E numbers of food additives.
\r\n\t
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Abdurakhmonov",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11724.jpg",keywords:"Full Genome Sequencing, Mutations and Genetic Variations, Whole-Virus Inactivated and Live-Attenuated, Recombinant Protein-Based, WHO-Protocols, Preclinical and Clinical Trials, New Strain Protection, Availability and Usage, Side Effects, Novel Designs, Country Experiences, Booster-Dose Injection",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 17th 2022",dateEndSecondStepPublish:"May 24th 2022",dateEndThirdStepPublish:"July 23rd 2022",dateEndFourthStepPublish:"October 11th 2022",dateEndFifthStepPublish:"December 10th 2022",remainingDaysToSecondStep:"5 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"A leading biotechnologist, coordinating biotechnology science including COVID-19 vaccine development, laboratory studies, and clinical trials and production in Uzbekistan. Prof. Abdurakhmonov founded (2012) the Center of Genomics and Bioinformatics of Uzbekistan. He received a BS in Biotechnology from the National University, an MS in Plant Breeding from Texas A&M University, USA, a Ph.D. in Molecular Genetics, a Doctor of Science in Genetics, and a full professorship in Molecular Genetics.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"213344",title:"Prof.",name:"Ibrokhim Y.",middleName:null,surname:"Abdurakhmonov",slug:"ibrokhim-y.-abdurakhmonov",fullName:"Ibrokhim Y. Abdurakhmonov",profilePictureURL:"https://mts.intechopen.com/storage/users/213344/images/system/213344.jpg",biography:"Ibrokhim Y. Abdurakhmonov received his B.S. (1997) in biotechnology from the National University, M.S. in plant breeding\n(2001) from Texas A&M University of USA, Ph.D. (2002) in molecular genetics, Doctor of Science (2009) in genetics, and full professorship (2011) in molecular genetics and molecular biotechnology from Academy of Sciences of Uzbekistan. He founded (2012)\nthe Center of Genomics and Bioinformatics of Uzbekistan. He\nreceived the 2010 TWAS prize, and “ICAC Cotton Researcher of the Year 2013” for\nhis outstanding contribution to cotton genomics and biotechnology. He was elected\nas The World Academy of Sciences (TWAS) Fellow (2014) and as a member (2017)\nof the Academy of Sciences of Uzbekistan. 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Very simply, it is a substance which is added to food to enhance its flavor, appearance, or other favorable quality. In fact, the food protection committee of the US national research council defined food additives as “A substance or a mixture of substances other than a basic food stuff that is present in a food as a result of an aspect of production, processing, storage, or packaging” [1]. According to US FDA (Food and Drug Administration), a food additive is “any substance, the intended use of which results or may reasonably be expected to result–directly or indirectly–in its becoming a component or otherwise affecting the characteristics of any food” [2]. Although the term ‘food additives’ has been used frequently at present, its utilization has been practiced since ancient times; and probably dating back to much earlier than the hunter-gatherer era. Even though food additives confer much benefit to all sectors, such as the manufacturers, retailers, and customers, utilization of food additives must be carried out extremely cautiously.
\nAdditives, for the most part, are synthetic chemicals. Present day consumers are turning to natural ingredients and bio-based additives due to adverse effects caused by some chemicals. Therefore, plant-derived substances are gaining a foot hold as preservatives, colorants, flavors, and even as antibacterial agents [3, 4].
\nAlmost all safe-to-use food additives are given ‘E numbers’ by the European Food Safety Authority. In order to get to this status, the food additive must pass all the safety checks. Following are the general categories of food additives and their E numbers. However, when one food additive has more than one function, it is given only one E number. Chemical compounds and other species are constantly added to the list of safe-to-use food additives as the food additives pass the safety checks. An up to date list of food additives and their E numbers could be obtained from official UK food standards agency web site https://www.food.gov.uk/science/additives/enumberlist#toc-1. The general list of E numbers of food additives is given in Table 1 [5].
\nBlock of numbers | \nFood additives | \n
---|---|
E100-E199 | \nColors | \n
E200-E299 | \nPreservatives | \n
E300-E399 | \nAntioxidants and acidity regulators | \n
E400-E499 | \nThickeners, stabilizers and emulsifiers | \n
E500-E599 | \nAnticaking agents | \n
E600-E699 | \nFlavor enhancers | \n
E700-E799 | \nAntibiotics | \n
E900-E999 | \nGlazing agents and sweeteners | \n
E1000-E1599 | \nAdditional chemicals | \n
E numbers of food additives.
Chemical | \nOdor | \n
---|---|
Diacetyl, acetylpropionyl, acetoin | \nButtery | \n
Isoamyl acetate | \nBanana | \n
Benzaldehyde | \nBitter almond, cherry | \n
Cinnamaldehyde | \nCinnamon | \n
Ethyl propionate | \nFruity | \n
Methyl anthranilate | \nGrape | \n
Limonene | \nOrange | \n
Ethyl decadienoate | \nPear | \n
Allyl hexanoate | \nPineapple | \n
Ethyl maltol | \nSugar, cotton candy | \n
Ethylvanillin | \nVanilla | \n
Methyl salicylate | \nWintergreen | \n
Acid | \nDescription | \n
---|---|
Glutamic acid salts | \nThis amino acid’s sodium salt, monosodium glutamate (MSG), is one of the most commonly used flavor enhancers in food processing. Mono- and diglutamate salts are also commonly used. | \n
Glycine salts | \nSimple amino acid salts typically combined with glutamic acid as flavor enhancers. | \n
Guanylic acid salts | \nNucleotide salts typically combined with glutamic acid as flavor enhancers. | \n
Inosinic acid salts | \nNucleotide salts created from the breakdown of AMP, due to high costs of production, typically combined with glutamic acid as flavor enhancers. | \n
5’-Ribonucleotide salts | \nNucleotide salts typically combined with other amino acids and nucleotide salts as flavor enhancers. | \n
According to the US FDA, “A color additive is any dye, pigment, or substance, which when added or applied to a food, drug or cosmetic, or to the human body, is capable (alone or through reactions with other substances) of imparting color” [2]. Food colors are used as food additives mainly to yield better sensory effects, specifically appearance contentment. The reasons for adding colors to food are manifold. First, color may be lost due to the processing and storage conditions of food, and thus food colors are added to compensate such loss of color. Second, food items with natural colors may show a variation of color, and thus food colors are added to correct such variations in color. Third, food colors may be added to further improve the natural color of the food. Fourth, food colors are added to give color to food items with no color [2].
