Cachaça Classification Using Chemical Features and Computer Vision

Cacha¸ca is a type of distilled drink from sugarcane with great economic importance. Its classification includes three types: aged, premium and extra premium. These three classifications are related to the aging time of the drink in wooden casks. Besides the aging time, it is important to know what the wood used in the barrel storage in order the properties of each drink are properly informed consumer. This paper shows a method for automatic recognition of the type of wood and the aging time using information from a computer vision system and chemical information. Two algorithms for pattern recognition are used: artificial neural networks and k-NN (k-Nearest Neighbor). In the case study, 144 cacha¸ca samples were used. The results showed 97% accuracy for the problem of the aging time classification and 100% for the problem of woods classification.info:eu-repo/semantics/publishedVersio

A feasibility cachaca type recognition using computer vision and pattern recognition

Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.info:eu-repo/semantics/publishedVersio

Detection and recognition of moving video objects: Kalman filtering with deep learning

© 2021. All rights reserved. Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers. The first layer, which is the input image, is of 100 * 100 size. The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling. The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms

Metodologias para seleção de variáveis explicativas e detecção de inconformidades de predição aplicadas à espectroscopia por fluorescência

A capacidade de predizer eventos futuros a partir de conhecimentos históricos é a base para a modelagem preditiva. Criar um modelo capaz de quantificar variáveis de interesse, classificar ocorrências ou prever comportamentos, acompanham a evolução dos algoritmos modernos de aprendizado de máquina. Na indústria de transformação, muitas das informações mais relevantes para o controle de processos ainda são adquiridas unicamente através de técnicas laboratoriais, que são custosas, destrutivas e morosas (como, por exemplo, concentração molecular de espécies de interesse, pureza de fármacos, lubricidade de óleos, teor de proteína em alimentos, etc.). Um possível caminho para automação destes sistemas é o estudo de novos sensores capazes de capturar uma informação auxiliar de fácil obtenção, que possa ser transformada matematicamente nas saídas de interesse. Surge então a aspiração por estudos que combinam a escolha de sensores adequados com metodologias capazes de extrair de maneira eficiente a informação útil contida nestes dados. Neste trabalho são apresentadas metodologias baseadas em diferentes estratégias para seleção de variáveis explicativas e otimização de modelos empíricos. Ainda, é proposta uma metodologia para qualificação de inconformidades em novas leituras utilizando redes neurais. É apresentada a metodologia AnTSbe, um algoritmo híbrido baseado nas meta-heurísticas Colônia de Formigas (ACO) e Busca Tabu (TS), desenvolvido para otimizar a seleção de variáveis de entrada em problemas combinatórios complexos. A hibridização das meta-heurísticas visa evitar a estagnação precoce e a ciclagem de subgrupos, comuns nessas metodologias. O algoritmo também introduz o uso da expansão polinomial e combinatória das variáveis de entrada, em um esforço para incrementar o poder preditivo dos modelos. Como estudo de caso, espectroscopia por fluorescência é utilizada para predizer concentração de enxofre em diesel combustível. Os modelos preditivos ajustados foram superiores a outras técnicas descritas na literatura, com erros absolutos percentuais médios de predição menores que 4%. As adaptações propostas se mostraram eficientes, quando comparadas a pesquisas prévias com a mesma base de dados. Uma adaptação é proposta ao algoritmo AnTSbe, focada para dados de fluorescência, com o conceito de Delta Pair. Uma nova camada de otimização é introduzida no algoritmo a fim de selecionar um par Excitação/Emissão que serve como regulador do meio, tendo sua intensidade de fluorescência decrescida de todos outros os pontos do espectro. Neste estudo, são acompanhados três processos distintos de envelhecimento de cachaça, com o intuito de predizer a concentração de fenólicos na bebida ao longo do tempo, com base em dados fluorescência. A adaptação Delta Pair se mostrou especialmente funcional quando combinada com expansão de bases e para predição de cachaças envelhecidas comerciais, que não participaram da etapa de calibração dos modelos. A seguir, matrizes excitação – emissão de fluorescência captadas in situ em fermentações com S. cerevisiae foram utilizadas para calibrar uma rede neural convolucional residual, como intuito de predizer glicose, etanol e biomassa no meio biológico. Em paralelo, foi desenvolvida uma metodologia baseada em redes neurais do tipo autoencoder (AE), capazes de corretamente reconstruir os espectros originais. A metodologia utiliza o erro de reconstrução da rede AE treinada para triagem não supervisionada de novos espectros, conseguindo identificar espectros com inconformidades, e qualificar a confiança que se pode atribuir a um novo dado, baseado na magnitude deste erro. Por fim, a metodologia AnTSbe é utilizada para predizer impurezas nas correntes de uma unidade de separação de propano/propeno, expandindo o uso da metodologia para casos da indústria petroquímica com base em dados simulados