Multivariate analysis for quality control of agrifood materials using near infrared spectroscopy

Seguridad y calidad alimentaria son uno de los conceptos más demandados actualmente en la industria agroalimentaria. La mayoría de análisis de control de los productos alimentarios se lleva a cabo mediante métodos tradicionales (vía húmeda). Los principales problemas relacionados con este tipo de análisis son el consumo de tiempo para la obtención de los resultados de una sola muestra, el coste del análisis, así como la limitación en cuanto a su implantación en la línea de producción o en el campo, entre otros. Paralelamente al desarrollo e innovación tecnológica, numerosos métodos han sido implementados para la determinación, evaluación y control de la calidad de los productos agroalimentarios en las últimas décadas. Estos métodos están basados en la detección de varias propiedades tanto físicas como químicas correlacionadas con ciertos factores cualitativos de los productos. Uno de los métodos más difundido y aún en desarrollo debido a su gran aplicabilidad, es la espectroscopía de infrarrojo cercano (tecnología NIRS, Near Infrared Spectroscopy). Han pasado más de 20 años desde su primera introducción como potente herramienta hecha por Karl Norris en el análisis de la composición de los cereales. El planteamiento de esta tesis nace de la necesidad, cada vez mayor, del control de los parámetros de calidad de los productos agroalimentarios de manera rápida y precisa. La categorización del trigo en función de su calidad o el valor añadido que adquiere la soja según el porcentaje de proteína o grasa presente en una determinada variedad ha llevado al estudio de la aplicación de la espectroscopía de infrarrojo cercano en dichos productos. El objetivo general de la investigación ha consistido en la aplicación de la tecnología NIRS para la determinación de parámetros de calidad en muestras de...Food safety and quality are currently the most popular concepts in the food industry. Usually, most control analyses of food products are carried out by conventional methods (wet chemistry). However, some of the main negative issues of these methods are: they are time consuming in order to obtain the results of a single sample, the raising price and the limitation on its implementation in the production line or in the field, among others. At the same time to the technological innovation and development, during the last decades many methods have been implemented for the identification, assessment and quality control of food products. These methods are based on the detection of various physical and chemical properties correlated with certain product quality factors. One of the most widespread due to its wide applicability is the near-infrared spectroscopy (NIRS technology, Near Infrared Spectroscopy). It has been over 20 years since its first introduction as a powerful tool made by Karl Norris in the analysis of the composition of the grains. The approach of this thesis arises from the increasing need of fast and accurate analyses of quality parameters control on food products. The categorization of wheat in terms of quality and the added value acquired by the percentage of soy protein or fat in a particular variety has led to the study of the application of near infrared spectroscopy in these products. The general objective of the research has been the application of NIRS technology for the determination of quality parameters in wheat and soybean samples. As a result, this study has led to the development of four chapters: - "Development of robust soybean NIR Calibration Models with temperature compensation and high variability in the data basis." This chapter was focused on the development of robust calibrations by adding in the group of samples instrumental and environmental variability..

Air Quality and Source Apportionment

Atmospheric particulate matter (PM) is known to have far-ranging impacts on human health through to climate forcing. The characterization of emission sources and the quantification of specific source impacts to PM concentrations significantly enhance our understanding of, and our ability to, eventually predicting the fate and transport of atmospheric PM and its associated impacts on humans and the environment. Recent advances in source apportionment applications have contributed unique combinations of chemical and numerical techniques for determining the contributions of specific sources, including diesel exhaust and biomass burning. These advances also identify and help characterize the contributions of previously uncharacterized sources. Numerical modeling has also enabled estimations of contributions of emission sources to atmospherically processed PM in urban and rural regions. Investigation into the emissions sources driving air quality is currently of concern across the globe. This Special Issue offers studies at the intersection of air quality and source apportionment for study areas in China, Germany, Iceland, Mexico, and the United States. Studies cover diverse methods for chemical characterization and modeling of the impact of different emission sources on air quality

