Reactive distillation studies aimed at the production of biodiesel through esterification.
\r\n\tFood insecurity results in fear of hunger and starvation that ultimately affects one’s ability to work for sustainability and economic growth of the country. In addition to this, food insecurity results in various chronic diseases due to reduce immunity that ultimately, a burned on the county economy. Therefore, this book will intend to discuss in detail about the food insecurity challenges and their effect on the quality of life. This book will also aim to provide an overview about the new trends and future prospective that help to resolve the food security issues.
",isbn:"978-1-80356-942-0",printIsbn:"978-1-80356-941-3",pdfIsbn:"978-1-80356-943-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"090302a30e461cee643ec49675c811ec",bookSignature:"Dr. Muhammad Haseeb Ahmad, Dr. Muhammad Imran and Dr. Muhammad Kamran Khan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11475.jpg",keywords:"Nutrition, Poverty, Hunger, Food Waste Utilization, Innovative Technologies, Food Processing, Genetically Modified Food, Policy Making, Trade Reforms, Climate Change, Agriculture Productivity, Disease Resistant Crops",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 7th 2022",dateEndSecondStepPublish:"May 5th 2022",dateEndThirdStepPublish:"July 4th 2022",dateEndFourthStepPublish:"September 22nd 2022",dateEndFifthStepPublish:"November 21st 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"An emerging scientist in the field of food science and technology with special expertise in development of rapid and nondestructive technologies, chemometrics and data mining.",coeditorOneBiosketch:"Muhammad Imran has expertise in extrusion technology, microencapsulation, lipids chemistry, sensory evaluation and food process engineering.",coeditorTwoBiosketch:"A renowned scientist with expertise in Novel food processing technologies.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"292145",title:"Dr.",name:"Muhammad",middleName:null,surname:"Haseeb Ahmad",slug:"muhammad-haseeb-ahmad",fullName:"Muhammad Haseeb Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/292145/images/system/292145.png",biography:"Dr. Muhammad Haseeb Ahmad is currently an assistant professor in the Department of Food Science, Government College University Faisalabad, Pakistan. He also served as an assistant professor for one year at the National Institute of Food Science and Technology, University of Agriculture Faisalabad, Pakistan. He received his doctoral degree from Hohenheim University, Stuttgart, Germany, in 2016. During his stay there, he also worked as a research associate for research projects relevant to various food disciplines. Dr. Ahmad is the author of about thirty five research publications and twelve book chapters. He has also presented his research work at various national and international conferences (25). 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He won the Indigenous and IRSIP (Department of Food Science and Human Nutrition, Michigan State University, East Lansing, USA) Fellowships for completion of doctorate research funded by HEC, Islamabad, Pakistan. Dr. Muhammad Imran has expertise in extrusion technology, microencapsulation, lipids chemistry, sensory evaluation, and food process engineering. Until today, Dr. Muhammad Imran has authored 80 publications (International & National) in various Impact Journals of Scientific repute and written 15 Book Chapters as principal author and co-author. 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The common factor is the binding between macromolecules and tannins that lead to: (i) the conversion of an animal hide into the leather (tanning or
In the tanning process, the tannins bind to the hide’s matrix, which is composed primarily of the protein collagen ordered in microcrystalline helical units. The purpose of
In higher plants, tannins are primarily reserved as a chemical defence against pathogens. The complex with macromolecules such as cellulose and pectin, send out the exo-enzymes capable of utilising cellulose or pectin, either as a carbon source or for branching cell wall barriers to more nutrient-cytoplasm, depriving of the substrate or binding sites to these substrates. Another important function of tannin complexes is to impede the decomposition of plant litter, also when the leaf is fallen. This provides the delay in decomposition, which allows a constant input or seasonally demanding input of nutrients to the soil [1].
In the other processes, proteins of animal or human saliva interact with tannins of the unripe fruit, forages, or vegetable-derivates such as red wine, tea, and chocolate. Tannin molecules can bind proteins or enzymes at the level of specific amino acids, and modify the folding, the molecular weight, and the core binding site, to form soluble complexes or precipitates, which can alter protein function or inhibit enzyme activity [8]. This binding is at the basis of the astringent sensation experienced when tannins precipitate salivary proteins, and as a result, they lose their ability to lubricate the epithelial membranes of the mouth [6]. This sensation in mouth discourages the animal from feeding the unripe fruit or high-tannin forages and determines the unpleasantness of consumers for some tannin-rich products. These are the reason why, in the last decades, the interest in astringency has been constantly increased in different research areas.