de processo (e não de fluorescência). A metodologia se mostrou capaz de corretamente predizer os perfis de concentração das três colunas de separação do processo com erros absolutos percentuais médios inferiores a 5%, com foco especial para quantificação dos contaminantes em cada corrente, que precisam ser mantidos sob controle para garantir a lucratividade da operação. Os artigos desenvolvidos demonstram, inclusive na ordem apresentada, o sucesso das metodologias propostas em aprofundar a seleção de variáveis significativas e otimização de modelos empíricos preditivos. A sucessão dos casos estudados parte do desenvolvimento do algoritmo estocástico base, segue para a busca de um reforço na capacidade de generalização dos modelos otimizados baseados em espectroscopia por fluorescência, apresenta uma técnica para qualificação de novas amostras e conclui com o uso dos algoritmos desenvolvidos em um caso industrial.The ability to predict future events from historical observations is the basis for predictive modeling. Creating a model capable of quantifying variables of interest, classifying occurrences or predicting behavior, follows the evolution of modern machine learning algorithms. In the manufacturing industry, much of the most relevant information for process control is still acquired only through laboratory techniques, which are costly, destructive and time-consuming (such as, for example, molecular concentration of species, purity of drugs, lubricity of oils, protein content in food, etc.). A possible way to automate these systems is the study of new sensors capable of capturing auxiliary information of easy application, which can be mathematically transformed in the outputs of interest. This is the aspiration for studies that combine the choice of skilled sensors with methodologies capable of efficiently extracting the useful information contained in the data. In this work we propose methodologies based on different machine learning methods for the optimization of empirical models. AnTSbe methodology is presented, a hybrid algorithm based on Ant Colony (ACO) and Tabu Search (TS) metaheuristics, developed to optimize the selection of input variables in complex combinatorial problems. The hybridization of metaheuristics aims to avoid early stagnation and cycling of subgroups, common in these methodologies. The algorithm also introduces the use of polynomial and combinatorial expansion of the input variables, in an effort to increase the predictive power of the models. As a case study, fluorescence spectroscopy is used to predict sulfur concentration in diesel fuel. The adjusted predictive models were superior to other techniques from literature, with mean absolute percentage errors of prediction smaller than 4%. The proposed adaptations were efficient, when compared to previous researches with the same database. An adaptation is proposed to the AnTSbe algorithm, focused on fluorescence data, with the concept of DeltaPair. A new optimization layer is introduced in the algorithm in order to select an Excitation/Emission pair that serves as a medium regulator, having its fluorescence intensity decreased from all other points in the spectrum. In this study, three distinct cachaça aging processes are followed, in order to predict the concentration of phenolics in the spirit over time, based on fluorescence data. The DeltaPair adaptation is especially functional when combined with base expansion and for the prediction of aged commercial cachaças, which does not participate in the calibration stage of the models. Following, fluorescence excitation - emission matrices, collected in situ in fermentations with S. cerevisiae, were used to calibrate a residual convolutional neural network, in order to predict glucose, ethanol and biomass in the biological environment. In parallel, a methodology based on autoencoder-type neural networks (AE) was developed, capable of correctly reconstructing the original spectra. The methodology uses the trained AE reconstruction error for unsupervised screening of new spectra, managing to identify abnormal spectra, and to qualify the confidence that can be attributed to a new data, based on the magnitude of this error. Despite the focus on fluorescence spectroscopy data, most of the methodologies were designed to be of general use, whatever the data source, with little or no modification. Finally, the AnTSbe methodology is used to predict impurities in the streams of a propane/propylene splitter unit, expanding the use of the methodology for cases in the petrochemical industry based on simulated process data (and not fluorescence). The methodology proved to be capable of correctly predicting the concentration profiles of the three process’ separation columns with mean absolute percentage errors below 5%, with a special focus on quantifying the contaminants in each stream, which need to be kept under control to ensure profitability of the operation. The articles developed demonstrate, in the order presented, the success of the proposed methodologies in deepening the selection of significant variables and the optimization of predictive empirical models. The succession of the studied cases starts from the development of the base stochastic algorithm, goes on to seek a reinforcement in the generalizability of the optimized models based on fluorescence spectroscopy, presents a technique for qualifying new samples and concludes with the use of the algorithms developed in an industrial case