\nThere are two types of food colors, certified colors and colors exempt from certification. The certified colors are synthetic compounds. They are usually more effective than natural compounds and they do not introduce off-flavors to the foods. Colors derived from natural sources are exempt from certification. These compounds are more expensive than synthetic compounds. Yet, the colors exempt from certification may give off-flavors to the foods [2].
\nHealth effects of food colorants are a major concern among the consumers and regulatory bodies; and thus, carrying out toxicity studies determining health effects are considered very significant today. A recent study revealed that Allura Red AC lacks genotoxicity after the European Food Safety Authority showed its concern on this matter [6]. In addition to toxicity studies, remedies for the adverse effects of food colorants are being evaluated. For example, Rafati et al. demonstrated that the negative effects caused by tartrazine in mice could be mitigated by the simultaneous administration of vitamin E [7].
\nAlthough food colors are added to enhance organoleptic appeal of the foods, naturally occurring food colors such as curcumin and riboflavin possess other beneficial health effects. In fact, curcumin exhibits numerous bioactivities such as antioxidant, antimicrobial, and anticancer [8, 9]. Riboflavin, also, acts as an antioxidant, and it is linked to several health benefits [10]. Numerous strategies have been explored to increase the stability of natural colorants due to beneficial health effects or general lack of toxicity of these compounds [11, 12]. As expected, novel sources of natural colorants are being explored due to the positive attributes of natural colorants [13]. In addition, encapsulation techniques and other innovative methods are being explored in order to improve numerous properties of food colorants as opposed to directly add food colorants in food [14].
\nThe list of colors usually used in food manufacturing is stated below [5].
\nList of colors: Curcumin, Riboflavin, Riboflavin-5’-phosphate, Tartrazine, Quinoline yellow, Sunset Yellow FCF, Orange Yellow S, Cochineal, Carminic acid, Carmines, Azorubine, Carmoisine, Amaranth, Ponceau 4R, Cochineal Red A, Erythrosine, Allura Red AC, Patent Blue V, lndigotine, Indigo Carmine, Brilliant Blue FCF, Chlorophylls and chlorophyllins, Copper complexes of chlorophyll and chlorophyllins, Green S, Plain caramel, Caustic sulphite caramel, Ammonia caramel, Sulphite ammonia caramel, Brilliant Black BN, Black PN, Vegetable carbon, Brown FK, Brown HT, Carotenes, Annatto, Bixin, Norbixin, Paprika extract, Capsanthin, Capsorubin, Lycopene, Beta-apo-8’-carotenal (C30), Ethyl ester of beta-apo-8’-carotenoic acid (C30), Lutein, Canthaxanthin, Beetroot Red, Betanin, Anthocyanins, Litholrubine BK.
\nFood preservatives have become an indispensible part of the food industry today. In simple terms, a food preservative is any substance that hinders food deterioration caused by microbes, enzymes, or any other chemical reaction. Millions of people suffer from hunger as a result of lack of enough food [15] and thus, the advantages of using food preservatives in food processing are plenteous. Food preservatives along with other food additives are under strict control by numerous governing bodies. A short account of the governing system is given under Section 11.
\nMost artificial food preservatives impart negative health effects at high doses. For instance,
Natural preservatives are an appealing alternative to artificial preservatives, especially with respect to health effects. A novel trend is to explore and utilize essential oils such as clove essential oil and eugenol extracted from cloves, limonene extracted from citrus fruits, and essential oil extracted from cinnamon as food preservatives of numerous food items including fresh cut produce, juices, and fish [3, 18–20]. As expected, encapsulated natural food preservatives including thyme essential oil and curcumin have shown favorable properties such as sustained release and enhanced antioxidant and antimicrobial properties [21, 22]. In addition to natural products, fermented milk products have shown promise as food preservatives [23]. The reasons for utilizing natural products and nonsynthetic products as food preservatives include imparting health benefits to the consumers and gaining “clean label” advantage.
\nNumerous approaches are being taken to find novel food preservatives with ameliorated properties. For instance, peptides have been used successfully as potential food preservatives [24]. Once a peptide food preservative is identified, mass production may be carried out using biotechnology. Combinations of food preservatives have also been studied to discover the combined effect and the possibility of substituting synthetic food preservatives by such combinations. For example,
Some food preservatives used in food manufacturing are listed below [5].
\nList of preservatives: Sorbic acid, Potassium sorbate, Calcium sorbate, Benzoic acid, Sodium benzoate, Potassium benzoate, Calcium benzoate, Ethyl p-hydroxybenzoate, Sodium ethyl p-hydroxybenzoate, Sodium methyl p-hydroxybenzoate, Sulfur dioxide, Sodium sulphite, Sodium hydrogen sulphite, Sodium metabisulphite, Potassium metabisulphite, Calcium sulphite, Calcium hydrogen sulphite, Potassium hydrogen sulphite, Biphenyl; diphenyl, Nisin, Natamycin, Hexamethylene tetramine, Dimethyl dicarbonate, Potassium nitrite, Sodium nitrite, Sodium nitrate, Potassium nitrate, Propionic acid, Sodium propionate, Calcium propionate, Potassium propionate, Boric acid, Sodium tetraborate; borax.
\nAntioxidants play a pivotal role in the food industry, combating oxidative stress on oxygen-sensitive species. The antioxidants used in the food industry are either hydrophilic, lipophilic, or amphiphilic, protecting various types of ingredients. Certain antioxidants function also as acidity regulators. Examples include ascorbic acid and citric acid. Acidity regulators are also an essential group of food additives as lowering the pH of the food usually assists to retard microbial attack.
\nAlthough antioxidants are deemed to confer numerous health benefits to the humans, synthetic antioxidants such as butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) have shown negative health effects [26]. On the contrary, some reports have shown chemoprevention properties of those synthetic carcinogenic antioxidants [27]. Again, the issue of toxicity is dealt with by adhering strictly into the ADI published by the governing bodies worldwide including the US FDA. Although the results of synthetic antioxidants are inconsistent, numerous natural antioxidants have the ability to function as nontoxic anticarcinogenic compounds. Examples include ferulic acid, caffeic acid, curcumin, vitamin E, polyphenolic catechins, and carnosol [28].
