\r\n\tAlthough the microorganism was later described by several other researchers with multiple synonyms, Escherich was recognized as the first, establishing the definitive name of the microbe as Escherichia coli in 1954.
\r\n\tIn 1933, Alfred Adam showed that certain serotypes of "dyspepsia Koli" (as he called the diarrheagenic E. coli strains) were implicated in epidemics of pediatric diarrhoea. In 1944, Kauffman proposed a classification scheme that is still in use today for the purpose of differentiating commensal types from pathogens and subclassifying them.
\r\n\tEscherichia coli, in its natural habitat, lives in the intestines of most healthy mammals. It is the main facultative anaerobic organism of the digestive system. In healthy individuals, that is, if the bacterium does not acquire genetic elements that encode virulent factors, the bacterium acts as a commensal forming part of the intestinal microbiota and thus helping the absorption of nutrients.
\r\n\tIn humans, E. coli colonizes the gastrointestinal tract of a neonate by adhering to the mucus of the large intestine within a few hours of birth. Since then, it remains in a relationship of mutual benefit. However, these commensal strains can cause infections in immunosuppressed patients.
\r\n\r\n\tPathogenic strains of E. coli, on the other hand, as soon as they colonize a healthy host, can cause infections of varying severity in the intestine, urinary tract, meningitis, and sepsis, among other infections.
\r\n\tDiarrhea caused by pathogenic strains of E. coli is an important cause of death in children under 5 years of age, especially in sub-Saharan Africa and South Asia, where it is one of the four most important causes of moderate and severe diarrhea, potentially lethal An increase in mortality is associated with enteropathogenic strains.
\r\n\tUrinary tract infections are more common in women because of the short length of the urethra (25 to 50 mm) compared to men (about 15 cm). Among the elderly, urinary infections tend to be of the same proportion between men and women.
\r\n\tBecause the bacteria invariably enter the urinary tract through the urethra (an ascending infection), poor hygiene habits can predispose to infection; however, other factors become important, such as pregnancy, benign or malignant hypertrophy of the prostate, and in In many cases, the initiating event of the infection is unknown. Although ascending infections are the cause of lower urinary tract infections and cystitis, this is not necessarily the cause of upper infections such as pyelonephritis, which may have a hematogenous origin.
In industrialized nations, manufacturing has become a key growth factor. “Great Britain was the first industrializer and became the technological leader of the world economy. Manufacturing became the main engine of the economic growth in the 19th century, spreading manufacturing production technologies to other countries” [1]. Digital manufacturing (Artificial Intelligence, bigdata analytics, cloud computing, among others) is changing the nature of manufacturing production. The adoption of these technologies (by developing countries) can foster inclusive and sustainable industrial development and the achievement of the Sustainable Development Goals [2].
Wood industry is key for sustainability and an important economic activity in many countries. In recent years, manufactured wood products for construction have had special relevance. These represent 38.1% of wood-based products worldwide [3].
As a biological material, wood is variable in its physical and anatomical properties. Wood mechanical or chemical transformation processes are affected. In manufacturing processes, process variables must be managed to achieve quality and productivity standards. In industrial systems, productivity and product quality are affected by multiple variables. The “wood material” adds an additional complexity degree. Traditionally, human experience has been able to control many variables and maintain the operating system. However, the human capacity has limits and to use sensors and computers is needed to help online decision-making.
For decades, multiple sensors for generic physical variables such as pressure, speed, or temperature have been developed. Specific sensors capable of measuring and characterizing wood, including destructive and non-destructive testing, have been developed. For industrial use in manufacturing process control non-destructive characterization is the most important. This allows monitoring and control (usually by humans) the process in real time. In a competitive scenario, to achieve higher operating factors, lack assets availability is critical. To have in real time information on the operational behavior allows online decision-making. Lack of data and its analysis does not allow to forecast and prescribe operation behaviors and improve performance. Decision-making has become more complex, uncertain, and rapidly changing external conditions. However, the use of online data in industrial settings is still incipient. Computing power and robust algorithms capable of predicting behavior in complex environments using data are recent.