Identifying gene regulatory networks common to multiple plant stress responses

Stress responses in plants can be defined as a change that affects the homeostasis of pathways, resulting in a phenotype that may or may not be visible to the human eye, affecting the fitness of the plant. Crosstalk is believed to be the shared components of pathways of networks, and is widespread in plants, as shown by examples of crosstalk between transcriptional regulation pathways, and hormone signalling. Crosstalk between stress responses is believed to exist, particularly crosstalk within the responses to biotic stress, and within the responses to abiotic stress. Certain hormone pathways are known to be involved in the crosstalk between the responses to both biotic and abiotic stresses, and can confer immunity or tolerance of Arabidopsis thaliana to these stresses. Transcriptional regulation has also been identified as an important factor in controlling tolerance and resistance to stresses. In this thesis, networks of regulation mediating the response tomultiple stresses are studied. Firstly, co-regulation was predicted for genes differentially expressed in two or more stresses by development of a novel multi-clustering approach, Wigwams Identifies Genes Working Across Multiple Stresses (Wigwams). This approach finds groups of genes whose expression is correlated within stresses, but also identifies a strong statistical link between subsets of stresses. Wigwams identifies the known co-expression of genes encoding enzymes of metabolic and flavonoid biosynthesis pathways, and predicts novels clusters of co-expressed genes. By hypothesising that by being coexpressed could also infer that the genes are co-regulated, promoter motif analysis and modelling provides information for potential upstream regulators. The context-free regulation of groups of co-expressed genes, or potential regulons, was explored using models generated by modelling techniques, in order to generate a quantitative model of transcriptional regulation during the response to B. cinerea, P. syringae pv. tomato DC3000 and senescence. This model was subsequently validated and extended by experimental techniques, using Yeast 1-Hybrid to investigate the protein-DNA interactions, and also microarrays. Analysis of mutants and plants overexpressing a predicted regulator, Rap2.6L, by gene expression analysis identified a number of potential regulon members as downstream targets. Rap2.6L was identified as an indirect regulator of the transcription factor members of three potential regulons co-expressed in the stresses B. cinerea, P. syringae pv. tomato DC3000 and long day senescence, allowing the confirmation of a predicted gene regulatory network operating in multiple stress responses