Monitoring and characterization of yeasts behavior under fermentation processes using technometric approaches

Tese de doutoramento em Chemical and Biological EngineeringTechnometrics concerns on the development and use of statistical methods in different fields, such as biotechnological processes, in order to understand their multivariate and multidimensional complexity. Chemical changes occurring within these processes can be monitored using chemometric tools that combined with bioinformatic methodologies, can provide an enlarged overview of the process, enabling the unbiased study of metabolites and dynamic changes in response to the environmental conditions. For this purpose, different chemometric tools were used, namely relevant principal component analysis (RPCA), multi-way principal component analysis (MPCA), partial least squares logistic regression (PLSLOG) and unfolded partial least squares (U-PLS). Phenotypic and physiological behaviors of three different Saccharomyces cerevisiae strains, a laboratorial S288c, and two industrials CA11 and PE-2, were evaluated under different stress conditions. Toxic and inhibitory conditions were induced by introducing 1.0% (v/v) ethanol, 1-butanol, isopropanol, tert-Amyl alcohol, 0.2% (v/v) furfural and 0.5% (v/v) 5-hydroxymethylfurfural (5-HMF) in batch fermentations with YPD as culture medium. MPCA and PLS-LOG allowed to evidence the different behavior of S288c comparing to PE-2 and CA11, and a higher impact caused by 1-butanol, furfural and 5-HMF in phenotypic and physiological profiles. PE-2 revealed to be the most robust strain, quickly adapting to the environmental conditions, even under the highest stress conditions. It was also observed a correlation between the flocculation profile inhibition under those conditions, with an increased production of intracellular glycerol. This relationship was confirmed by PLS-LOG where intracellular glycerol and trehalose, as well as extracellular acetic acid production showed to be linked to the inhibition of CA11 cells flocculation. Metabolic changes occurring within CA11 and PE-2 fermentations in the presence of 1-butanol, furfural and 5-HMF were also evaluated, using RPCA. CA11 fermentations enhanced the production of ethanol, isovaleric acid and isoamyl acetate, whereas PE-2 favored the production of more aromatic compounds, such as esters - phenylethyl acetate, ethyl hexanoate, ethyl octanoate and ethyl dodecanoate. These results suggested that PE-2 is less susceptible to the stress effect of the three tested molecules. PLS-LOG models allowed the prediction (R2 =0.90) of the metabolic behavior of both strains during the fermentations: the presence of 1-butanol induced the production of esters ethyl acetate and isoamyl acetate (and its precursor, 3-methyl-1-butanol), as well as butyric acid (which encourages the use of both strains in bio-butanol production systems); CA11 and PE-2 synthesized furfuryl alcohol from furfural; the presence of furfural and 5-HMF induced the production and accumulation of fatty acids in the medium, to counterbalance the inhibitory effects. The impact of metabolic profile of S. cerevisiae PYCC 4653 on its antioxidant capacity, in synthetic grape juice supplemented with phenolics acids was assessed. A bioanalytical pipeline, combining electrochemical features with biochemical background was proposed, for biological systems fingerprinting and sample classification. The electrochemical profile, phenolic acids and the volatile fermentation fraction, were evaluated for 11 days, using cyclic voltammetry, target and non-target metabolic approaches, respectively. It was found that acetic acid, 2-phenylethanol and isoamyl acetate have a significative contribution for samples metabolic variability and the electrochemical features demonstrated redox-potential changes throughout the alcoholic fermentations, showing at the end, a similar pattern to normal wines. S. cerevisiae also showed the capacity of producing chlorogenic acid in the supplemented medium fermentation from simple precursors present in the minimal medium. The proposed bioanalytical