The fourth industrial revolution presents great opportunities for wood manufacturing processing. Sensor’s development, high computing capacity, industrial internet (Internet of Things, IoT) and learning algorithms can allow a much better handling of uncertainty and material variability. Algorithms based on artificial intelligence make possible online decisions and prediction of phenomena that are difficult to model with conventional techniques. Sensor’s availability and Machine Learning techniques allow the intelligent capture, display, and data analysis [4, 5]. Machine Learning (and Deep Learning) uses the history of data, of positive or negative experiences to model real processes and automatically conclude for other situations. The choice of the algorithms depends to the problem. Therefore, new opportunities are open for academia and industry to improve the industrial wood processing. Studying appropriate performant models and determining which variables and which sensors to use, among other considerations, are part of the data engineering and futures research work.
This work presents the industry 4.0 scopes, components, and opportunities for the wood industrial manufacturing. Data collection and engineering are the focus. Some author’s examples are shown.
In recent decades, the industry has evolved toward more intensive use of digital technologies. And this has passed from traditional automation to a new industrial revolution, the fourth industrial revolution or Industry 4.0. The first revolution was the introduction of mechanization and steam power. The second was the incorporation of mass production and division of labor. And the third revolution incorporated electronics, automation and CAX (Computer Aided X) technologies.
The 1980s changed the direction of industrial production policies. Taylorian mass production evolved toward a production of a variety of products, with costs like those of the production of large quantities. In 1990s, the focus was on integrating manufacturing automation technologies mainly robotics, CNC (Computer Numerical Control), CAD / CAM (Computer Aided Design/ Computer Aided Engineering), and automatic control (Advanced Manufacturing Technologies). Computer Integrated Manufacturing (CIM) emphasized data integration into the production cycle of the firm, it was the third industrial revolution [6]. Manufacturing evolved from the intensive labor use in traditional manufacturing to a sophisticated set of processes based on information technology [4].
In 2011, Industry 4.0 emerges as a focus of the competitiveness of Germany’s industry [7]. It is defined as networks that incorporate Cyber-Physical Systems that handle bigdata and use Artificial Intelligence. Industry 4.0 is based on developments of the last 20 years, at least four: research in artificial intelligence, better computing capacity and speed, internet development and wireless communication. For example, the combination of distributed systems, self-organizing systems and artificial intelligence was the prelude to what today in industry 4.0 is known as Cyber-Physical Systems [8, 9, 10, 11].
Data science and artificial intelligence would be the “core” of Industry 4.0 [2]. The consequences are the virtual factory (or digital twin) and autonomous machines (Cyber-physical Systems) capable of interacting “intelligently” with other machines and humans [12]. Data analysis (data science) makes it possible to make decisions and predict dynamic phenomena that are difficult to model with conventional techniques. Industry 4.0 is the data revolution, especially in the manufacturing industry [13]. Today object is about the massive use of data and analysis for the design and operation of industrial systems.
Many authors have defined components part of this fourth revolution. Some place more emphasis on hardware devices and others on software elements. However, both data and automatic analysis are the base and more common denominator: the data science approach. Several methodologies exist to drive data projects, but in general that consists in fourth steps: to know the problem, to understand the data, to extract features and to model an analyze [14]. Data engineering is complementary and fundamental to achieve implementations: from data capture to the action over the physical system. First task in data engineering is to make available data: to select sensors, to process signals and to generate descriptors and data warehouse. Second, it is to know the physical processes, to understand and visualize data and to extract features. And finally, tasks are modeling and implementation for actions (Figure 1).
Data engineering process.
Without data engineering 4.0 technologies would not be possible. In this work, four technological components are visited: Industrial Internet, Cloud Computing, Virtual Factory and Cyber-Physical Systems.
II Is to use the internet for industrial purposes. All is called Internet of Things (IoT). An IoT system consists of Industrial Wireless Networks (IWN) and Internet of Things (IoT) [15]. It includes machines and equipment, networks, the cloud, and terminals. “Things” and “objects” interact with each other and cooperate to achieve common goals. “IoT is capable of offering specific and personalized products. Users can customize products via web pages. Then, web servers transmit data to the industrial cloud and plants via wired or wireless networks” [5]. 5G technology will allow high speeds of communication and industrial internet feasible.
Also, it is possible to define Internet of Services (IoS). IoS allows providers to offer their services over the Internet. “IoS is emerging, based on the idea that services are made easily available through web technologies, allowing companies and private users to combine, create and offer new kind of value- added services” [16].