The term astringency derives from the Latin verb,
Whilst the chemical definition of astringency is related to the ability of tannins to bind proteins, in sensory terms, it is described as different and concomitant feelings of drying, puckering, and roughing [17, 18]. Astringency can be defined as a tactile sensation, because: (i) it is perceived on non-gustatory surfaces such as on the soft palate, gingiva, lips [12], (ii) does not show adaptation but also (iii) increases upon repeated ingestion [19], leading to carry-over effects during the tasting. However, side tastes as bitterness, sourness, and sweetness can highly modulate the overall astringency [20]. The sensitivity of MRs to astringents as well as basic tastes may elucidate the complexity of red wine astringency, which has been described by 33 different subqualities [21]. Amongst these “hard,” “green,” and “rich” have been associated with bitterness, acidity, and high flavour concentration, respectively [22], “harsh,” “abrasive,” and “drying” have been found to define astringency as a negative sensation, whilst the “complex” and “mouth-coat” subqualities have been associated to a positive impact during tasting [21]. These subqualities were also associated with touch standards when utilised to describe the tactile astringent sensations in the mouth elicited by red wines [23, 24]. The qualitative traits of astringency as “soft”, “mouth-coat”, and “rich” represented the drivers of liking for Sangiovese wine [25]. Similarly, for Tannat [26], and Côtes du Rhône and Rioja appellations wines [27], the attribute “mouth-coat” contributed to the quality of the wine.
It is also true that the perception of astringency is mediated by psychological factors [28], but salivary protein composition [29] and tannin’s structure and composition [30, 31] represent the principal factors. In this regard, numerous reviews have been produced during the past years [32, 33, 34, 35, 36, 37, 38].
Saliva is a biological fluid primarily produced by the three pairs of “major” salivary glands (parotid, submandibular, and sublingual glands) in mouth and by the minor ones by 10% [39]. In the whole, saliva are presently more than 2000 different proteins and peptides [40, 41], which are the result of protein post-translational modifications before being secreted in the mouth [42]. Although saliva is predominantly a watery fluid (99.5%) with a complex mixture of proteins (0.3%; 1–2 mg/mL), ions and other organic compounds (0.2%) are also present. The whole saliva continuously baths the oral cavity and having a pH ranging from 6.2 to 7.4 acts as a buffering system. The saliva is continuously secreted (0.3–7 mL/min) and plays a role in protecting the tooth and mucosal integrity, in antibacterial and antiviral activity, digestion of food, speech, lubrication, taste, and represents a biomarker tool for some diseases [41, 43]. The main families of proteins include enzymes (amylase, carbohydrase, lipase), lactoferrin, high (M1), and low (M2) molecular-weight glycoproteins (mucins), peptides as agglutinins, immunoglobulins, proline-rich proteins (PRPs), cystatins, histatins and statherins [44].
There is evidence that saliva may affect the way we perceive the taste and mouthfeel of foods in various ways [45, 46, 47]. During the wine tasting, saliva transports and dissolves the stimuli substances [48]. Saliva constituents are of great importance for establishing protein-tannin interactions. In particular, the PRPs, histatins, mucin, amylase are the main salivary proteins involved in the binding with polyphenols eliciting astringency [49]. The differences between the binding of the same polyphenol to different proteins result from differences in the amino acid sequences [50].
The PRPs account for approximately 70% of the total secretory protein and are subdivided into acidic, basic, and glycosylated PRPs. They are characterised by an abundance of proline, glutamic acid/glutamine, and glycine [51]. The presence of these four amino acids, especially proline, which are the so-called alpha-helix breakers, enables the protein to form secondary structures, which assumes a random coils conformation in solution [10, 52]. This feature may allow PRPs to universally bind various types of polyphenols, mainly tannins with different sizes and structures. Some species, such as humans, rats, and mice, produce PRPs containing about 40% proline [53, 54]. However, some species produce salivary proteins, which are rich in proline but do not show a high affinity to tannins due to extensive glycosylation [54].