Nir Spectral Techniques and Chemometrics Applied to Food Processing

Tesis por compendio[ES] Las técnicas rápidas, no destructivas y libres de químicos tienen una demanda creciente en muchos campos de la industria. Las técnicas de espectroscopia de infrarrojo cercano (NIRS) y imágenes hiperespectrales NIR (NIR-HSI) han mostrado un gran potencial para determinar los parámetros de calidad de los alimentos, autenticar productos alimenticios, detectar el fraude, entre otras. En la NIRS, las medidas se toman en puntos específicos, detectando solo una pequeña porción; en la NIR-HSI, la información espectral y espacial se combinan, lo que la convierte en una opción adecuada para muchos productos alimenticios, ya que son matrices muy heterogéneas. Por lo tanto, este estudio tuvo como objetivo revisar la aplicación de NIRS (dispersivos), NIR de Transformada de Fourier (FT) y HSI en la evaluación de los parámetros de calidad de harina de trigo y productos a base de trigo, así como para la autenticación y determinación de la composición de estos productos. Además, este trabajo tuvo como objetivo identificar y clasificar diferentes tipos de muestras de fibra agregadas a la semolina y pasta producidas por estas formulaciones, y monitorear el proceso de cocción de esta pasta enriquecida en fibra mediante técnicas espectrales. Además, se objetivó aplicar HSI a otro producto en polvo, por lo que se cuantificó el contenido de pectina en las cáscaras de naranja. Primero, se adquirieron espectros NIR para comparar la precisión en la clasificación de muestras enriquecidas con fibra, para cuantificar la cantidad de estas fibras y verificar su distribución en muestras de semolina. Para la clasificación se utilizaron el Análisis de Componentes Principales (PCA) y el Soft Independent Modelling of Class Analogy (SIMCA). Los modelos de regresión de mínimos cuadrados parciales (PLSR) aplicados a espectros NIR-HSI mostraron R²P entre 0,85 y 0,98 y RMSEP entre 0,5 y 1, y los modelos se utilizaron para construir los mapas químicos para verificar la distribución de fibra en las superficies de las muestras. Además, se probó el NIR-HSI junto con Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) para investigar la capacidad de evaluación, resolución y cuantificación de la distribución de fibra en la pasta. Los resultados mostraron R²P entre 0.28 y 0.89,% de falta de ajuste (LOF) <6%, varianza explicada sobre 99% y similitud entre espectros puros y recuperados sobre 96% y 98%. Además, se probó VIS/NIR-HSI en el modo de transmisión como una alternativa objetiva para la clasificación de muestras de pasta según el tiempo de cocción. El análisis discriminante lineal (LDA) mostró valores de sensibilidad y especificidad entre 0,14-1,00 y 0,51-1,00, respectivamente, y una tasa de ausencia de error (NER) superior a 0,62. El análisis discriminante de mínimos cuadrados parciales (PLSDA) mostró valores de sensibilidad y especificidad entre 0,67-1,00 y 0,10-1,00, respectivamente, y NER superiores a 0,80. Los resultados de este trabajo mostraron que la técnica NIR-HSI se puede utilizar para la identificación y cuantificación de la fibra agregada a la semolina. Además, NIR-HSI y MCR-ALS pueden identificar la fibra en la pasta. La HSI en el modo de transmisión demostró ser una técnica adecuada como alternativa objetiva para la clasificación de muestras de pasta según el tiempo de cocción como una forma de automatizar la determinación de los atributos de la pasta. La determinación del contenido de pectina en cáscaras de naranja se investigó usando NIR-HSI. LDA mostró mejores resultados de discriminación considerando tres grupos: bajo (0-5%), intermedio (10-40%) y alto (50-100%) contenido. Los modelos PLSR basados en espectros completos mostraron mayor precisión (R2> 0,93, RMSEP entre 6,50 y 9,16% de pectina) que los basados en pocas longitudes de onda seleccionadas (R2 entre 0,92 y 0,94, RMSEP entre 8,03 y 9,73% de pectina). Los resultados demuestran el potencial de NIR-HSI para cuantificar el contenido de pectina en las cáscaras de naranja, proporcionando una técnica valiosa para los productores de naranja y las industrias de procesamiento.[CA] Les tècniques ràpides, no destructives i lliures de químics tenen una demanda creixent en molts camps de la indústria. Les tècniques d'espectroscopia d'infraroig proper (NIRS) i d'imatges hiperespectrals NIR (NIR-HSI) han demostrat tindre un gran potencial per a determinar paràmetres de qualitat d'aliments, autenticar productes alimentaris, detectar frau entre altres aplicacions. Mentre que en la NIRS proper les mesures es prenen en punts específics de la mostra i es detecta una porció menuda, en la HSI es combina informació espectral i espacial de tal manera que és una opció adient per a molts tipus de productes alimentaris, ja que són matrius molt heterogènies. Per tant, este estudi va tindre com objectiu revisar tota l'aplicació de NIRS (dispersius), NIR de Transformada de Fourier (FT) i HSI en l'avaluació dels paràmetres de qualitat de la farina de blat i els productes a base de blat, així com per a l'autenticació i determinació de la composició d'estos productes. A més a més, este estudi va tindre com objectiu identificar i classificar diferents tipus de mostres de fibra afegides a la semolina i pasta produïdes per formulació de fibra i semolina, i monitorar mitjançant tècniques espectrals el procés de cocció d'aquesta pasta enriquida amb fibra. A més, este treball va tindre com objectiu aplicar HSI a un altre producte en pols, de tal manera que es va quantificar el contingut de pectina en les corfes de taronja. Primer, es van adquirir espectres NIR per comparar la precisió en la classificació de mostres enriquides amb fibra, per quantificar estes fibres i verificar la seua distribució en mostres de sèmola. Per a la classificació es van emprar l'Anàlisi de Components Principals (PCA) i el SIMCA (Soft Independent Modelling of Class Analogy). Els models de regressió de mínims quadrats parcials (PLSR) aplicats a espectres NIR-HSI mostraren R²P entre 0,85 i 0,98 i RMSEP entre 0,5 i 1% de contingut de fibra, i els models s'utilitzaren per construir els mapes químics per verificar la distribució de fibra en les superficies de les mostres. Així mateix, es va provar NIR-HSI amb Multivariate Curve Resolution-Alternating Least Square (MCR-ALS) per a investigar la capacitat d'avaluació, resolució i quantificació de la distribució de fibra en la pasta enriquida. Els resultats mostraren un R²P entre 0,28 i 0,89%, lack of fit (LOF) 0,93, RMSEP entre 6,50 i 9,16% de pectina) que els basats en longituds d’ona seleccionades (R2 entre 0,92 i 0,94, RMSEP entre 8,03 i 9,73% de pectina). Els resultats demostren el potencial de NIR-HSI per a quantificar el contingut de pectina en corfa de taronja i proporcionen una tècnica valuosa per als productors de taronja i les indústries de processament.[EN] Fast, non-destructive and chemical-free techniques are in increasing demand in many fields of the industry. Near-infrared spectroscopy (NIRS) and NIR hyperspectral imaging (NIR-HSI) techniques have shown great potential in determining food quality parameters, authenticating food products, detecting food fraud, among many other applications. While in near infrared spectroscopy, the measurements are taken at specific points on the sample, detecting only a small portion; in hyperspectral imaging, spectral and spatial information are combined, making it a suitable choice for many food products, since they are very heterogeneous matrices. Therefore, this study aimed to review all the application of (dispersive) NIRS, Fourier Transform (FT) NIR, and HSI in assessing wheat flour and wheat-based products quality parameters, as well for the authentication and determination of composition of these products. Moreover, this work aimed to identify and classify different types of fibre samples added to the semolina and pasta produced by semolina-fibre formulations, and to monitor the cooking process of this fibre-enriched pasta by spectral techniques. In addition, this work had the aim of applying HSI to other powdered product, so the pectin content in orange peels was quantified. First, NIR spectra were acquired to compare the accuracy in the classification of fibre-enriched samples, to quantify the amount of these fibres and verify their distribution on semolina samples. Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were used for classification. Partial Least Squares Regression (PLSR) models applied to NIR-HSI spectra showed R2P between 0.85 and 0.98, and RMSEP between 0.5 and 1% of fibre content, and the models were used to construct the chemical maps to check the fibre distribution on the samples surface. Moreover, NIR-HSI together with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), was tested to investigate the ability for the evaluation, resolution and quantification of fibre distribution in enriched pasta. Results showed coefficient of determination of validation (R²V) between 0.28 and 0.89, % of lack of fit (LOF) 0.93, RMSEP between 6.50 and 9.16% of pectin) than those based on few selected wavelengths (R² between 0.92 and 0.94, RMSEP between 8.03 and 9.73%). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries.This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior- Brasil (CAPES) [Finance Code 001]; São Paulo Research Foundation (FAPESP) [grant numbers 2015/24351-2, 2017/17628-3, 2019/06842- 0]; and by projects AEI PID2019-107347RR-C31 and PID2019-107347RR-C32, and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014-2020. The authors would like to thank Nutrassim Food Ingredients company for the donation of the fibre samples, the support provided by Enrique Aguilar María, Carlos Alberto Velasquez Hernández, Diego Hernández Catalán, Carlos Ruiz Catalá and Andrés Estuardo Prieto López during system installation, experimental analysis and data acquisition.Teixeira Badaró, A. (2021). Nir Spectral Techniques and Chemometrics Applied to Food Processing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178758Compendi