pipeline proved to be a very efficient strategy for fingerprinting biological systems, by integration of the information from different chemical detectors. Finally, a non-targeted high-throughput metabolomics pipeline combining GC-MS data preprocessing with multivariate analysis, was developed and integrated in new “in-house” software, called XMetabolomics (developed during this thesis). The pipeline was built to enhance the identification of key metabolites involved in the process, through the exploration of the temporal relationships between interesting metabolites related to a chemical phenomenon. It was applied to a Port wine “forced aging” process under different oxygen saturation regimes. RPCA showed that the use of extreme oxygen saturation and high temperatures during Port wine aging induced the occurrence of chemical reactions undesirable for the aromatic profile, affecting the quality of the final product. Under those conditions an increased production of dioxane and dioxolane isomers and furfural was observed, leading to excessive degradation of the wine aromatic profile, color and taste. The production of dioxane isomer was highly correlated with the production of dioxolane isomer, benzaldehyde, sotolon, and many other metabolites whose identification could be of great interest for their contribution for the final aromatic profile of the Port wine. In sum, during this thesis, the potential of the use of chemometrics and bioinformatics approaches was explored in the characterization (by RPCA and MPCA), classification and prediction (by PLS-LOG and UPLS, respectively) of physiological, phenotypic and metabolic changes in bioprocesses as an adaptation response to environmental conditions. The joint effect of distinct variables (measured using HPLC, GCFID, GC-MS and cyclic voltammetry) in multivariate data analysis allowed enhancing the knowledge about chemical and biochemical dynamics in biotechnological processes.A tecnometria consiste no desenvolvimento e uso de métodos estatísticos em diferentes áreas, tais como processos biotecnológicos, de modo a compreender a sua complexidade multivariada e multidimensional. As alterações químicas que ocorrem nestes processos podem ser monitorizadas utilizando ferramentas de quimiometria que, associadas a métodos de bioinformática, podem proporcionar uma visão alargada do processo e logo, o estudo equitativo dos metabolitos e as alterações dinâmicas em resposta às condições ambientais. Ao longo deste trabalho, diferentes ferramentas de quimiometria foram utilizadas, nomeadamente, relevant principal component analysis (RPCA), multi-way principal component analysis (MPCA), partial least squares logistic regression (PLS-LOG) e unfolded partial least squares (U-PLS). Foi efetuado o estudo de comportamentos fenotípicos e fisiológicos de três estirpes diferentes de Saccharomyces cerevisiae, uma laboratorial, S288c, e duas industriais, CA11 e PE -2, sob diferentes condições de stress. Foram adicionadas moléculas tóxicas e inibitórias no meio YPD, nomeadamente, 1,0% (v/v) de etanol, 1-butanol, isopropanol e 2-metil-2-butanol, 0,2 % (v/v) de furfural e 0,5 % (v/v) de 5-hidroximetil- furfural (5-HMF). O MPCA e o PLS-LOG evidenciaram o diferente comportamento da estirpe S288c em relação à CA11 e PE-2, e um maior impacto causado pelo 1-butanol, furfural e 5-HMF nos perfis fenotípicos e fisiológicos. A PE-2 revelou ser a estirpe mais robusta e a que melhor se adaptou às condições ambientais impostas, mesmo sob as mais severas. Observou-se uma correlação entre a inibição do perfil de floculação nestas condições, com um aumento da produção de glicerol intracelular. Esta relação foi confirmada utilizando o PLS-LOG onde a produção de glicerol e trealose intracelulares, bem como de ácido acético extracelular mostraram estar associadas ao fenómeno de inibição da floculação das células da CA11. As alterações metabólicas que ocorrem nas fermentações utilizando a CA11 e PE- 2 na presença de 1- butanol, furfural e 5- HMF também foram avaliadas por RPCA. Enquanto a estirpe CA11 favoreceu a produção de etanol, ácido