\nAs with other food additives, the trend is to utilize and seek for natural food antioxidants. Both pure antioxidants and plant extracts are used and explored these days. Moreover, encapsulation of pure antioxidants and plant extracts showing antioxidant properties is carried out to obtain improved attributes such as improved stability and sustained release of those bioactive compounds [29, 30]. The liposomal encapsulation of the
A list of antioxidants used in food manufacturing is stated below [5].
\nList of antioxidants: Ascorbic acid, Sodium ascorbate, Calcium ascorbate, Fatty acid esters of ascorbic acid, Tocopherols, Alpha-tocopherol, Gamma-tocopherol, Delta-tocopherol, Propyl gallate, Octyl gallate, Dodecyl gallate, Erythorbic acid, Sodium erythorbate, Tertiary-butyl hydroquinone (TBHQ), Butylated hydroxyanisole (BHA), Butylated hydroxytoluene (BHT), Extracts of rosemary, 4-Hexylresorcinol.
\nAcidity regulators such as citric acid, tartaric acid, and phosphoric acid are numbered together with antioxidants in the E numbering system [5, 31]. This approach is very logical as certain acidity regulators, such as citric acid, exhibit antioxidant properties. In fact, citric acid has imparted favorable effects on food, functioning as an acidity regulator and antioxidant simultaneously [32]. What’s more, food acidity regulators have shown advantageous combined effects with other food additives on food. Antibrowning effect of citric acid together with ascorbic acid and nitrogen on banana smoothies is an example [33].
\nThe list of acidity regulators commonly used in food manufacturing is stated below [5].
\nList of acidity regulators: Sodium lactate, Potassium lactate, Calcium lactate, Citric acid, Sodium citrates, Potassium citrates, Calcium citrates, Tartaric acid (L-(+)), Sodium tartrates, Potassium tartrates, Sodium potassium tartrate, Phosphoric acid, Sodium phosphates, Potassium phosphates, Calcium phosphates, Magnesium phosphates, Sodium malates, Potassium malate, Calcium malates, Metatartaric acid, Calcium tartrate, Adipic acid, Sodium adipate, Potassium adipate, Succinic acid, Triammonium citrate, Calcium disodium ethylene diamine tetra-acetate; calcium disodium EDTA.
\nThickeners, stabilizers, emulsifiers, and gelling agents have become an integral part in the current food manufacturing industry. Thickeners increase the volume, change the viscosity, and increase the processability of the food items. Stabilizers, as the name implies, stabilize the food products; sometimes through the utilization of fillers. Emulsifiers assist in the miscibility of otherwise immiscible substances possible. For instance water-in-oil or oil-in-water emulsions used in the food industry are made utilizing emulsifiers. Gelling agents mainly contribute to the viscosity and sensory properties of the food products. In sum, all thickeners, stabilizers, emulsifiers and gelling agents contribute to the stability and palatability of the food product.
\nThis category of food additives also consist of natural and synthetic compounds. In fact, lecithin that assists in emulsification and stabilization for most food products is mostly extracted from soy bean, and thus it is a natural additive [34]. However, numerous studies are being conducted evaluating the positive effects of synthetic lecithin [35]. Alginate functioning as both a thickener and gelling agent is another natural food additive in this group [36]. Apart from the natural compounds, synthetic emulsifiers such as polysorbates constitute an important component of this group. Although considered food grade, several health concerns have arisen regarding such artificial emulsifiers [37].
\nA list of thickeners-stabilizers-emulsifiers-gelling agents used in food manufacturing is listed in Tables 2 and 3 [5].
\nList of thickeners-stabilizers-emulsifiers-gelling agents: Lecithins, Alginic acid, Sodium alginate, Potassium alginate, Ammonium alginate, Calcium alginate, Propane-1-2-diol alginate, Agar, Carrageenan, Processed eucheuma seaweed, Locust bean gum; carob gum, Guar gum, Tragacanth, Acacia gum; gum Arabic, Xanthan gum, Karaya gum, Tara gum, Gellan gum, Konjac, Soybean hemicellulose, Cassia gum, Polyoxyethylene sorbitan monolaurate; Polysorbate 20, Polyoxyethylene sorbitan mono-oleate; Polysorbate 80, Polyoxyethylene sorbitan monopalmitate; Polysorbate 40, Polyoxyethylene sorbitan monostearate; Polysorbate 60, Polyoxyethylene sorbitan tristearate; Polysorbate 65, Pectins, Ammonium phosphatides, Sucrose acetate isobutyrate, Glycerol esters of wood rosins, Cellulose, Methyl cellulose, Ethyl cellulose, Hydroxypropyl cellulose, Hydroxypropyl methyl cellulose, Ethyl methyl cellulose, Carboxy methyl cellulose, Crosslinked sodium carboxy methyl cellulose.
\nAs the name implies, the role of anticaking agents is to prevent lumping or caking in food. These agents are added mostly for powders or granulated material. Among the numerous advantages of using anticaking agents include: sustenance of sensory attributes, easiness of packaging, efficient transportation, and simplicity to yield high quality products for consumption. Depending on the food product involved, either water-soluble or organic solvent-soluble anticaking agents are used.
\nAnticaking agents frequently used in food manufacturing are stated below [5].
\nList of anticaking agents: Calcium Aluminum Silicate, Calcium Phosphate tribasic, Calcium Sili-cate, Calcium Stearate, Cellulose, Magnesium Carbonate, Magnesium Oxide, Magnesium Silicate, Magnesium Stearate, Microcrystalline Cellulose, Propylene Glycol, Potassium Ferrocyanide, Trihydrate, Silicon Dioxide, Sodium Aluminum Silicate, Sodium Ferrocyanide, decahydrate.