CC is a set of resources, including physical servers, networks, storage, and user applications accessible from Internet [17]. CC is a new concept. it is a collection of configurable computing services to be made accessible and released as specified [18]. It also allows easy and on-demand network access. Different networks, servers, storage, applications, and services resources are disponible today. Services providers, e.g., Microsoft Azure, Alibaba Cloud, Amazon, and Google Cloud, provide access through the internet. Clients pay only for the resources they use. CC services are one crucial components of the Industry 4.0 including IoT and CPSs [19].
CPS comprise intelligent devices capable of exchanging information autonomously, causing actions and controlling each other independently. “CPS are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the Internet” [20]. CPS is an integration of computation with physical world. Computers monitor and control the physical processes. Feedback loops act where physical processes affect computations and vice versa [21, 22]. Software and hardware with sensor and action are integrated (Figure 2).
CFS: Interaction of agents [
VF is defined as a virtual model that assists people and machines in the execution of their tasks. They are systems that work in the background. In 1993, the VF concept was introduced by Onosato and Iwata [23]. VF It considers the actual context information such as the position and state of an object. In a virtual factory, the CPS perform tasks, communicate, and take those actions to the real world of the plant [9]. VF include virtual organization, emulation facility and integrated simulation. In [24]. VF is defined “as an integrated simulation model of major subsystems in a factory that considers the factory as a whole and provides an advanced decision support capability.” Virtual models can guide physical entities responding to the changes in their environment and to improve operations [25].
A similar concept, Digital Twin (DT) has been proposal. In [26] DT a production line is integrated with the real production processes using a simulation model. Real-time interaction between virtual and physical world allows DTs to respond to unexpected changes in manufacturing processes more rapidly [23].
It is “manufacturing, any industry that makes products from raw materials using manual labour or machinery and that is usually carried out systematically with a division of labour. In a more limited sense, manufacturing denotes the fabrication or assembly of components into finished products on a fairly large scale” [27]. Many authors difference manufacturing and process industry [28].
Manufacturing is a discrete system and uses machines or workstations to change forms, dimensions, or surfaces. Lines or cells assembly parts to obtain final products. Process industry is a continuous system and put emphasis over chemical processes, or batch like reaction, heat, cold, to generate final product liquid, gas or solid. In the forest industry, manufacturing is concerned to the “solid” wood transformation and process industry more with pulp and paper industry.
The first wood transformation begins with the log after harvest. In the sawmill industry the main product is dry sawn wood. In the board industry products are veneers, flakes, particles, and fibers. Second transformation generates appearance and engineering products such as moldings, furniture parts, plywood, CLT (Cross Laminated Timber), OSB (Oriented Strands Board) and particle or fiberboard. Different operations can be considered: milling, molding, peeling, pressing, drying, gluing, painting, among others.
Main operations of the manufacturing industry are cutting operations that produce changes in shape, dimensions, and surfaces. Wood is an anisotropic material, but it is treated as an orthotropic material. Its mechanical properties change on the radial, tangential and longitudinal axes. This affects the “cutting” behavior according to the direction of the stress of the cutting tool [29]. Similarly, anatomy, density, singularities, and moisture content impact product quality and productivity. For example, well known is the effect of properties and species on the drying of lumber or veneer.
In cutting with or without chip, the tool interaction with the material produces cutting forces that release energy producing pressure waves and tool wear [29]. In the sawmill and remanufacturing industry, tool wear directly affects production costs due to its negative effects on dimensional and surface quality of the product. In sawmill, cutting forces wear the tooth on all faces, increasing friction. Friction changes heat and cutting angles producing inefficient cut over time. The surface quality increases its roughness.
Wear and heat of tool lead to loss of rigidity increasing kerf and dimensional inaccuracy.
In longitudinal sawing, working angles α, β and γ of the cutting tool, geometry, feed per tooth, the feed and cutting speed movement must be optimized (Figure 3). These and other variables depend on the properties of the wood and the cutting height. In high productivity sawmilling feeding speeds of over 120 m / min are driven, correct monitoring and control in real time is key. In [30], factors involved in the sawing process are classified into three categories: (1) workpiece, (2) feed, and (3) tool. Combined effects are analyzed showing a complete review of studies. Here, emphasis is to put the sensor to allow online prediction and intelligent monitoring systems and increase the productivity.
A typical sawmill tool and material interaction.