isovalérico e acetato de isoamilo, a PE-2 levou à produção de outros compostos aromáticos, tais como o acetato de feniletilo, etil hexanoato, octanoato e dodecanoato ao longo das fermentações. Estes resultados reforçam que a PE-2 é menos suscetível ao efeito stressante dessas moléculas. Os modelos PLS-LOG permitiram prever (R2 = 0,90) o comportamento metabólico de ambas as estirpes, durante as fermentações: a presença de 1-butanol induziu a produção de ésteres de acetato de etilo e acetato de isoamilo (e o seu precursor, 3-metil -1- butanol), bem como o ácido butírico (encorajando a utilização de ambas as estirpes em sistemas de produção de bio-butanol); as estirpes CA11 e PE-2 sintetizaram álcool furfurílico a partir de furfural; a presença de furfural e 5- HMF induziu a produção e acumulação de ácidos gordos, de forma a contrabalançar os efeitos inibitórios na obtenção de energia para as células, metabolizando ácidos gordos no meio. O impacto do perfil metabólico da S. cerevisiae PYCC 4653 sobre a capacidade antioxidante foi avaliado, em fermentações utilizando sumo de uva sintético suplementadas com ácidos fenólicos. Foi apresentada uma metodologia bio-analítica (combinando os perfis eletroquímico e bioquímico) para a caracterização do comportamento da levedura em resposta às perturbações impostas. O perfil eletroquímico, os ácidos fenólicos e a fração volátil das fermentações, foram avaliados durante 11 dias, utilizando a voltametria cíclica, e abordagens metabólicas supervisionadas e não supervisionadas. Verificou-se que o ácido acético, 2- feniletanol e o acetato de isoamilo têm uma contribuição significativa na variabilidade metabólica e as características electroquímicas revelaram as alterações do potencial redox durante as fermentações. O perfil eletroquímico da fermentação alcoólica mostrou, no final, um padrão semelhante ao dos vinhos reais. A S. cerevisiae também mostrou a capacidade de produzir ácido clorogénico, no meio de fermentação suplementado a partir de precursores simples, presentes no meio mínimo. A metodologia proposta provou ser uma estratégia eficiente na caracterização de fenómenos biológicos e químicos, através da integração da informação de vários detetores químicos. Por fim, uma metodologia de processamento metabólico não-direcionado e de alto-débito, combinando o pré-processamento dos dados de GC-MS com a análise multivariada, foi desenvolvida e integrada num novo software, denominado X-Metabolomics também desenvolvido no decorrer desta tese. A metodologia foi construída para melhorar a identificação dos metabolitos-chave envolvidos no processo biotecnológico, através da exploração das relações temporais entre os metabólitos interessantes relacionados ao mesmo fenómeno químico. Esta foi aplicada a um processo de “envelhecimento forçado” de vinho do Porto, sob diferentes regimes de saturação de oxigénio. O RPCA mostrou que a utilização da saturação extrema de oxigénio e de temperaturas elevadas durante o envelhecimento do vinho do Porto induziu a ocorrência de reações químicas indesejáveis para o perfil aromático, que afetam a qualidade do produto final. Nestas condições, foi observado um aumento da produção de isómeros de dioxano e dioxolano e furfural, que levaram a uma degradação excessiva do perfil aromático, cor e sabor do vinho. A produção do isómero de dioxano está altamente correlacionada com a produção de um isómero dioxolano, benzaldeído, sotolon, e muitos outros metabolitos, cuja identificação poderia ser de grande interesse pela sua contribuição para o perfil aromático final do vinho do Porto. Em suma, durante esta tese, foi explorado o potencial da utilização de abordagens de tecnometria, incluindo métodos de quimiometria e bioinformática, na caracterização (por RPCA e MPCA), classificação e previsão (por PLS-LOG e U-PLS respetivamente) das alterações fisiológicas, fenotípicas e metabólicas em bioprocessos, em resposta às condições ambientais. O efeito conjunto de distintas variáveis na análise multivariada, permitiu ampliar o conhecimento acerca das dinâmicas químicas e bioquímicas em processos biotecnológicos