\nFlavors and flavor enhancers are of extreme importance in the food industry as it is what makes the food sensational. Flavor is perceived by the taste and smell via chemical senses. Also, the chemical irritants perceived in the mouth and throat, temperature and texture are factors affecting the flavor of a food. Nowadays, both natural and artificial substances are used as food flavors Table 2. The basic universally recognized flavors include: sweet, sour, tangy, bitter, umami, hot, that can be perceived through the tongue. On the other hand, the number of sensations that can be perceived through the nose (smell) is limitless. As a result, the food industry is ever growing utilizing different combinations of taste and smell. What’s more, there is another group of chemical substances that do not impart any flavor in to the food product but enhance the existing flavor in the food Table 3. These flavor enhances are highly valued in the food industry as these substances contribute significantly into cost reduction in food manufacturing. Flavors and flavor enhancers frequently used in food manufacturing are stated [38].
\nFlavors and flavor enhances also are evaluated for their health effects by numerous scientists worldwide. Further, extraction of numerous novel natural flavors is being carried out around the globe as a result of the higher inclination of the customers to such natural compounds [39]. There has been much criticism on the health effects of glutamate—a much consumed flavor enhancer. However, mixed results have been published and there is no evidence to prove that glutamate possesses negative health effects, according to a recent report [40]. Like almost all other food additives, encapsulation, for instance microencapsulation and emulsification, is used as means of enhancing the properties of food flavors [41, 42].
\nAntibiotics are being used in the food industry today to increase the shelf life of numerous food items, especially perishable food items including milk [43]. Although not directly added during food processing, nonvegetarian food may contain a certain amount of antibiotics since antibiotics are frequently used in animal production. However, any antibiotic used for human therapeutic purposes or for animal feed additive are banned for use in the food industry. Tetracycline is a classic example. Maximum permissible amounts of such antibiotic residues have been declared and much emphasis is given to regular monitoring of antibiotic residues in food [44]. Moreover, the antibiotics used in the food industry show slower activity than those used for therapeutic purposes [45]. Antibiotics frequently used in food manufacturing are stated below.
\nList of antibiotics: Nisin, Natamycin, Subtilin, Tylosin Phytoncides.
\nPhytoncides are antibiotics obtained from plants. Examples include: mustard oil, thyme, cinnamaldehyde, eugenol, etc. [46].
\nAntibiotics permitted as food additives are being experimented heavily, especially to engineer more potent variants [47]. Further, encapsulation has become a common technique to enhance the desirable properties of antibiotics. For instance, coated liposomes encapsulating nisin has shown improved sustained release properties beneficial for applications in the food sector [48].
\nGlazing agents may be either natural or synthetic. They are used mainly for preservation of food items by forming a thin coat around it [49]. A list of glazing agents frequently used in food industry is stated below [5].
\nList of glazing agents: Stearic acid, Beeswax, Candelilla wax, Carnauba wax, Shellac, Microcrystalline wax, Crystalline wax, Lanolin, Oxidized polyethylene wax, Esters of colophonium, Paraffin.
\nThe most commonly used sweetener used in the food industry is sucrose as it is readily available. Thus, the performance of other sweeteners is frequently measured against that of sucrose [50]. Glucose is also frequently used in the food industry, especially in the manufacturing of confectionaries [51]. However, substitutes for common sugars, natural or artificial, are in high demand due to the prevalence of diabetes mellitus among a significant proportion of people worldwide. Other requirements for sugar substitutes include weight loss, dental care, and reactive hypoglycemia. In addition, using sugar substitutes is cost effective since the sugar substitutes are many times (sometimes more than 100 or even 1000 times) sweeter than sucrose [52]. A list of sweeteners frequently used in food manufacturing is stated below [5, 53].
\nList of sweeteners: Sorbitol, Sorbitol syrup, Mannitol, Acesulfame K, Aspartame, Cyclamic acid and its Na and Ca salts, lsomalt, Saccharin and its Na - K and Ca salts, Sucralose, Thaumatin, Neohesperidine DC, Steviol glycoside, Neotame (as a flavor enhancer), Salt of aspartame-acesulfame, Maltitol, Maltitol syrup, Lactitol, Xylitol, Erythritol.
\nThe European Food Safety Authority has grouped some food additives as ‘additional chemicals’ as those chemicals cannot be grouped together with other food additives. As indicated in Table 1, these chemicals are numbered from E1000 to E1599. Even though these chemicals may function as other food additives, they have different properties and thus treated differently. For instance, invertase having the number E1103 functions as emulsifiers-stabilizers-thickeners-gelling agents but is in a special category.
\nA list of other chemicals frequently used in food manufacturing is stated below [5].
\nList of other chemicals: Polydextrose, Polyvinylpyrrolidone, Polyvinylpolypyrrolidone, Polyvinyl alcohol, Pullulan, Basic methacrylate copolymer, Oxidized starch, Monostarch phosphate, Distarch phosphate, Phosphated distarch phosphate, Acetylated distarch phosphate, Acetylated starch, Acetylated distarch adipate, Hydroxyl propyl starch, Hydroxy propyl distarch phosphate, Starch sodium octenyl succinate, Acetylated oxidized starch, Starch aluminum Octenyl succinate, Triethyl citrate, Glyceryl triacetate; triacetin, Propan-1-2-diol; propylene glycol, Polyethylene glycol.
\nFood additives are under strict control of numerous governing bodies. In the European Union, the governing bodies are the European Food Safety Authority (EFSA) and the European Commission, Parliament and Council. These bodies are accountable for the safety assessment, which includes toxicological studies and dietary exposure assessment, authorization which includes maintaining and publishing data bases of food additives permitted to be used in the EU, and control which is involved in legislation and labeling of food additives. The U.S. Food and drug administration (US FDA) is the main governing body of food additives in USA, and almost all other countries have their own governing bodies of food safety. Food and Agriculture Organisation (FAO) and the World Health Organisation (WHO) work together in the international arena via a Joint Expert Committee on Food Additives (JECFA) [54–57].
\nJoint Expert Committee on Food Additives from 1961 has taken initiative of matters regarding the acceptable daily intake (ADI) level. “ADI is a measure of the amount of a specific substance (originally applied for a food additive, later also for a residue of a veterinary drug or pesticide) in food or drinking water that can be ingested (orally) on a daily basis over a lifetime without an appreciable health risk”. “ADIs are expressed usually in milligrams (of the substance) per kilogram of body weight per day.” [58]
\nAll of these food additives are used to fine tune the food items to yield a superb food product having sensational attributes. In addition, the preservative effect that the food additives impart is of utmost importance. Further, food safety governing bodies worldwide have set maximum levels to be used in the food industry for all approved food additives. Thus, health risk is at a low level. However, it is advisable to change ones diet time to time so that the subject is not exposed to the same food additives for lengthy periods of time. This practice also may not be essential if the customer pays attention to the recommended daily intake of the ingredients.