For wood manufacturing processes, influencing factors are the material, the operation, and the transformation technology. In cutting processes, factors are combined, the cutting tool being important. These impact on assets, rotating mechanisms, landings, materials, motors, auxiliary systems, and other devices. Heat and mechanical power impact machine availability. In sawing, saws fatigue generates cracks and microcracks in the bottom of the blade throat. Vibrations impact on clamping and feeding systems acting on the products dimensional accuracy [31].
In other manufacturing operations such as drying, painting, gluing, or pressing, heat and mass transfer phenomena, adhesion and stress-deformation intervene. For example, in veneer continuous drying, air velocity, steam temperature and feed rate determine the cracks presence and the product moisture content. For years, for different species different transformation technologies have been studied. For sawmill, main factors are presented Figure 4.
Factors in a sawmill monitoring.
To make available data is a key task. Unfortunately, many industrial environmental have not yet all availability. Wood industries are not the exception. To measure power, temperatures, tool wear, pressions, velocities, vibrations and physical and wood anatomical characteristics requires robust sensors. Today, dimensions, moisture, density and many wood and panels defects can be tested online. Indirectly certain critical variables can be quantified. That is especially important when these variables depend on more conventional measurable physical phenomena (e.g., electrical variables, temperatures, vibrations, sound, etc.). In [32], cutting and feed per tooth are correlate with acoustic emission and saw temperature. In mechanical operations, cutting forces explain good machining behavior [33, 34]. In general, tool wear can be related indirectly with heat liberation, power consummation vibrations or acoustic emission.
In the plywood industry, peeling cutting forces with vibrations, acoustic emission and artificial vision can be correlated [33]. Cutting in particle panels can be explained by power consummation [34]. For milling, [35] show that sound and vibrations can be used to predict the online surface quality. For
In the same order, many superficial wood characteristics can be measured by artificial vision techniques [39, 40]. Today, industrial scanners can extract different knout types and singularities, colors, timber edger and in certain applications X-ray determines internal defects [41, 42, 43]. In the panel industry, dimensions, density, and panel moisture are captured online. To classify veneers, scanner test splits, discoloration, and holes. However mechanical properties are yet tested outline.
Several industrial applications are today available. For example, in sawmills, vision and laser are used to capture the logs true shape and the dimensions of boards (by companies like USNR, MPM and Microtec). Many modern sawmills around the word are users of these technologies. X-rays was yet developed for logs, probably Microtec is a company leader over this segment. To detects wood defects, both internal and external, to board in second transformation applications exist. Main suppliers are Weining, GreCon and Microtec with WoodEye©.
In the wood manufacturing industry, normally scanners aide to control specifical and local operation like parts classification, first cutting in sawmill or thickness mat on particle or fiber panels. Yet, data is non stocked for analysis or to create prediction models. World class wood producers are beginning to use and collect real-time data to extract information and add value (interviews and experiences of the authors).
Artificial Intelligence (AI) or more specific Machine Learning (ML) is the core of industry 4.0 [44]. Artificial Intelligent has been defined by E. Rich like “the study of how to make computers do things at which, at the moment, people are better” [45]. If it is believed that intelligence is only a human property. Another Langton’s definition of intelligence involves all living system [46]. A prominent AI area is Machine Learning (ML) consisting in the capacity to learn to solve problems. ML is the study of computer algorithms that improve automatically through experience [47]. And experience are historical data. Last years, Deep Learning (DL) is a new approach and area of ML. In DL, news algorithms using multi-layer artificial neural network work [48]. ML and DL permit today successful applications and an increase considerable research in many fields.
Complex structure of bigdata can be discovery using DL technics like Convolutional Neural Network (CNN) [49, 50]. Support Vector Machine, Random Forest or Bayes technics work on an important set of problems. However, CNN are advantageous to extract features of industrial bigdata [48]. Computing capacity and bigdata turn possible DL technics to industrial systems applications [51, 52, 53].
To wood industry several authors have showed advances using ML. In [54], plywood defects are classified by Support Vector Machine (SVM). In [55], wood quality is automatically classified. In [35], Neural Networks like cutting prediction is used. Recurrent Neural Networks (RNN) is special type of Neural Network. Pass knowledge can be used to learning and predict. In [56], by RNN productivity prediction of a high production sawmill is modeled.