Beyond the Anthropocene: Multispecies Encounters in Contemporary Latin American Literature, Art, and Film

abstract: In the face of what many scientists and cultural theorists are calling the Anthropocene, a new era characterized by catastrophic human impact on the planet’s geologic, atmospheric, and ecological makeup, Latin American writers, artists, and filmmakers today from various disciplinary and geographical positionalities are engaging in debates about how to respond ethically to this global crisis. From an interdisciplinary perspective that incorporates cutting-edge theories in multispecies ethnography, material ecocriticism, and queer ecology, this study examines multispecies relationships unfolding in three telescoping dimensions—corporealities, companions, and communities—in contemporary Latin American cultural production while uncovering indigenous and other-than-dominant epistemologies about human-nonhuman entanglements. I argue that contemporary cultural expression uncovers long, overlapping histories of social and environmental exploitation and resistance while casting the moment of encounter between individuals of different species as hopeful figurations of human-nonhuman flourishing beyond the Anthropocene. Instead of remaining hopelessly mired in the dire geographies of planetary decline, the works of Uruguayan writer Teresa Porzecanski, Mexican author Daniela Tarazona, Mexican textile sculptor Alejandra Zermeño, Argentine filmmaker Lucía Puenzo, Colombian installation artist María Fernanda Cardoso, Colombian poet Juan Carlos Galeano, Colombian graphic artist Solmi Angarita, and Brazilian poet Astrid Cabral dramatize a multitude of multispecies encounters to imagine the possibility of a better world—one that is already as close as our skin and as present as the nonhuman “others” that constitute our existence. These works imagine the human itself as a product of multispecies interactions through evolutionary time, multispecies companionships as formed around queer kinships, and biocultural communities as emerging through communicative, ethical encounters.Dissertation/ThesisDoctoral Dissertation Spanish 201

The political ecology of soybean farming systems in Mato Grosso, Brazil: a cross-scale analysis of farming styles in Querência-MT

Over the past two decades the expansion of soybean production in Brazil has been assessed and used as an example of the success or failure of large-scale, mechanized agricultural production. Indeed, the economic, social and environmental implications of this agricultural expansion are highly contested. Nevertheless, the complexity behind this process is rarely depicted. Instead simplistic and monolithic notions of agronegocio (agribusiness), and linear interpretations of soybean expansion are offered. These general accounts reduce agrarian dynamics, diversity of farming styles and differences in livelihoods to a homogenous phenomenon in all soybean production regions in Brazil. This limits the scope to understand processes of socio-technical, socio-economic and socio-environmental transformations and the existence of diverse pathways related to the soybean agri-food systems. This study therefore rejects the simple narratives, and argues for a more nuanced understanding of the diverse processes and dynamics between soybean farming styles and its actors' interactions as part of fast‐changing agri‐food systems. This is done through a case study approach in the municipality of Querência in the state Mato Grosso, Brazil. An examination of narratives (the ways different people talk about and construct farming and its objectives) and practices (the different farming styles and livelihood strategies) informs this analysis. In particular, the research explores how a heterogeneity of soybean farming styles – contrasting large-scale, medium-scale and smallholder soybean farmers – is constructed in a particular place, offering in turn a more nuanced account of the standard, highly polarised assessment of farming styles and their implications. It then contributes to an understanding of how policies and practices related to diverse soybean agri-food systems in Mato Grosso state are played out. This sheds light on how notions of rural development are constructed and how pathways to sustainable development are seen

Reversible photochromism of synthetic hackmanites in radiation detection and quantification