\n“Innovation is change that unlocks new value” according to Jamie Notter [59]. Adding food additives to enhance the attributes of food is an ancient concept of value addition of food practiced from as early as the hunter-gatherer era. In the modern era, the demand is greater than the supply, and innovation is the change that satisfies the demand by all the sectors, such as the manufacturers, retailers, and customers. While using food additives to enhance the attributes of food, it is of prime importance that guidelines by the relevant food safety authorities are followed since synthetic and natural compounds with various health effects are widely used as additives. Deviations from food safety regulations may result in serious negative outcomes. As a result, any party breaching rules and guidelines regarding food additives will have to face serious consequences, including harsh court decisions against them. In sum, considering the health effects and regulations regarding food additives are extremely significant although utilizing food additives have enabled the beings to enjoy a plethora of various food products.
\nA deterministic system is a system whose governing physical laws are specified so that if the state of the system at some time is known, then one can precisely predict the state at a later time. Nondeterministic systems are divided into two categories: stochastic and random. A stochastic system has governing physical laws that even if the state at some point in time is known precisely, it is impossible to determine the state of the system at a later time precisely. It is possible to determine the probability of a state, rather than the state itself. A random system is one which has no apparent governing physical laws. Practically, we treat all unpredictable systems, stochastic or random as stochastic systems, since we employ the same methods to study them. While we are unable to predict the state of a random process, we can evolve a strategy to deal with such processes. Such a strategy is based on a branch of mathematics dealing with unpredictable systems, called statistics.
Estimation is the process of extracting information from data which can be used to predict the behavior of state variables in a system. The estimation uses statistical criteria to infer the actual value of unknown variables. Estimation models are used to process noisy measurements, filter them, and detect inaccuracies. When random signals are passed through a deterministic system, their statistical properties are modified. A deterministic system to which random signals are input, so that the output is a random signal with desired statistical properties is called a filter. Filters can be linear or nonlinear, time-invariant or time varying. However, for simplicity we will usually consider linear, time-invariant filters. Linear, time-invariant filters are commonly employed in control systems to reduce the effect of measurement noise on the control system. In such systems, the output is usually a superposition of a deterministic signal and a random measurement noise.
The output of a filter not only has a frequency content different from the input signal, but also certain other characteristics of the filter, such as a phase-shift or a change in magnitude. In other words, the signal passing through a filter is also distorted by the filter, which is undesirable. A filter would produce an output signal based upon its characteristics, described by the transfer-function, frequency or impulse response, or a state-space representation of the filter. However, a filter can be designed to achieve a desired set of performance objectives, i.e. the numerator and denominator polynomials of the filter’s transfer function, or coefficient matrices of the filter’s state-space model, can be selected by a design process to achieve the conflicting requirements of maximum noise attenuation and minimum signal distortion.
There are several prediction models to infer the system state, although, it can be shown that of all estimation tools Kalman Filter (KF) is the one that minimizes the variance of the estimation error which enables accurate estimation of the process.
The first application of state estimation was in the aerospace field to solve problems related to the prediction of position in aerospace vehicles. Nowadays, estimation has been applied in several fields of engineering and control systems. One common application is in data acquisition, to solve the problem of predicting the state of a system that cannot be measured directly due to the characteristics and complexity of the environment.
KF is an estimator proposed by Rudolph E. Kalman in 1960. It is an algorithm to estimate the evolution of a dynamic system, especially when data has a lot of noise. The principle of the filter is to find the probability of the hypothesis of predicted state and using the data from the measurement to correct it and improve the future estimation at each time. It is a suitable algorithm to apply in dynamic systems, linking real-time measurements and predicting the state of system parameters through time approaches. KF has been implemented in several fields, such as in navigation systems [1, 2, 3, 4], financial models [5, 6, 7], tracking vehicles [8, 9] and image processing [10, 11, 12]; only to mention some of them. Nevertheless, this statistical tool is useful for two main purposes: estimation and performance analysis of estimators.
In the field of IC technology, it has been implemented for thermal estimation. Multicore processors use a dynamic thermal management mechanism that use embedded thermal sensors for monitoring the real-time thermal behavior of the processor, this kind of sensors are susceptible to a variety of source of noise and this causes the discrepancies between actual temperatures observed by on-chip thermal sensors. Therefore, to fix the discrepancies in sensing, Kalman’s prediction is used to estimate real values from noisy sensor readings [13]. Another novel application of KF is in the electric vehicle industry, the estimation of the charge state of lithium-ion battery is an important parameter in order to guarantee a safe operation of them. The battery performance is influenced by aging; this fact makes difficult to predict the battery state, to overcome this issue the application of KF in combination with other methods is a suitable methodology [14, 15, 16, 17].
Recently, KF has been applied in several industrial applications. With the development of manufacturing process, welding automation emerges as one important tool to speed up the production rate in the assembly line in stronger and high-quality welds. Nevertheless, there are several factors that could influence the welding quality and the most important is the arc length, which could be influenced by the irregular surface of the workpiece and the loss of the tungsten electrode. To enhance the quality during the Gas-Tungsten Arc Welding (GTAW) process, KF is applied in order to keep the arc length stable and minimize the external noise [18]. In the field of sensorless control, KF have been used in intelligence electrical drives. To control induction motor drives without mechanical speed sensor at the motor shaft allows reduced hardware complexity, and low costs. Additionally, the use of induction motors without position sensor is useful for applications with abrasive and hard surface. Thereby, the application of an estimation method it’s necessary in order to predict the position and velocity of the shaft [19, 20, 21].
