region of the Blue Nile, and siltation in the downstream reservoirs unless soil and water conservation measures are taking place. In the Tana sub-basin, it is found that expansion of cultivation land and decline in woody shrub are the major contributors to the rise in surface run-off and to the decline in the groundwater component from 1986 to 2010. Similarly, decline of woodland and expansion of cultivation land are found to be the major contributors to the increase in surface run-off and water yield. They also contributed to the decrease in groundwater and actual evapotranspiration components in the Beles watershed. Increased run-off and reduced baseflow and actual evapotranspiration would have negative impacts on water resources, especially in relation to erosion and sedimentation in the upper Blue Nile River Basin. As a result, expansion of cultivation land and decline in woody shrub/woodland appear to be major environmental stressors affecting local water resources. GCMs simulated near-future annual total rainfall and average temperature were used to investigate the sensitivity of the catchment to near-future CC. The results showed an increase in streamflow in the annual and the main rainy season, but decrease in the dry period when compared to the baseline period. Catchment response for future LULC scenario showed opposite effect to that of near-future CC. The combined effects of climate change and LULC dynamics can be quite different from the effects resulting from LULC or CC alone. At the outlet of the Tana watershed, streamflow response is amplified under concurrent land cover and climate change scenarios compared to the baseline scenario; but the streamflow has an augmenting response at the outlet of the Beles watershed under future climate change and land use scenarios compared to that of current period. The important inference from these findings is that it could be possible to alleviate intense floods or droughts due to future climate change by planning LULC to achieve particular hydrological effects of land cover in the basin. Continuing expansion of cultivation land and decrease in natural vegetation, coupled with increased rainfall due to climate change, would result in high surface runoff in the main rainy season, which would subsequently increase flooding, erosion and sedimentation in already degraded lands. Sound mitigation measures should therefore be applied to reduce these adverse environmental consequences. On the other hand, the simulated climate and land-use change impacts on the Tana watershed hydrological regime might increase the availability of streamflow to be harnessed by water-storage structures. In conclusion, the present study has developed an innovative approach to identify the major environmental stressors of critical source region of the Blue Nile River in order to effectively managing the water resources and climate risk. Understanding the catchment responses to environmental changes improves sustainability of the water resources management particularly given that the hydropower and the irrigation schemes are recently established for energy and food security.:TABLE OF CONTENTS LIST OF ABBREVIATIONS LIST OF FIGURES LIST OF TABLES 1. General Introduction 2. The study area 3. Gap Filling and Homogenization of Climatological Datasets in the Headwater Region of the Upper Blue Nile Basin, Ethiopia Abstract 3.1. Introduction 3.1.1. Data 3.2. Methodology 3.2.1. Quality control and gap filling 3.2.2. Homogenization 3.3. Results and Discussion 3.3.1. Gap filling 3.3.2. Homogeneity 3.3.3. Verification of the homogenization 3.3.4. Impact of homogenization on the rainfall and temperature series 3.4. Conclusions Acknowledgements 4. Revisiting trend analysis of hydroclimatic data in the Upper Blue Nile basin based on homogenized data Abstract 4.1 Introduction 4.2 Data and Methodology 4. 2.1 Data 4. 2.2 Linear trend 4. 2.3 Trend magnitude 4.3 Results and Discussions 4.3.1. Linear mean climate trends 4.3.1.1. Rainfall 4.3.1.2. Maximum Temperature (Tmax) 4.3.1.3. Minimum Temperature (Tmin) 4.3.1.4. Mean temperature (Tmean) 4.3.1.5. Diurnal temperature range (DTR) 4.3.1.6. Streamflow 4.3.2. Effect of homogenization on Tmax, Tmin, Tmean and DTR linear trends 4.3.3. Linear extreme climate trends 4.3.1. Temperature 4.3.2. Precipitation 4.4 Conclusions Acknowledgements 5. Recent Changes in Land Use/Cover in the Headwater Region of the Upper Blue Nile Basin, Ethiopia 85 Abstract 5.1 Introduction 5.2 Materials and Methods 5.2.1 Data used and image pre-processing 5.2.2 Classification accuracy assessment 5.2.3 Extent and rate of change 5.2.4 Detecting the most systematic transitions (dominant signals of change) 5.4 Results and Discussion 5.4.1 Accuracy assessment 5.4.2 Extent and rate of LULC changes 5.4.3 Rate of land use and land cover change 5.4.4 Detection of most systematic transitions 5.5 Conclusions Acknowledgements 6. Hydrological Responses to Land use/cover Changes in the Tana and Beles Watersheds, the Upper Blue Nile, Ethiopia Abstract 6.1 Introduction 6.2 Method 6.2.1 Hydrological modeling 6.2.2 Partial least squares regression 6.3 Results and Discussion 6.3.1 Calibration and validation of SWAT 6.3.2 Impacts of LULC changes on hydrology at the basin scale 6.3.3 Contribution of changes in individual LULCs to hydrological components 6.4 Conclusions Acknowledgements 7. Combined Impact of Climate and Land Use Changes on Hydrology in the Tana and Beles Sub-Basins, Upper Blue Nile, Ethiopia Abstract 7.1 Introduction 7.2 Methodology 7.2.1 Simulation 7.2.2 Climate change scenarios 7.2.3 LULC change scenarios 7.3 Results and Discussion 7.3.1 Future versus current LULC impact on the basin hydrology 7.3.2 Future versus baseline climate 7.3.3 Impact of combined future climate and LULC changes on hydrology 7.4 Uncertainties and Limitations 7.5 Conclusions Acknowledgements 8. Overall Conclusions, Recommendations and Future Research Directions 8.1. Overall Conclusions 8.2 Recommendations and Directions for further research References

