Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra

Coupled modelling of land surface microwave interactions using ENVISAT ASAR data

In the last decades microwave remote sensing has proven its capability to provide valuable information about the land surface. New sensor generations as e.g. ENVISAT ASAR are capable to provide frequent imagery with an high information content. To make use of these multiple imaging capabilities, sophisticated parameter inversion and assimilation strategies have to be applied. A profound understanding of the microwave interactions at the land surface is therefore essential. The objective of the presented work is the analysis and quantitative description of the backscattering processes of vegetated areas by means of microwave backscattering models. The effect of changing imaging geometries is investigated and models for the description of bare soil and vegetation backscattering are developed. Spatially distributed model parameterisation is realized by synergistic coupling of the microwave scattering models with a physically based land surface process model. This enables the simulation of realistic SAR images, based on bioand geophysical parameters. The adequate preprocessing of the datasets is crucial for quantitative image analysis. A stringent preprocessing and sophisticated terrain geocoding and correction procedure is therefore suggested. It corrects the geometric and radiometric distortions of the image products and is taken as the basis for further analysis steps. A problem in recently available microwave backscattering models is the inadequate parameterisation of the surface roughness. It is shown, that the use of classical roughness descriptors, as the rms height and autocorrelation length, will lead to ambiguous model parameterisations. A new two parameter bare soil backscattering model is therefore recommended to overcome this drawback. It is derived from theoretical electromagnetic model simulations. The new bare soil surface scattering model allows for the accurate description of the bare soil backscattering coefficients. A new surface roughness parameter is introduced in this context, capable to describe the surface roughness components, affecting the backscattering coefficient. It is shown, that this parameter can be directly related to the intrinsic fractal properties of the surface. Spatially distributed information about the surface roughness is needed to derive land surface parameters from SAR imagery. An algorithm for the derivation of the new surface roughness parameter is therefore suggested. It is shown, that it can be derived directly from multitemporal SAR imagery. Starting from that point, the bare soil backscattering model is used to assess the vegetation influence on the signal. By comparison of the residuals between measured backscattering coefficients and those predicted by the bare soil backscattering model, the vegetation influence on the signal can be quantified. Significant difference between cereals (wheat and triticale) and maize is observed in this context. It is shown, that the vegetation influence on the signal can be directly derived from alternating polarisation data for cereal fields. It is dependant on plant biophysical variables as vegetation biomass and water content. The backscattering behaviour of a maize stand is significantly different from that of other cereals, due to its completely different density and shape of the plants. A dihedral corner reflection between the soil and the stalk is identified as the major source of backscattering from the vegetation. A semiempirical maize backscattering model is suggested to quantify the influences of the canopy over the vegetation period. Thus, the different scattering contributions of the soil and vegetation components are successfully separated. The combination of the bare soil and vegetation backscattering models allows for the accurate prediction of the backscattering coefficient for a wide range of surface conditions and variable incidence angles. To enable the spatially distributed simulation of the SAR backscattering coefficient, an interface to a process oriented land surface model is established, which provides the necessary input variables for the backscattering model. Using this synergistic, coupled modelling approach, a realistic simulation of SAR images becomes possible based on land surface model output variables. It is shown, that this coupled modelling approach leads to promising and accurate estimates of the backscattering coefficients. The remaining residuals between simulated and measured backscatter values are analysed to identify the sources of uncertainty in the model. A detailed field based analysis of the simulation results revealed that imprecise soil moisture predictions by the land surface model are a major source of uncertainty, which can be related to imprecise soil texture distribution and soil hydrological properties. The sensitivity of the backscattering coefficient to the soil moisture content of the upper soil layer can be used to generate soil moisture maps from SAR imagery. An algorithm for the inversion of soil moisture from the upper soil layer is suggested and validated. It makes use of initial soil moisture values, provided by the land surface process model. Soil moisture values are inverted by means of the coupled land surface backscattering model. The retrieved soil moisture results have an RMSE of 3.5 Vol %, which is comparable to the measurement accuracy of the reference field data. The developed models allow for the accurate prediction of the SAR backscattering coefficient. The various soil and vegetation scattering contributions can be separated. The direct interface to a physically based land surface process model allows for the spatially distributed modelling of the backscattering coefficient and the direct assimilation of remote sensing data into a land surface process model. The developed models allow for the derivation of static and dynamic landsurface parameters, as e.g. surface roughness, soil texture, soil moisture and biomass from remote sensing data and their assimilation in process models. They are therefore reliable tools, which can be used for sophisticated practice oriented problem solutions in manifold manner in the earth and environmental sciences