In applications related with radio astronomy, KF has been applied for the analysis of Very-Long-Baseline Interferometry (VLBI) data, in order to analyze parameters such as base line lengths, earth orientation parameters, radio source coordinates and tropospheric delays. Nowadays, modern antennas are being constructed and equipped with highly accurate broadband receiving systems. Besides the accurate observations gotten by astronomic instruments, it is necessary to implement estimation methods in order to optimize the models applied in data analysis [22, 23]. In power systems, one of the main difficulties is power quality due to total harmonics distortion (THD) that is mainly caused by nonlinear loads. THD effects are strongly correlated with issues as device heating, break down electronic components, network interference, etc. Several filters have been performed to decrease the effect of harmonics; nevertheless, the application of KF has shown an important reduction in the effect of harmonics [24, 25, 26]. In the field of biomedicine KF is widely used over other estimation methodologies to overcome the different sources of noise. Specifically, KF has been used to smooth and predict signals from Electroencephalogram and Electrocardiogram signals [27, 28]. Recently in the literature there are reports on a new methodology to protect the confidentiality of the transmitted data based on a Kalman filter. This strategy proposes the implementation of encrypted algorithm using KF, and is suggested to be used in Industrial cyber–physical systems (ICPSs) to protected data privacy [29, 30].
As it has been mentioned above, KF has been used in diverse fields of science and technology to predict specific parameters of interest according to the application. Temperature evolution is an important parameter to measure and predict, in order to study or control the temperature in an environment [31, 32], device [13, 33, 34] and process [18]. It is well known that RTDs are commercial devices very useful to monitor the temperature due their stability and accuracy. However, RTDs are self-heating causing noisy readings making the RTD a suitable example to implement KF for temperature estimation. Importantly, we searched in the literature and found no evidence of previous work reporting the use of a KF to filter the noise and predict the temperature behavior from RTD readings.
The final objective of this study is to obtain the specification of a linear dynamic system (Wiener filter [35]) which accomplishes the prediction, separation, or detection of a random signal [36]. With the state-transition method, a single derivation covers a large variety of problems: growing and infinite memory filters, stationary and non-stationary statistics, etc. Having guessed the “state” of the estimation (i.e., filtering or prediction) problem correctly, one is led to a nonlinear difference (or differential) equation for the covariance matrix of the optimal estimation error. From the solution of the equation for the covariance matrix we obtain the coefficients characterizing the optimal linear filter [36]. The following is a simplified derivation described previously in the references [37, 38].
The initial state,
Thus
The correlation matrix,
The correlation matrix is the expected value of the matrix
Consider a plant which we cannot model accurately using only a deterministic model, because of the presence of uncertainties called process noise and measurement noise:
In the linear, time-varying state-space representation above,
Since we cannot predict the state-vector,
Since the state-vector,
If T < t, this is a data-smoothing (interpolation) problem. If T = t, this is called filtering. If T > t, we have a prediction problem. Since the original treatment is general enough, the collective term estimation is used [36].
Hence, the best estimate to obtain for
Taking in consideration the deviation of the estimated state- vector,
The best estimate of state-vector happens if
The state-equation of the Kalman filter is that of a time-varying observer, and can be written as follows:
If we substitute Eq. (4) into Eq. (12) we get:
Substituting Eq. (13) into Eq. (7)
Here
Using Eqs. (6) and (7), we obtain:
Eq. (16) is the error covariance update equation, where
The trace of the error covariance matrix is the sum of the mean squared errors. The mean squared error may be reduced by minimizing the trace of
We rewrite Eq. (16);
Taking the trace of this expression gives:
Then, we differentiate with respect to
Setting to zero and solving for
Substitution of Eq. (20) into [17], gives:
Eq. (21) is the update equation for the error covariance matrix with optimal gain.
State projection is derived using;
To project the error covariance matrix into the next time interval, k + 1 we first find an expression for the error based on the prior error;
Eq. (7) in time k + 1 is;
Assuming that
This completes the description of the filter.
An algorithm loop is required to make the program in MATLAB and in C-code for the microprocessor. The loop is summarized in the Figure 1.
Recursive algorithm for the Kalman filter.
The KF assumes that the system model is linear and known, the system and measurement noises are white, and the states have initial conditions with known means and variances. The power spectral densities used can be treated as tuning parameters to design an observer with excellent performance and robustness. The linear Kalman filter can also be used to design observers for nonlinear plants, by treating nonlinearities as process noise with appropriate power spectral density matrix.
Since the Kalman filter is an optimal observer the appearance of matrix Riccati equation is not surprising. We are interested in a steady Kalman filter, i.e. the Kalman filter for which the covariance matrix converges to a constant in the limit
From the projections into
Using Eq. 30 in Eqs. 31 and 32 we get:
Rewriting Eq. 34 we get:
When in steady state:
Then we arrive at the Riccati equation:
The iterative solution of the Riccati equation is not required in real time. The observer gain is calculated off-line for predictive control applications [40]. Riccati equations are mainly used to control large scale systems, estimation, and, detection processes.
In this work the discrete-time algebraic Riccati equation (DARE) was solved to obtain the covariance matrix P of the Kalman gain. The discrete-time algebraic Riccati equation is represented by the next form [41]:
Where
Eq. (38) can be written in the short form:
Where:
The application of the Kalman filter implies solving the DARE, which can be solved by several solution methods. Computational methods to solve Riccati equations can be categorized into three classes: invariant subspace methods, deflating subspace methods, and Newton’s methods. The generalized Schur method that is classified as a deflating subspace method is used to solve DARE. The generalized Schur algorithm is a strong algebraic tool that allows computing classical decompositions of matrices, such as the QR and LU factorizations [42]. The next algorithm was used to solve DARE [43]:
Input arguments:
Output arguments:
Form the pencil
Transform the pencil
Using an orthogonal transformation and reorder the generalized real Schur form. So that all the pencil
Form the matrix:
Resistive temperature detectors (RTD) have attracted attention to be employed as thermal health monitors. As clinical thermometers they are stable and reliable presenting high accuracy and resolution [44]. One of the most widely used RTD is the emerging thin-film resistor which has minimal impact on complex circuits due to its small size and due to their negligible mass.
The basic function of the sensor is determined by a proportional increment of resistance when temperature is applied. RTDs can be employed on a rigid or flexible substrate [45, 46, 47], the metal combination with a flexible o rigid substrate can cover conformal applications. RTD fabrication can be done by metals like Pt [48, 49, 50], Cu [51], Ag [52], and Ni [53], among other materials. Nickel presents a suitable option for RTD fabrication due to its wide temperature linear range of operation and its relatively low price.