In ML, practice and testing are keys. Data engineering methodologies are important to validate complex problems having many variables and non-lineal relations [52]. Choice of model’s hyperparameters, learning and evaluation data size can become decisive to achieve good performances. Learning data sizes can be different according to the problem. Always, more data is better. Fortunately, in industrial environments data can be “bigdata”. To validate models, data size can go from some miles to millions.
Studies in sawmill industrial process show that RNN to predict productivity with 30,000 records 0.8 of coefficient of determination can be obtained [56]. To classify veneer quality in a plywood continuous industrial drying using Neural Network and Random Forest 6,000 records allowed accuracies over O.8 [57]. In this case, online data collection was implemented to stock veneers, operation, and technology variables. Raw data was pre-process and filtered and a data set warehouse was generated. Material factors considered dimensions, moisture, and forest origin. Operations taken account feed and batch sizes. Drying technology variables were different important factors like steam temperature, pressions and opening. Response was veneer quality (Figure 5).
Data collection in a drying veneer process.
In a melamine particle panels industry too much money can be lost if the final product classification is not good. Using computer vision, multispectral sensors, cloud computing and ML algorithms it is possible to classify panels with 0.95 of accuracy (Figure 6). Multi sensor and data integration permit better performances. More of 14,000 records were used to learning and testing.
Multispectral data process to classify industrial melamine panels [
Benefices and components of Industry 4.0 was presented. Focus is on data engineering. Data analysis, Machine Learning and Deep Learning are in the core of Industry 4.0. Availability of sensors, better computing processor and wireless communication turn possible this new revolution and great opportunities for manufacturing industries. IoT is beginning. 5G technology will allow high speeds of communication and industrial internet feasible. Computing cloud represents opportunities for Small and Medium Enterprises too. Lower cost can be obtained when data processing and stockage is done in the cloud. CFS are still in growth and ML models to autonomous computing are showing auspicious and robust. Virtual factory (digital twin) is subject of a series of investigations. Prototypes of virtual reality and simulation models using real-time data is a reality.
In the wood manufacturing industry, last year, research contributions toward 4.0 techniques have been focuses in developing no-destructive sensors and models. Most of the investigations have been driven into the labs. But industry 4.0 woks with data, bigdata. Learning and testing ML models requires a lot of experiences. Industry to increase productivity and product quality need robust algorithms working within hazard environment to carry out intelligent actions. Either actions to aide decision making or automatic control. ML or DP models must be performants. The authors argue that it is necessary to approach academy and producers. Experience is fundamental to understanding data. The fourth revolution is the data revolution. In this context, researcher and practitioners should be overcome three factors: know the wood, understand the process, and use data engineering methodologies.
Authors thank to: Research Office of University of Bio-Bio, Chile: Research Project 2060360 IF/R, Machine Learning and Vision Computer Group and Intelligent Industry and Complex System Research Group; CORFO-Chile Agency; FONDEF-IDEA of R&D National Agency of Chile (ANID), Project ID14I20364; Project CYTED TICs4CI: Applications TICS for Intelligent Cities; Arauco Company; MASISA Company; Biobio Manufacturers Association (AGMET); Wood Engineering Department at the University of Bio-Bio.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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The toxic and allergic reactions of synthetic dyes are compelling the people to think about natural dyes. Natural dyes are renewable source of colouring materials. Besides textiles it has application in colouration of foods, medicine and in handicraft items. Though natural dyes are ecofriendly, protective to skin and pleasing colour to eyes, they are having very poor bonding with textile fibre materials, which necessitate mordanting with metallic mordants, some of which are not eco friendly, for fixation of natural dyes on textile fibres. So the supremacy of natural dyes is somewhat subdued. This necessitates newer research on application