<p>  </p> <p>Dicamba (3,6-dichloro-2-methoxybenzoic acid) and 2,4-D (2,4-dichlorophenoxyacetic acid) are two widely used herbicides for broadleaf weed control in soybeans. However, off-target application of dicamba and 2,4-D can cause severe damage to sensitive vegetation and crops. Early detection and assessment of off-target damage caused by these herbicides are necessary to help plant diagnostic labs and state regulatory agencies collect more information of the on-site conditions so to develop solutions to resolve the issue in the future. In 2021, the study was conducted to detect damage to soybean leaves caused by dicamba and 2,4-D by using LeafSpec, an accurate handheld hyperspectral leaf scanner. . High resolution single leaf hyperspectral images of 180 soybean plants in the greenhouse exposed to nine different herbicide treatments were taken 1, 7, 14, 21 and 28 days after herbicide spraying. Pairwise PLS-DA models based on spectral features were able to distinguish leaf damage caused by two different modes of action herbicides, specifically dicamba and 2,4-D, as early as 2 hours after herbicide spraying. In the spatial distribution analysis, texture and morphological features were selected for separating the dosages of herbicide treatments. Compared to the mean spectrum method, new models built upon the spectrum, texture, and morphological features, improved the overall accuracy to over 70% for all evaluation dates. The combined features are able to classify the correct dosage of the right herbicide as early as 7 days after herbicide sprays. Overall, this work has demonstrated the potential of using spectral and spatial features of LeafSpec hyperspectral images for early and accurate detection of dicamba and 2,4-D damage in soybean plants.</p> <p>   </p>

Les traits fonctionnels foliaires sont des paramètres clés des processus écologiques dans les forêts. Malgré les progrès réalisés dans la cartographie de traits foliaires à partir d’imagerie hyperspectrale, il subsiste un besoin de construire des modèles spectres-traits spécifiques à un environnement donné, entraînés à partir de mesures à l’échelle de l’arbre, afin d'améliorer la précision des cartes locales de traits. Nous avons cartographié 12 traits fonctionnels foliaires dans une forêt tempérée mixte à partir d’imagerie hyperspectrale aéroportée. Des échantillons foliaires de cimes d'arbres (n = 166), couvrant un total de 16 espèces, ont été recueillis à l'aide d'une plateforme de drone pour mesurer des traits foliaires d’arbres individuels, à partir desquels la réflectance spectrale de la couronne a également été mesurée. Des modèles de régression des moindres carrés partiels (PLSR) ont été utilisés pour prédire les traits foliaires à partir des spectres de réflectance à l’échelle de l’arbre (400-2400 nm). Ces modèles ont prédit la masse foliaire par unité de surface (LMA), la surface foliaire spécifique (SLA) et l'épaisseur en eau (EWT) avec une haute précision (R2 > 0.8, %RMSE < 15). Les modèles de concentration de pigments, de l'azote et de la cellulose ont montré une performance modérée (R2 = 0.53–0.68, %RMSE = 17.24–21.31). Les performances les plus faibles ont été observées pour la lignine, le carbone, le contenu en matière sèche (LDMC) et l'hémicellulose (R2 = 0.24–0.44, %RMSE = 20.67–26.13). Des cartes à haute résolution spatiale (1.25 m pixel-1) de traits foliaires ont été produites pour l'ensemble de la zone d'étude de 16 km2. Notre étude s'ajoute aux recherches approfondies visant à utiliser la télédétection pour surveiller la biodiversité des traits fonctionnels à plus grande échelle et fournit des modèles qui saisissent la variation intraspécifique de nombreuses espèces d'arbres d'une forêt tempérée mixte de l'est du Canada. / Foliar functional traits are key drivers of ecological processes in forests. Despite progress in forest foliar trait mapping from imaging spectroscopy, there is a need to build environment-specific, spectra-trait models trained from tree-level measurements to improve the accuracy of local trait maps. We mapped 12 foliar functional traits in a mixed temperate forest using airborne imaging spectroscopy. Top-of-canopy foliar samples from tree crowns (n = 166), representing a total of 16 species, were collected using a drone platform to measure foliar traits for individual trees, from which tree-level crown spectra were also determined. Partial least squares regression (PLSR) models were used to predict foliar traits from tree-level reflectance spectra (400-2400 nm). These models predicted leaf mass per area (LMA), specific leaf area (SLA) and equivalent water thickness (EWT) with high accuracy (R2 > 0.8, %RMSE < 15). Models for pigment, nitrogen and cellulose concentrations showed a moderate performance (R2 = 0.53–0.68, %RMSE = 17.24–21.31). Poorest performance was observed for lignin, carbon, leaf dry mass content (LDMC) and hemicellulose (R2 = 0.24–0.44, %RMSE = 20.67–26.13). High-resolution (1.25 m pixel-1) foliar trait maps were produced for the entire 16-km2 study area. Our study adds to the extensive research aiming to use remote sensing to monitor forest functional trait biodiversity at larger scales and provides models that capture intraspecific variation across many tree species from a mixed temperate forest in eastern Canada.