A Systematic Review of Methods for Investigating Climate Change Impacts on Water-Energy-Food Nexus

This is the final version. Available on open access from Springer via the DOI in this recordData Availability: The data sources are listed in the ‘Selected articles’ in References, some articles are not cited in the manuscript but are selected for review (Carvajal et al. 2017; Solaun and Cerda 2017; Yin et al. 2017; Bieber et al. 2018; Gaudard et al. 2018; Hasan and Wyseure 2018; Gohar et al. 2019; Ortiz-Bobea 2019; Rigden et al. 2020; Beltran-Peña and D’Odorico 2022; Kumar et al. 2023).Water, energy and food are important for human survival and sustainable development. With climate change, investigating climate change impacts on Water-Energy-Food nexus has been a topic of growing interest in recent years. However, there is a lack of a systematic review of the current state and methodologies of Water-Energy-Food nexus studies under climate change. Here, we review research articles investigating climate change impacts on Water-Food, Water-Energy and Water-Energy-Food nexus over last seven years. The existing methods and tools, spatial scales, and future climate scenarios setting in these articles are summarised and analysed. We found that the analyses methods could be divided into four categories (physics-based modelling, statistical methods, supervised learning and operation optimisation), among them, physics-based modelling accounts for the largest proportion. The reviewed studies cover a range of scales from site scale to global, with most studies focusing on the regional scale. Models used for small to middle scale are mainly related to hydrology and water resource, while large-scale modelling is based on interdisciplinary models. Future climate scenarios setting include emission scenarios and global warming scenarios based on Global Climate Models (GCMs). A number of future research challenges have been identified. These include spatial scale and resolution, internal physical mechanism, application of novel artificial intelligence models, extreme climate events, potential competition in nexus systems as well as data and model uncertainty.China Scholarship Counci