Parotid and submandibular secretions also contain several low molecular-weight histidine-rich peptides [55, 56]. Amongst 12 forms, the histatin 1, 3, and 5 are predominant and vary in size from 7 to 38 residues. These peptides show a high content of basic residues, such as lysine, arginine, and histidine [57]. They tightly bind tannins, even if some peptides are devoid of proline [58]. Conversely, others observed high tannin precipitation by histatins thanks to the interactions formed by basic residues and proline [59].
Amongst the low molecular weight salivary proteins, there is a selectivity in binding polyphenols (as PGG, procyanidin trimer, epicatechin, malvidin-3-glucoside): the acidic PRPs considerably form soluble and insoluble complexes with PGG and trimer but not with epicatechin; basic PRPs and glycosylated PRPs seem to not interact with trimer, whilst basic PRPs show a high affinity for epicatechin, malvidin-3-glucoside, and a mixture of both; the statherin shows no selectivity [60, 61].
Mucins are the major constituents of the viscous layer coating hard and soft tissues in the oral cavity. Mucins are generally composed of a peptide core (apomucin) enriched in serine, threonine, and proline residues and carbohydrate side chains (oligosaccharides) that are linked O-glycosidically to threonine or serine. M1 is a polymeric mucin due to the formation of disulfide linkages between cysteine residues in non-glycosylated domains, whilst M2 is a monomer [62]. Average proline content of 10% seems to be responsible for protein-phenol interactions [63].
Amylase is secreted mainly by the parotid gland in both glycosylated and non-glycosylated isoforms [64]. It is an enzyme capable of hydrolysing bonds within amylose and amylopectin and is composed mainly of amino acids like aspartic acid > glutamic acid > arginine [65]. However, amino acids as tyrosine and tryptophan seem to be crucial for interaction with polyphenols [66]. The non-glycosylated form of amylase contains 22 proline and 16 tryptophan amino acid residues in its sequence that enable the binding with polyphenols [50].
Astringent wines are commonly defined as “tannic” because tannins are the main polyphenolic compounds involved in the sensation of astringency. Swain and Bate-Smith [67] provided the first useful phytochemical definition of tannin, being “water-soluble phenolic compounds, having molecular weights lying between 500 and 3000, which have the ability to precipitate alkaloids, gelatin, and other proteins”. Tannins can be classified in condensed tannins, phlorotannins, and hydrolysable tannins. Condensed tannins are large macromolecules that consist of two or more monomeric (+)-catechin or (−)-epicatechin units called procyanidins, whilst prodelphinidins consist of (+)-gallocatechin or (−)-epigallocatechin units. In plants, condensed tannins are found as oligomers (2–10 monomer units) or polymers (>10 monomer units). The number of monomer units in a polymer may be as high as 83 units [68]. The subunit composition varies amongst tannins from grape skins, seeds, and stems [69, 70, 71]. The phlorotannins are present in marine brown algae as polymers of phloroglucinol (1,3,5 trihydroxy-benzene) in different ranges of molecular sizes (126 Da–650 kDa). They are analogous to the terrestrial condensed tannins since they do not contain a carbohydrate core [72]. Hydrolysable tannins, structurally perhaps the most complex tannins, comprise three subclasses such as simple gallic acid, poly-galloyl esters of glucose (gallotannins), and esters of ellagic acid (ellagitannins). Derivatives of gallic acid contain one to five galloyl groups that can be esterified to either glucose (e.g., pentagalloyl glucose) or quinic acid (e.g., monogalloyl quinic acid). Gallotannis can contain six or more galloyl groups and can be characterised by having one or more digalloyl groups (e.g., hetpagalloyl glucose). Complex gallotannins have a higher capacity for precipitating proteins than simple galloyl glucoses [73].
Ellagitannins may be divided into six subgroups: hexahydroxydiphenoyl esters, dehydro-hexahydroxydiphenoyl esters and their modifications, nonahydroxytriphenoyl esters (e.g., vescalagin), flavonoellagitannins (e.g., acutissimin A), and oligomers with different degrees of oligomerisation and types of linkages [74].