Information seeking and information avoidance are the mechanisms humans natural used for coping with uncertainties and adapting to environmental stressors. Uncertainties are rooted in knowledge gaps. In social sciences, the relationship between knowledge gaps and perceived risk have received little attention. A review of the information science literature suggests that few studies have been devoted to the investigation of the role of this relationship in motivating information-seeking behavior. As an effort to address the lack of theory building in the field of information science, this study attempts to construct a model of information seeking under risk (MISR) by examining the relationships among perceived risk, knowledge gap, fear arousal, risk propensity, personal relevance, and deprivation and interest curiosity as antecedents to motivation to seek information. An experimental approach and a scenario-based survey method are employed to design the study. Partial least square structural equation modeling (PLS-SEM) analysis was conducted to test the relationships in the proposed model. Perceived risk was found to be a highly significant predictor of information seeking in moderately high-risk situations. Similarly, personal relevant has a significant negative effect on perceived risk and its interaction with knowledge gap motivates information seeking.

Customers adoption of Internet banking has become a widely-researched topic, although it is fair to state that some research gaps still exist. This research aims to fill some of the research gaps by examining the factors that determine the relevant behaviour of three different categories of Internet banking customers in China (i.e. current users, non-users, and discontinued users), and by developing two conceptual models that are derived from different, but complementary, theoretical approaches. The Decision Making Model and the Service and Relationship Evaluation Model are developed in this research. The Decision Making Model is grounded in the technology acceptance model (TAM) and it incorporates an additional construct of perceived value of using Internet banking. Additionally, the Service and Relationship Evaluation Model is derived from the service quality evaluation and relationship quality evaluation literature. Unlike in most other Internet banking adoption studies, these two conceptual models are used complementarily to deliver a comprehensive understanding of customers Internet banking adoption in China. The models are tested using a sample of 614 Chinese Internet banking customers collected via mall-intercept personal interviews based on questionnaires. Partial Least Square (PLS) path modelling and mediation analysis are applied to test the hypotheses advanced in the two models. The key findings of this research show that perceived value is a major factor for explaining customers Internet banking adoption, thus indicating to the banks that they should reduce costs associated with using Internet banking while providing more (perceived) benefits to customers; the importance of incorporating perceived value in Internet banking adoption model(s) is also demonstrated. The findings also confirm that perceived usefulness and perceived ease of use are important factors that determine the adoption of Internet banking by all categories of customers. Current users and non-users perceptions of their behavioural control over using Internet banking contribute to their adoption of Internet banking, and such control perceptions are shaped by self-efficacy, perceived government support and technological support. Additionally, it is demonstrated that both current users and discontinued users perceived value and perceived service quality of Internet banking have positive associations with their satisfaction with Internet banking, which lead to their Internet banking adoption. Moreover, the findings reveal that current users are more likely to continue with Internet banking if they are affectively committed to their banks; they are less likely to continue with Internet banking if they are calculatively committed to their banks due to the costs associated with leaving the banks. These therefore indicate the importance of establishing high-quality customer-bank relationships and placing less strict switching cost barriers that impose less pressure on their existing customers. This research contributes to the Internet banking adoption literature by (i) identifying the important category of Internet banking discontinued users, apart from current users and non-users; and (ii) using two complementary conceptual models, which are grounded in different theoretical streams, to investigate the relevant adoption behaviour of all three categories of Internet banking customers. It hence delivers a comprehensive understanding of personal customers adoption of Internet banking in China.