Airborne thermography and ground geophysical investigation for detecting shallow ground disturbance under vegetation

This thesis discusses the potential of airborne thermal prospection for detecting shallow ground disturbance beneath vegetation based on images acquired by the NERC Airborne Thematic Mapper (ATM) at thermal infrared wavelengths. Shallow ground disturbance creates a differential heat flux due to a variation in the thermal properties between disturbed and undisturbed soils. When observed above a canopy, the effect of vegetation growth on the thermal regime of the underlying soils is poorly understood. The research extends current understanding by examining areas where ground disturbance is known to exist under variable vegetation cover at an archaeological site at Bosworth, Leicestershire and areas of abandoned mine activity on Baildon Moor, W. Yorkshire and in the N. Pennine Orefield, Weardale. The investigation focuses on qualitative image interpretation techniques, where anomalies on day and night thermal images are compared with those manifest on the multispectral images, and a more quantitative approach of Apparent Thermal Inertia (ATI) modelling. Physical thermal inertia is a parameter that is sensitive to volumetric variations in the soil, but cannot be measured directly using remote sensing techniques. However, an apparent thermal inertia is determined by examining the day and night temperature contrast of the surface, where spatial variations can signify potential features buried in the near-surface environment. Ground temperature profiling at the Bosworth site indicates that diurnal heat dissipates between 0.20-0.50m at an early stage in vegetation development with progressively lower diurnal amplitudes observed at 0.20m as the vegetation develops. Results also show that the time of diurnal maximum temperature occurs progressively later as vegetation develops, implying an importance for thermal image acquisition. The quantitative investigation concentrates on the Bosworth site where extensive ground geophysical prospection was performed and vertical soil samples extracted across features of variable multispectral, thermal and ATI response to enable comparison of the observed airborne thermal response with physical soil properties. Results suggest that there is a high correlation between ATI and soil moisture properties at 0.15-0.25m depth (R(^2)=0.99) at an early stage in cereal crop development but has a high correlation at a wider depth range (0.10-0.30m) at a later stage in development (R(^2)=0.98). The high correlation between physical ground disturbance and the thermal response is also corroborated qualitatively with the results of the resistivity surveys. The ATI modelling reveals similar features to those evident on day or night thermal images at an early stage in vegetation growth, suggesting that thermal imaging during the day at an early stage in vegetation growth may supply sufficient information on features buried in the near-surface environment. Airborne thermal imaging therefore provides a useful complementary prospection tool for archaeological and geological applications for surfaces covered by vegetation

Optical and radar remote sensing applied to agricultural areas in europe

The global population growth, as well as the social and economic importance that the agricultural sector has in many regions of the world, makes it very important to develop methods to monitor the status of crops, to improve their management, as well as to be able to make early estimates of the agricultural production. One of the main causes of uncertainty in the production of crops is due to the weather, for example, in arid and semiarid regions of the world, periods of drought can generate big losses in agricultural production, which may result in famine. Thus, FAO, during their summit in June 2008, stressed the need to increase agricultural production as a measure to strengthen food security and reduce malnutrition in the world. Concern for increasing crop production, has generated, during the last decades, significant changes in agricultural techniques. For example, there has been a widespread use of pesticides, genetically modified crops, as well as an increase in intensive farming. In turn, the market influences crop rotations, and as a consequence, changes in the spatial distribution of crops are very common. Therefore, in order to make estimates of agricultural production, it is also necessary to map regularly the crop fields, as well as their state of development. The aim of this thesis is to develop methods based on remote sensing data, in the radar and optical spectral regions, in order to monitor crops, as well as a to map them. The results of this thesis can be combined with other techniques, especially with models of crop growth, to improve the prediction of crops. The optical remote sensing methods for classifying and for the cartography of crops are well established and can be considered almost operational. The disadvantage of the methods based on optical data is that they are not applicable to regions of the world where cloud coverage is frequent. In such cases, the use of radar data is more advisable. However, the classification methods using radar data are not as well established as the optical ones, therefore, there is a need for more scientific studies in this field. As a consequence, this thesis focuses on the classification of crops using radar data, particularly using AIRSAR airborne data and ASAR satellite data