of natural dyes on different natural fibres for completely eco friendly textiles. The fundamentals of natural dyes chemistry and some of the important research work are therefore discussed in this review article.",book:{id:"9203",slug:"chemistry-and-technology-of-natural-and-synthetic-dyes-and-pigments",title:"Chemistry and Technology of Natural and Synthetic Dyes and Pigments",fullTitle:"Chemistry and Technology of Natural and Synthetic Dyes and Pigments"},signatures:"Virendra Kumar Gupta",authors:[{id:"305259",title:"Dr.",name:"Virendra",middleName:null,surname:"Kumar Gupta",slug:"virendra-kumar-gupta",fullName:"Virendra Kumar Gupta"}]},{id:"49647",title:"Fiber Selection for the Production of Nonwovens",slug:"fiber-selection-for-the-production-of-nonwovens",totalDownloads:10568,totalCrossrefCites:9,totalDimensionsCites:17,abstract:"The most significant feature of nonwoven fabric is made directly from fibers in a continuous production line. While manufacturing nonwovens, some conventional textile operations, such as carding, drawing, roving, spinning, weaving or knitting, are partially or completely eliminated. For this reason the choice of fiber is very important for nonwoven manufacturers. The commonly used fibers include natural fibers (cotton, jute, flax, wool), synthetic fibers (polyester (PES), polypropylene (PP), polyamide, rayon), special fibers (glass, carbon, nanofiber, bi-component, superabsorbent fibers). Raw materials have not only delivered significant product improvements but also benefited people using these products by providing hygiene and comfort.",book:{id:"5062",slug:"non-woven-fabrics",title:"Non-woven Fabrics",fullTitle:"Non-woven Fabrics"},signatures:"Nazan Avcioglu Kalebek and Osman Babaarslan",authors:[{id:"119775",title:"Prof.",name:"Osman",middleName:null,surname:"Babaarslan",slug:"osman-babaarslan",fullName:"Osman Babaarslan"},{id:"175829",title:"Dr.",name:"Nazan",middleName:null,surname:"Kalebek",slug:"nazan-kalebek",fullName:"Nazan Kalebek"}]},{id:"41409",title:"Surface Modification Methods for Improving the Dyeability of Textile Fabrics",slug:"surface-modification-methods-for-improving-the-dyeability-of-textile-fabrics",totalDownloads:7063,totalCrossrefCites:13,totalDimensionsCites:36,abstract:null,book:{id:"3137",slug:"eco-friendly-textile-dyeing-and-finishing",title:"Eco-Friendly Textile Dyeing and Finishing",fullTitle:"Eco-Friendly Textile Dyeing and Finishing"},signatures:"Sheila Shahidi, Jakub Wiener and Mahmood Ghoranneviss",authors:[{id:"58854",title:"Dr.",name:null,middleName:null,surname:"Shahidi",slug:"shahidi",fullName:"Shahidi"}]}],onlineFirstChaptersFilter:{topicId:"296",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:42,paginationItems:[{id:"82914",title:"Glance on the Critical Role of IL-23 Receptor Gene Variations in Inflammation-Induced Carcinogenesis",doi:"10.5772/intechopen.105049",signatures:"Mohammed El-Gedamy",slug:"glance-on-the-critical-role-of-il-23-receptor-gene-variations-in-inflammation-induced-carcinogenesis",totalDownloads:11,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",subseries:{id:"18",title:"Proteomics"}}},{id:"82875",title:"Lipidomics as a Tool in the Diagnosis and Clinical Therapy",doi:"10.5772/intechopen.105857",signatures:"María Elizbeth Alvarez Sánchez, Erick Nolasco Ontiveros, Rodrigo Arreola, Adriana Montserrat Espinosa González, Ana María García Bores, Roberto Eduardo López Urrutia, Ignacio Peñalosa Castro, María del Socorro Sánchez Correa and Edgar Antonio Estrella Parra",slug:"lipidomics-as-a-tool-in-the-diagnosis-and-clinical-therapy",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82440",title:"Lipid Metabolism and Associated Molecular Signaling Events in Autoimmune Disease",doi:"10.5772/intechopen.105746",signatures:"Mohan Vanditha, Sonu Das and Mathew John",slug:"lipid-metabolism-and-associated-molecular-signaling-events-in-autoimmune-disease",totalDownloads:17,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82483",title:"Oxidative Stress in Cardiovascular Diseases",doi:"10.5772/intechopen.105891",signatures:"Laura Mourino-Alvarez, Tamara Sastre-Oliva, Nerea Corbacho-Alonso and Maria G. Barderas",slug:"oxidative-stress-in-cardiovascular-diseases",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Importance of Oxidative Stress and Antioxidant System in Health and Disease",coverURL:"https://cdn.intechopen.com/books/images_new/11671.jpg",subseries:{id:"15",title:"Chemical Biology"}}}]},overviewPagePublishedBooks:{paginationCount:33,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:{name:"Kobe College",institutionURL:null,country:{name:"Japan"}}}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. 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