Dans un marché en permanente progression et pour répondre aux besoins des brasseurs en malt de qualité, la maîtrise du procédé de maltage est indispensable. La qualité du malt est fortement dépendante des conditions opératoires, en particulier des conditions de trempe, mais également de la qualité de la matière première : l'orge. Dans cette étude, nous avons établi des modèles polynomiaux qui mettent en relation les conditions opératoires et la qualité du malt. Ces modèles ont été couplés à nos algorithmes génétiques et nous ont permis de déterminer les conditions optimales de maltage, soit pour atteindre une qualité ciblée de malt (friabilité), soit pour permettre un maltage à faible teneur en eau (pour réduire la consommation en eau et maîtriser les coûts environnementaux de production) tout en conservant une qualité acceptable de malt. Cependant, la variabilité de la matière première est un facteur limitant de notre approche. Les modèles établis sont en effet très sensibles à l'espèce d'orge (printemps, hiver) ou encore à la variété d'orge utilisée. Les modèles sont surtout très dépendants de l'année de récolte. Les variations observées sur les propriétés d'une année de récolte à une autre sont mal caractérisées et ne sont donc pas intégrées dans nos modèles. Elles empêchent ainsi de capitaliser l'information expérimentale au cours du temps. Certaines propriétés structurelles de l'orge (porosité, dureté) ont été envisagées comme nouveaux facteurs pour mieux caractériser la matière première mais ils n'ont pas permis d'expliquer les variations observés en malterie.Afin de caractériser la matière première, 394 échantillons d'orge issus de 3 années de récolte différentes 2009-2010-2011 ont été analysés par spectroscopie MIR. Les analyses ACP ont confirmé l'effet notable des années de récolte, des espèces, des variétés voire des lieux de culture sur les propriétés de l'orge. Une régression PLS a permis, pour certaines années et pour certaines espèces, de prédire les teneurs en protéines et en béta-glucanes de l'orge à partir des spectres MIR. Cependant, ces résultats, pourtant prometteurs, se heurtent toujours à la variabilité. Ces nouveaux modèles PLS peuvent toutefois être exploités pour mettre en place des stratégies de pilotage du procédé de maltage à partir de mesures spectroscopiques MIR / In a continuously growing market and in order to meet the needs of Brewers in high quality malt, control of the malting process is a great challenge. Malt quality is highly dependent on the malting process operating conditions, especially on the steeping conditions, but also the quality of the raw material: barley. In this study, we established polynomial models that relate the operating conditions and the malt quality. These models have been coupled with our genetic algorithms to determine the optimal steeping conditions, either to obtain a targeted quality of malt (friability), or to allow a malting at low water content while maintaining acceptable quality of malt (to reduce water consumption and control the environmental costs of malt production). However, the variability of the raw material is a limiting factor for our approach. Established models are very sensitive to the species (spring and winter barley) or to the barley variety. The models are especially highly dependent on the crop year. Variations on the properties of a crop from one to another year are poorly characterized and are not incorporated in our models. They thus prevent us to capitalize experimental information over time. Some structural properties of barley (porosity, hardness) were considered as new factors to better characterize barley but they did not explain the observed variations.To characterize barley, 394 samples from 3 years of different crops 2009-2010-2011 were analysed by MIR spectroscopy. ACP analyses have confirmed the significant effect of the crop-years, species, varieties and sometimes of places of harvest on the properties of barley. A PLS regression allowed, for some years and for some species, to predict content of protein and beta-glucans of barley using MIR spectra. These results thus still face product variability, however, these new PLS models are very promising and could be exploited to implement control strategies in malting process using MIR spectroscopic measurements