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

FUNCTIONAL CHARACTERIZATION OF TOMATO PROSYSTEMIN AND PROSYSTEMIN REGIONS: NOVEL TOOLS FOR PLANT DEFENSE

Prosystemin (ProSys) is a pro-hormone of 200 aminoacidic residues which releases a bioactive peptide hormone of 18 amino acids called Systemin (Sys) involved in the activation of a complex signaling cascade that leads to the production of defense compounds. The tomato genome contains only one copy of Prosys gene; it is composed of 4176 bp and is structured into 11 exons, of which the last one codes for Sys. Sys peptide was traditionally considered as the principal actor that confers protection against both biotic and abiotic environmental challenges observed in tomato plant overexpressing the ProSys. Thus, a single peptide hormone is capable of eliciting multiple defense pathways to counteract a wide range of unfavourable conditions for the plant. So far, it was unknown whether ProSys had any biological function other than being an intermediate in the synthesis of Sys. However, recent evidences suggest that Prosys devoid of the Sys sequence contributes to defense responses. This observation prompted us to investigate the biochemical and structural features of the ProSys protein. To this purpose ProSys has been expressed in BL21 (DE3) E. coli cells and purified. A detailed characterization of this pro-hormone by means of multidisciplinary approach revealed for the first time that this precursor behaves like an intrinsically disordered protein (IDP) possessing intrinsically disordered regions (IDRs) within the sequence. However, to find out an alternative delivery strategy not relying on transgenic plants, we decided to investigate the effects of exogenous application of the recombinant pro-hormone on the defense responses and its potential use as a plant protection tool in tomato. In particular, plant assays revealed that ProSys direct treatment of leaves is biologically active being very effective in the induction, both locally and systemically, of tomato defense-related genes, conferring protection against different pests. To our knowledge, this is the first biotic stress related IDP identified in plants, suggesting new interesting insights on the role of IDPs. into plant response against biotic stressors. IDPs are functionally important proteins lacking a stable or ordered three-dimensional structure. Despite being highly flexible, it has been demonstrated that IDPs have crucial roles in signal transduction process, cell-cycle regulation, gene expression and molecular recognition. The role of IDPs in these processes has been systematically studied in the animal kingdom. In contrast, less reports of these proteins from the plant kingdom are available in the scientific literature. In plant biology, IDPs play crucial roles among plant stress responses, signaling, and molecular recognition pathways, that resemble the functional roles of ProSys in the tomato defense pathways activated upon several biotic and abiotic stresses. These evidences aimed our study focused on the establishment of a relationship between ProSys structure and its biological activity. To this purpose different regions of ProSys have been expressed in BL21 (DE3) E. coli cells, purified and then characterized by a biophysical and biochemical point of view. Results showed that the recombinant fragments are disordered in agreement with what previously shown for the whole precursor. It was subsequently investigated whether the recombinant ProSys Fragments had any biological activity in activating defense responses upon biotic or abiotic attacks. In particular, by using a combination of gene expression analysis and bioassays, we proved that the exogenous supply of the recombinant ProSys Fragments to tomato plants promotes early and late plant defense genes, but only two fragments (namely Fragment I and III, encompassing the N-terminal part of the protein) were found to be the most promising. In addition, it was observed that the latter ones counteracted the development of Spodoptera littoralis larvae and the fungal leaf colonization. These results suggest that the direct application of these recombinant products, which are safe to humans and no-target organisms, may represent an exploitable tool for crop protection

Beta-Glucan in Foods and Health Benefits

This Special Issue entitled “β-glucan in foods and health benefits” reports on the health benefits of indigestible carbohydrates with respect to metabolic diseases and immune functions. The effects of β-glucan have been investigated through the use isolated preparations or natural dietary fibers from whole grain cereals and brans, yeasts, or Euglena. This Special Issue includes original research articles that are based on human intervention studies that address the effects of β-glucan on metabolic diseases and immune function-related markers as well as in vitro and in vivo studies. It also reviews the health benefits of β-glucans in humans