The subject of this thesis is centered on a mineral called hackmanite, also known as photochromic sodalite. It is found naturally in remote, mountainous places in Afghanistan, Pakistan, Greenland, Russia, Canada, and the United States. The natural mineral is costly to extract and – depending on the location – its optical properties and chemical impurities vary arbitrarily. Thus, it is not only more predictable, but also sustainable to synthesize the mineral in a laboratory from traceable reagents that contain known amounts of impurities. The synthesis route used in the experimental section in this work is a solid-state method where the reagents are mixed and heated in an oven at 850 °C and reduced with a hydrogen‒nitrogen gas mixture. The product, hackmanite (Na8Al6Si6O24(Cl,S)2), shows properties including luminescence, persistent luminescence, and reversible photochromism upon exposure to UV, X, gamma, nuclear, or particle radiation. Hackmanite’s photochromism is of particular interest since the coloration from white to pink can be reversed with visible light or heat, and this cycle can be repeated indefinitely. Hackmanite is thus able to react to its surrounding radiation atmosphere, and what makes the property even more interesting is that upon high-energy gamma radiation exposure the material “remembers” the exposure with a change of its color centers. In UV-induced coloration, the mechanism involves an electron transfer from a disulfide anion to a nearby chloride vacancy, which is a defect in the lattice due to the requirement of charge neutrality in the crystal. However, in X-ray- or other highenergy radiation-induced coloration the incident energies are so high that the coloration is caused by core-shell electrons and subsequent holes trapping after thermalization. Due to the nature of the coloration process, hackmanite’s application region spans from the high-energy gamma radiation to UV, however the material can also be used to detect visible light since the bleaching process (electrons returning to disulfide ions from the trap) occurs in the visible wavelength region. This property can be used for taking a photograph, as is shown in this thesis. KEYWORDS: hackmanite, photochromism, radiation detection, dosimetry, photographyTämän väitöskirjan aiheena on hackmaniitti-niminen mineraali, joka tunnetaan myös nimellä fotokrominen sodaliitti. Sitä esiintyy luonnossa syrjäisillä vuoristoseuduilla Afganistanissa, Pakistanissa, Grönlannissa, Venäjällä, Kanadassa ja Yhdysvalloissa. Luonnonmineraalin louhinta on kallista ja kestämätöntä, ja sen optiset ominaisuudet ja kemialliset epäpuhtaudet vaihtelevat satunnaisesti riippuen sijainnista. Näin ollen on ennakoitavampaa ja kestävämpää syntetisoida mineraalia laboratoriossa reagensseista, jotka ovat jäljitettäviä ja sisältävät tunnetut määrät epäpuhtauksia. Tämän työn kokeellisessa osassa synteesit toteutettiin kiinteän olomuodon menetelmällä, jossa lähtöaineiden seos kuumennetaan uunissa 850 °C:ssa ja pelkistetään vetytyppikaasuseoksella. Tuotteella eli hackmaniitilla (Na8Al6Si6O24(Cl,S)2), on ominaisuuksinaan luminesenssi, jälkiloiste ja palautuva fotokromismi altistuessaan UV-, röntgen-, gamma‑, ydin- ja hiukkassäteilylle. Hackmaniitin fotokromismi on erityisen kiinnostava ominaisuus, sillä vaaleanpunaiseksi värjätty hackmaniitti voidaan palauttaa takaisin valkoiseksi näkyvällä valolla tai lämmöllä, ja tätä sykliä voidaan toistaa loputtomasti. Tämän ominaisuuden tekee vielä mielenkiintoisemmaksi se, että gammasäteilyaltistuksen yhteydessä materiaali ”muistaa” korkeaenergisen altistuksensa värikeskuksensa ‒ joka on olennainen rakenne värjäytymismekanismissa ‒ muutoksella. UV-värjäytymisessä mekanismi sisältää elektronin virittymisen disulfidianionista läheiseen kloridivakanssiin, mikä on kiteen varaustasapainovaatimuksen mukaisesti muodostunut hilavirhe. Röntgen- tai muun korkeaenergisen säteilyn aiheuttamassa värjäytymisessä energiat ovat kuitenkin niin suuria, että värjäytymisen aiheuttaa sisäkuorten elektronien ja aukkojen loukkuuntuminen termalisaation jälkeen. Värjääntymisprosessin ansiosta hackmaniitin käyttöalue ulottuu korkeaenergisestä gammasäteilystä UV-säteilyyn, mutta materiaalia voidaan käyttää myös näkyvän valon havaitsemiseen, sillä haalenemisprosessi (elektronien palaaminen loukuista takaisin disulfidi-ioneihin) tapahtuu näkyvällä aallonpituusalueella. Tätä ominaisuutta voidaan käyttää valokuvaamisessa. ASIASANAT: hackmaniitti, fotokromismi, säteilyn havainnointi, dosimetria, valokuvau