MICROBIAL FOOD FERMENTATIONS: INNOVATIVE APPROACH USING INFRARED SPECTROSCOPY

Interest in food quality and production has increased in recent decades, mainly due to changes in consumer habits and behaviour, and the development and increase in the industrialisation of food chains. The growing demand for quality and safety in food production obviously calls for high standards for quality and process control, which in turn requires appropriate analytical tools for the analysis of food. In particular, many unit operations in industrial food processes are related to microbial fermentation, namely milk coagulation in dairy, dough in bakery, as well as must fermentation in wine and beer productions. Fermentation is one of the earliest methods adopted to obtain value-added food products with an extended shelf life. Humans applied fermentation to make products such as wine, mead, cheese and beer long before the biochemical process behind was understood. Even now the biochemistry of fermentations commonly applied in food processes has many aspects which have not been fully investigated yet. Briefly, fermentation is any metabolic process in which an organism converts a carbohydrate, such as starch or sugar, into an alcohol and/or organic acids entailing modifications in the final product. The transition to industrial productions entailed a standardisation of the fermentation processes and the obtained products. Currently, the main objective is to develop instruments able to be implemented in the process in order to closely monitor the products of interest and to detect in real time the smallest changes bringing to a more effective process control and management. In this contest, spectroscopy revealed to be an interesting analytical method to monitor food fermentations processes. Spectroscopy is a secondary analytical method which consists in recording the absorption changes due to the interaction of electromagnetic radiation with the matter. The basic principle is that every chemical compound absorbs, transmits or reflects light (electromagnetic radiation) over a certain range of wavelengths. The information recorded can, thus, be used to measure the amount of a known chemical substance if correlated to a reference analysis. Spectroscopy reveals to be one of the most useful methods for quantitative analysis in various fields such as chemistry, physics, biochemistry, material and chemical engineering and clinical applications. Indeed, any application that deals with chemical substances or materials can use this technique. Moreover, the improved instrumentation for performing in-line and on-line analyses at industrial level has rose in the last decades giving the opportunity to obtained real-time information about the progression of any process and allowed its implementation as strategy to monitor complex systems as food production. The food monitoring with spectroscopic devices has become possible thanks to Chemometrics (i.e. multivariate data analysis). Chemometrics has widely demonstrated to be the perfect partner to spectroscopy to deal with the complex chemical/physical systems that food matrix conforms. Chemometrics is able to extract relevant information from redundant and noisy spectra. In the last years the combination of spectroscopic analysis and Chemometrics was applied crosswise in food processes for qualitative and quantitative modelling in industrial applications. In particular, for the determination of compositional parameters affecting quality and safety of fermented food products such as wine, beer, yoghurt, vinegar and bakery products. Nevertheless, concerning complex biotransformations spectroscopy and Chemometrics are emerging techniques in food fermentation monitoring. The purpose of this PhD Thesis is the demonstration of the feasibility in the combination of spectroscopy and Chemometrics as an innovative working procedure for real time monitoring of food fermentation processes. The thesis consists of five main chapters Chapter 1 Chapters 2 and 3 present an introduction to the main fermentations and their control from an historical prospective, the employed analytical techniques (Near infrared and Mid Infrared spectroscopy) and to Chemometrics, respectively. Chapter 4 presents the experiments carried out on various fermentation food processes. In this section seven studies represent examples of applications of different spectroscopic methods in strong combination with Chemometrics to food fermentation processes as yogurt fermentation (Paper I, II and Paper III), wine malolactic transformation (Paper IV and V) and beer (Paper VI and VII). In addition to the mentioned contributions a brief state of the art and some preliminary results are reported regarding sourdough leaving process monitoring. The two basic Chemometrics tools, principal component analysis (PCA) and partial least squares (PLS) regression were mainly applied to the spectroscopic data collected from the fermentation processes in order to evaluate the results and focus on the relevant information and to correlate the spectral features with different relevant physical and/or chemical parameters such as the concentration of the main chemical species involved in the biotransformation. In particular, the principal components (PCs) scores obtained by monitoring wine and yoghurt fermentations were modelled as function of time to find out kinetic parameters, as maximum acceleration and deceleration of the transformation, important for the process control (PAPER I and V). The spectroscopic data obtained during yoghurt and beer fermentation monitoring were also investigated with multivariate curve resolution- alternating least squares (MCR-ALS), proving to be able to resolve multi-component mixtures into a simpler model (PAPER II and VII). The main conclusive remarks on the presented studies are given in Chapter 5 (CONCLUSIONS), including a discussion of challenges and future perspectives for further application of spectral monitoring and chemometrics in fermented food processes

Drying of granulated products by conventional heat transfer and high-frequency techniques

In this study the contribution of the different heat transfer methods in the drying of granulated materials is analysed from the viewpoint of the available electro-heat technology. Although the work has been directed to present day commercial applications and proposes definite drying schemes the research has been focused on high frequency heating techniques, which appear to be the most promising alternatives as far as medium and large throughput continuous processes are concerned. An experimental rig has been designed and built, this unit has been used to obtain a preliminary set of data on the performance of the high frequency drying process under different operating conditions