Clinical thermometers require a high definition and reliability because less than 1°C difference can indicate a health problem. The thermometer signal can be amplified by electronic means, but it is desirable to filter such readings. This work is focused to the filtering and prediction of an highly sensitive Nickel based thin film RTD (range, 273–325 K), to be incorporated to complex circuits [54], we present the theoretical analysis about the relation sensibility-resistance that matches with experimental results.
All metals produce an increase in its resistance to an increase in specific temperature, which means that resistance is linearly proportional to temperature change. This dependence between electrical resistance and temperature is the principle of operation used by a resistance temperature detector (RTD). The relation between temperature-resistance for Pt wire (RTD), is described by the equation known as the Calendar-Van Dusen, Eq. 41) [50].
Where R0°C is the resistance at 0°C, α and β are temperature coefficients and T is temperature, the temperature coefficients depend only on material properties. In addition, the RTD resistance depends on its geometrical design, according to Eq. 42.
Where “σ” is the resistivity, “L” length, “A” lateral area, “w” channel width, and “t” channel height. Only by increasing the length “L” or decreasing the area “A” that means reducing the “t” film thickness or the “w” channel wide, the resistance can increase.
The estimation of the thermal system is represented by the linear stochastic state-space description
Generally, the RTD system is modeled as an RLC circuit, which consists of a resistor a capacitor and an inductor in series with an input voltage. The output that we analyzed is the voltage across the resistor which is related to temperature change. The RLC circuit is represented by a second-order differential equation
Also, we may simplify the response of the system to that of a first-order RC circuit. This implies to solve a first-order ordinary differential equation:
In this work, a Kalman Filter is proposed to decrease the time response to improve the speed feedback and filtering of the perturbations by signal noise from physical signals as thermal detectors. In some instances, a reduced model is advisable to use in an embedded system due to easy implementation and low computational complexity [2].
Kalman filter can be embedded in a temperature system made by Resistance Thermal Detectors (RTD).RTD’s are robust elements that require relatively easy measurement, as a consequence are a useful thermal sensor for industry and medical applications. Nevertheless, these devices are exposing to vibration, electrical noise, and measurement errors generated by the thermoelectric effect caused by the temperature difference between electrical contacts, which affects the response time of the sensor. The implementation of the Kalman filter in a temperature system produces an optimal estimative of thermal behavior and decreases the uncertainties about the prediction of the temperature.
In order to describe the system in the state space, it is necessary to apply system identification methods using MATLAB. Then, after obtaining the system’s state space model we are able to use the Kalman filter algorithm to estimate the future output of the system.
To study the dynamics of our system, we used MATLAB functions
Using MATLAB we are able to acquire the Discrete-time identified state-space model:
with:
Estimated using
System ID using MATLAB. Input output model for a step response defined problem.
Bode diagram indicating the system is a second order system as described by the system transfer function.
Systems model and measured evolutions in time. Fit to estimation data: 90.27%.
We modify the MATLAB example for the time-varying case found in [55] and we code our own function to solve the Discrete Algebraic Riccati Equation. MATLAB functions like predict or forecast were found useful to understand the problem at hand, however they were not used in the code we present here.
w(1:n) = sqrt(Q)*randn(n,1);
v(1:n) = sqrt(R)*randn(n,1);
systv = ss(A,B,C,0,Ts);
ytv(1:n) = lsim(systv,U(1:n) + w(1:n)).
yvtv(1:n) = ytv(1:n) + v(1:n);
Ptv(:,:) = B(:,:)*Q*B(:,:)’; % Initial error covariance.
x = zeros(order,1); % Initial condition on the state.
order = 2;
yetv(1:n) = zeros(n,1);
ycov(1:n) = zeros(n,1);
for i = 1:n.
% Measurement update.
Mn(:,:) = Ptv(:,:)*C(:,:)’/(C(:,:)*Ptv(:,:)*C(:,:)’ + R);
x = x + Mn(:,:)*(yvtv(i)-C(:,:)*x); % x[n|n].
Ptv(:,:) = (eye(order)-Mn(:,:)*C(:,:))*Ptv(:,:); % P[n|n].
yetv(i) = C(:,:)*x;
errcov(i) = C(:,:)*Ptv(:,:)*C(:,:)’;
% Time update.
x = A(:,:)*x + B(:,:)*U(i); % x[n + 1|n].
Ptv(:,:) = A(:,:)*Ptv(:,:)*A(:,:)’ + B(:,:)*Q*B(:,:)’; P[n + 1|n].
end
%% DARE. We coded our own dare function [X,L,G] = sdare(A,B,Q,R).
[P_inf,L,M_inf] = sdare(atv,ctv’,Q,R);
for i = 1:p
% Measurement update.
x = x + M_inf’*(yvtv(i)-ctv*x); % x[n|n].
yetv_inf(i) = ctv*x;
errcov_inf(i) = ctv*P_inf*ctv’;
% Time update.
x = atv*x + btv*U(i); % x[n + 1|n].
P_inf = atv*P_inf*atv’ + btv*Q*btv’; % P[n + 1|n].
end
function [SD] = sdare(A,B,Q,R).
At = transpose(A);
Bt = transpose(B);
S1 = size(A);
E = eye(S1);
Z = zeros(S1);
Ri = inv.(R);
S = B*Ri*Bt;
Pdare = [A Z; −Q E];
Ndare = [E S; Z At];
[AA,BB,L,Z] = qz(Pdare,Ndare);
[AAS1,BBS1,QS1,ZS1] = ordqz(AA,BB,L,Z,‘udi’);
O = ZS1(1:2,1:2);
P = ZS1(3:4,1:2);
H = inv.(O);
SD = P*H;
end
Matlab was used to simulate the response of an RTD modelled as a second order system. In Figure 5(A) we show the plot of the true response y (cyan line) and the filtered response (red line). In Figure 5(B) the plot compares the measurement error with the estimation error. As can be seen in Figure 5(C) the time-varying filter also estimates the covariance errcov of the estimation error at each sample which shows when the filter reached steady state. As it can be seen, we have the possibility to predict the state after approximately 8 seconds. Also, we show the evolution of the estimated temperature response showing an error of −0.0948°C in the best of the cases and less than 1°C in the worst of the cases after 45 seconds.
(A) Evolution of the estimated temperature response showing an error of −0.0948°C in the best of the cases and less than 1°C in the worst of the cases. (B) Evolution of the measurement and estimation errors. (C) Evolution of the covariance of the error showing the possibility to predict the state after approximately 8 seconds.