Establishment of authenticity and typicality of sugarcane from Madeira Island, and its derivative sugarcane honey, based on molecular, chemical and chemometric analysis

Food authenticity has become a worldwide concern, foresting the importance of the development of strategies to guarantee the quality and safety of food products. The purpose of this doctoral thesis was the establishment of the typicality and authenticity of sugarcane cultivars from Madeira Island, and its main derivative, sugarcane honey. The followed strategy was based on the identification of typical volatile, sugars and furan derivatives profiles in sugarcane honey processed by the certified producer Fábrica Mel-de-Cana Ribeiro Sêco, and compare it with sugarcane-based syrups from non-certified regional producers and from producers from different geographical regions. Also, the typical volatile profile throughout all stages of sugarcane honey processing were identified during four years, allowing the evaluation of influence of each stage in the formation of its typicality. In addition, the typical volatile profile of all sugarcane cultivars currently cultivated in Madeira Island were identified, namely amarela, canica, roxa, radiada, verde and violeta. Different analytical methodologies based on effective extractive techniques and high-resolution chromatographic methods were successfully developed, optimized and validated, namely solid-phase microextraction combined with gas chromatography-mass spectrometry for the volatile profile, microextraction by packed sorbent combined with ultra-high performance liquid chromatography with diode-array detector for the furan derivatives profile, and ultrasound-assisted liquid-liquid extraction combined with liquid chromatography with a refractive index detector for sugars profile. Furthermore, an innovative and powerful Quality-by-Design approach was selected for development of the methodologies used for quantitation of furan derivatives and SGs. An exhaustive chemometric analysis based on one-way ANOVA, principal component analysis, partial least squares, linear discriminant analysis and hierarchical clustering analysis was successfully applied to recognize the typical profiles of sugarcane honey. This doctoral thesis represents the first attempt to define the typicality, authenticity and traceability of sugarcane honey from Madeira Island, providing a highly valuable information to support its European Union certification.A autenticidade alimentar tornou-se uma preocupação mundial, evidenciando a importância do desenvolvimento de estratégias para garantir a qualidade e segurança dos produtos alimentares. O objetivo do presente doutoramento foi estabelecer a tipicidade e autenticidade das cultivares de cana de-açúcar da Ilha da Madeira, e do seu principal derivado, o mel-de-cana. A estratégia seguida baseou-se na identificação de perfis típicos de voláteis, açúcares e derivados do furano no mel-de-cana processado pela empresa certificada Fábrica Mel-de-Cana Ribeiro Sêco, e compará-lo com xaropes de cana-de-açúcar de produtores regionais não certificados e produtores de diferentes regiões geográficas. Também foram identificados os perfis voláteis típicos ao longo de todas as etapas de processamento do mel-de-cana, permitindo avaliar a influência de cada etapa na formação de sua tipicidade. Além disso, foram identificados os perfis voláteis típicos de todas as cultivares de cana-de-açúcar atualmente cultivadas na Ilha da Madeira, nomeadamente amarela, canica, roxa, radiada, verde e violeta. Diferentes metodologias analíticas baseadas em técnicas extrativas e métodos cromatográficos foram desenvolvidos, otimizados e validados com sucesso, nomeadamente a microextração em fase sólida combinada com cromatografia gasosa e espectrometria de massa para perfil volátil, microextração por sorvente empacotado combinado com cromatografia líquida de ultra-alto desempenho com detetor díodos para perfil de derivados de furano, e extração líquido-líquido assistida por ultrassom combinada com cromatografia líquida com detetor de índice de refração para perfil de açúcares. Além disso, uma abordagem inovadora e poderosa de Quality-by-Design foi selecionada para o desenvolvimento das metodologias usadas para quantificação de derivados de furano e açúcares. A análise quimiométrica exaustiva baseada em ANOVA unilateral, análise de componentes principais, mínimos quadrados parciais, análise discriminante linear e análise de agrupamento hierárquico foi aplicada com sucesso para o reconhecimento de perfis típicos de mel-de-cana. O presente doutoramento representa a primeira tentativa de definir a tipicidade, autenticidade e rastreabilidade do mel-de-cana, fornecendo uma informação altamente valiosa para apoiar a sua certificação na União Europeia