Análise de propriedades intrínsecas e extrínsecas de amostras biométricas para detecção de ataques de apresentação

Orientadores: Anderson de Rezende Rocha, Hélio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os recentes avanços nas áreas de pesquisa em biometria, forense e segurança da informação trouxeram importantes melhorias na eficácia dos sistemas de reconhecimento biométricos. No entanto, um desafio ainda em aberto é a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintéticas, a partir das informações biométricas originais de um usuário legítimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biométrica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintéticas. Por exemplo, em biometria facial, uma tentativa de ataque é caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vídeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em íris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de íris de um usuário-alvo ou mesmo padrões de textura sintéticas. Nos sistemas biométricos de impressão digital, os usuários impostores podem enganar o sensor biométrico usando réplicas dos padrões de impressão digital construídas com materiais sintéticos, como látex, massa de modelar, silicone, entre outros. Esta pesquisa teve como objetivo o desenvolvimento de soluções para detecção de ataques de apresentação considerando os sistemas biométricos faciais, de íris e de impressão digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruído; em propriedades intrínsecas das amostras biométricas (e.g., mapas de albedo, de reflectância e de profundidade) e em técnicas de aprendizagem supervisionada de características. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponível contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuínos em um sistema biométrico facial, os quais foram coletados com a autorização do Comitê de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrínsecas das amostras biométricas relacionadas aos artefatos que são adicionados durante a fabricação das amostras sintéticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos métodos propostos na literature que se utilizam de métodos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrínsecas das faces, estimadas a partir da informação de sombras presentes em sua superfície; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de características relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos métodos considerados estado da arte para as diferentes modalidades biométricas consideradas nesta tese. A pesquisa também considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender características relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponíveis para o desenvolvimento e a avaliação de soluções para a detecção de ataques de apresentaçãoAbstract: Recent advances in biometrics, information forensics, and security have improved the recognition effectiveness of biometric systems. However, an ever-growing challenge is the vulnerability of such systems against presentation attacks, in which impostor users create synthetic samples from the original biometric information of a legitimate user and show them to the acquisition sensor seeking to authenticate themselves as legitimate users. Depending on the trait used by the biometric authentication, the attack types vary with the type of material used to build the synthetic samples. For instance, in facial biometric systems, an attempted attack is characterized by the type of material the impostor uses such as a photograph, a digital video, or a 3D mask with the facial information of a target user. In iris-based biometrics, presentation attacks can be accomplished with printout photographs or with contact lenses containing the iris patterns of a target user or even synthetic texture patterns. In fingerprint biometric systems, impostor users can deceive the authentication process using replicas of the fingerprint patterns built with synthetic materials such as latex, play-doh, silicone, among others. This research aimed at developing presentation attack detection (PAD) solutions whose objective is to detect attempted attacks considering different attack types, in each modality. The lines of investigation presented in this thesis aimed at devising and developing representations based on spatial, temporal and spectral information from noise signature, intrinsic properties of the biometric data (e.g., albedo, reflectance, and depth maps), and supervised feature learning techniques, taking into account different testing scenarios including cross-sensor, intra-, and inter-dataset scenarios. The main findings and contributions presented in this thesis include: the creation of a large and publicly available benchmark containing 17K videos of presentation attacks and bona-fide presentations simulations in a facial biometric system, whose collect were formally authorized by the Research Ethics Committee at Unicamp; the development of novel approaches to modeling and analysis of extrinsic properties of biometric samples related to artifacts added during the manufacturing of the synthetic samples and their capture by the acquisition sensor, whose results were superior to several approaches published in the literature that use traditional methods for image analysis (e.g., texture-based analysis); the investigation of an approach based on the analysis of intrinsic properties of faces, estimated from the information of shadows present on their surface; and the investigation of different approaches to automatically learning representations related to our problem, whose results were superior or competitive to state-of-the-art methods for the biometric modalities considered in this thesis. We also considered in this research the design of efficient neural networks with shallow architectures capable of learning characteristics related to our problem from small sets of data available to develop and evaluate PAD solutionsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140069/2016-0 CNPq, 142110/2017-5CAPESCNP