The unit step response depends on the roots of the characteristic equation. If both roots are real-valued, the second-order system behaves like a chain of two first-order systems, and the step response has two exponential components. If the roots are complex, the step response is a harmonic oscillation with an exponentially decaying amplitude [56]. In our case, the roots of the characteristic polynomial:
The state description for an RC system is described above. From there, we know that the dynamics are dependent only on the RC constant. In addition, there is an amplificator in the system electronics that has a gain of 260. To solve for the RC constant of the system we use the least-squares method (Chi square minimization). The system has a solution of the form
Which is a linear equation. Using a linear fitting program:
We obtain
readings[readIndex] = analogRead(inputPin); // read from the sensor.
total = total + readings[readIndex]; // add the reading to the total.
readIndex = readIndex +1; // advance to the next position in the array.
time_equis_readings[time_equis_readIndex] = time_equis_readIndex;
time_equis_readIndex = time_equis_readIndex +1;
if (readIndex > = numReadings) // if we’re at the end of the array.
{
for(i = 0;i < =numReadings-1;i++).
{
Y[i] = log(readings[i]);
time1[i] = time_equis_readings[i];
sumx = (sumx +time_equis_readings[i]);
sumx2 = (sumx2 + time_equis_readings[i]*time_equis_readings[i]);
sumy = (sumy +Y[i]);
sumxy = (sumxy +time_equis_readings[i]*Y[i]);
}
den = (numReadings*sumx2-sumx*sumx);
a = (sumx2*sumy -sumx*sumxy)/den;
Bc = (n*sumxy-sumx*sumy)/den;
// State description.
A = -Bc;B=Bc;C = 260;D = 0;
//wrap around to the beginning:
readIndex = 0;time_equis_readIndex = 0;
}
// KALMAN.
errcov = C*P*C;
for(i = 0;i < =numReadings-1;i++).
{
Mn = P*C/((C*P*C + R)); // initial estimate.
X = X + Mn*(readings[i]-C*X); // update estimate Average_readings[i].
P = (1-Mn*C)*P; // update covariance.
y_e[i] = C*X;
errcov = C*P*C;
X = A*X + B*U; // project into k + 1.
P = A*P*A + B*Q*B; // project into k + 1.
}
timer0_millis = millis();
// Solution to the Riccati equation.
F = -Bc;H = 260;
SQ = sqrt((H*H*Q*R) + (F*F*R*R));
SR = F * R;
P_inf = (SQ + SR)/(H*H);
M_inf = P_inf*C/(C*P_inf*C + R);
for(i = 0;i < =numReadings-1;i++).
{
// Measurement update.
// M_inf;
x_inf = x_inf + M_inf*(readings[i]-C*x_inf); // % x[n|n].
//P_inf; % P[n|n].
y_e_inf = C*x_inf;
errcov_inf = C*P_inf*C;
// Time update.
x_inf = A*x_inf + B*U;
P_inf = A*P_inf*A + B*Q*B;
}
}
The experiments were performed in a thermal bath giving step responses to the desired setup temperature. Figure 6 depicts the upward and downward evolution of the temperature, the Kalman filter and the two predictors (using two different Q and R settings). As can be seen the predictors follow the Temperature of the sensor closely, especially for the upward way, while the Kalman filter lags behind.
Implemented system. Step response for the upwards and downwards evolution. Two different Kalman filters were used to predict (by solving the DARE equation) the evolution of the future state with different Q and R to calibrate the desired response. In blue the evolution of the RTD sensor analog input, in Yellow and red the two Kalman predictors and the Kalman estimation in cyan color.
Sliding control [57] is an additional tool to predict the behavior of a second order system basically smoothing the system by boundary layers. The prediction of the system state trajectory is given using an uncertain model of the system. The subspace which represents the quantity of uncertainties in the prediction process, forces the estimate state trajectory to switching gain to converge the estimates to within a boundary of the real state values. To predict the state trajectory of our RLC system it’s possible to switch its gain by the subspace represented by a first-order RC model. The estimated state trajectory is forced to keep a switch back and forth within the boundary layer represented in our case by a RC model. By creating a boundary layer, the system is further constrained to have a solution existing in between two RC model solutions.
In Figure 7 it can be clearly seen that the use of two estimators may help predict the behavior of the RTD in a much better way. The system needs to be calibrated first in order to have the two Kalman filters enveloping the required solution. As can be seen in the upward direction, both predictors (yellow and red) envelope the desired response (blue), that of the RTD sensor improving the response of the Kalman filter without boundaries (cyan). Unfortunately, this is not the case in the downward evolution. From the nonlinear control systems point of view these two evolutions demark a region where the RTD stands thus making possible to program a better estimator. It is left as an outlook to program a third estimator using this boundary layer in order to have a better predictor, especially for the downward evolution.
Ascending and descending step responses of the Kalman filter and two Predictors which function in real time. In blue the RTD sensor response, in cyan the estimator response, in yellow and in red the two differently calibrated Kalman predictors.
As it can be shown the implementation of the Kalman filter brings the opportunity to estimate the forecast in real time of a second order system using first, MATLAB and second that of two first order systems using a simple RC system coded in C-language for a microprocessor. It has been shown that the program is able to predict the evolution of temperature for a RTD system. Even if the system is implemented using a first order system we can find evolving solutions for our estimation and prediction to be good enough. We predict the state after approximately 8 seconds showing an error of −0.0948°C in the best of the cases. In addition, a boundary layer may be programmed using two first order Kalman predictors which may be tuned by setting Q and R properly. We believe this is the first report on the use of a Kalman filter to predict the evolution of temperature from a RTD.
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\n\nIntechOpen works with award winning print-houses and we hold to the fact that all of our printed products are of the highest quality.
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\n\n100 - 159 GBP ex. VAT (available in USD and EUR)
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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. His research interests include optimization, computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, and intelligent systems. Prof. Sarfraz has been a keynote/invited speaker at various platforms around the globe. He has advised/supervised more than 110 students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He has authored and/or edited around seventy books. Prof. Sarfraz is a member of various professional societies. He is a chair and member of international advisory committees and organizing committees of numerous international conferences. He is also an editor and editor in chief for various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:"Beijing University of Technology",institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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