Tannins are the main responsible for the qualitative aspects of astringency as well for the intensity of the sensation. Grape seed and skin tannins are felt astringent as the mean degree of polymerisation (mDP), and galloylation increased [75]. Their ability to precipitate proteins also increases with mDP up to a given degree of polymerisation [34, 76]. However, monomeric and dimeric flavan-3-ols can induce astringent and bitter sensations [77]. Galloylation of monomers/oligomers and polymers enhances protein precipitation, and its extent depends on the grape variety [78]. The presence of high galloylation seems to be responsible for the coarse perception [75], which in turn can be decreased by a high content of epigallocatechin units on the tannin molecule. On the contrary, it seems that the hydroxylation of B-ring seems to decrease velvety astringency and increase the perception of puckering and drying astringency of wine fractions [79]. Salivary proteins seem to have a higher affinity for condensed tannins than for hydrolysable tannins because of different structural flexibility, size, polarity, affinity constants, and presence of free galloyl groups [80, 81, 82, 83, 84]. Oakwood tannins were mainly associated with smooth and mouth-drying sensations at low concentrations [85]. Astringency subqualities such as mouth-coat, full-body, persistent were mainly associated with oak-derived tannin, whilst the velvet, soft, and satin terms were associated with the exotic wood-derived tannin [25].
Compounds able to elicit sensations as tastes and mouth feelings are called
Anthocyanins, composed of a sugar bound to the anthocyanidin moiety (cyanidin, peonidin, delphinidin, petunidin, and malvidin), impart colour to the grapes and red wine and can be modified by different enological practices [93]. Controversial is the studies of the influence of anthocyanins on astringency. An anthocyanin fraction added in model wine solution was felt as “rough and chalk,” and slightly contributed to the overall astringency probably for contamination of the fraction with unknown phenolic compounds [94]. Successively, the isolated fractions of anthocyanidin–glucosides and anthocyanin coumarates did not influence astringency of wine solutions either the “coarse,” “chalk,” or “dry” astringent subqualities [95]. However, anthocyanins were able to interact with human salivary proteins forming soluble aggregates [96], and even precipitates, being the cinnamoylated the most reactive fraction (precipitation between 6.5 and 17.5%), also influencing the astringency perception [97]. Pyranoanthocyanins, anthocyanin-derived pigments that can form during red wine ageing, seems to be involved in astringency, since they are able to interact with salivary proteins by phenol, catechol or even flavanols structures, similarly to procyanidins [98].
Flavonols (kaempferol, quercetin, and myricetin) are present in grapes and wine as glycosides (sugar attached). In the plant, they act as a natural sunscreen in the skin of grape berries. In wine, they can be hydrolysed and act as cofactors for colour enhancement. Flavonol glycosides, such as 3-O-glucosides and 3-O-galactosides of quercetin, syringetin, and isorhamnetin, have been reported to be astringent at low detection threshold levels and characterised by a velvety astringency [99]. The addition of quercetin 3-O-glucoside (2 g/L) to wine increased astringency, leading to the formation of complexes with saliva at 200 μM [100]. However, such concentrations are not naturally present in red wine, in which quercetin 3-O-glucoside can range from 2 up to 34 mg/L, depending on the cultivar [101].
Many sensory active non-volatile compounds comprising hydroxybenzoic acids, hydroxycinnamic acids, flavon-3-ol glycosides, and dihydroflavon-3-ol rhamnosides were identified as the key inducers of the astringent mouthfeel of red wines using a molecular sensory approach [99]. The phenolic acids in wines, especially hydroxycinnamic and benzoic acid derivatives, have been reported to be more puckering astringent. These compounds have also been correlated with astringency in free-run and pressed wine [102]. The trans-
Given that the carbonyl function of salivary proteins is a very effective hydrogen bond acceptor [104], it would appear that it would play a significant role in bonding to polyphenols hydroxyls [10, 105]. Nowadays, the interaction between proteins and proanthocyanidins is widely recognised to be a combination of hydrogen bonding and hydrophobic effects in the acidic wine matrix. However, covalent bonding is also possible between proteins and polyphenols during oxidation [106] and nucleophilic addition processes [107]. In this chapter, we focused on the non-covalent binding involved in the astringent sensation.
Physico-chemical quantities (binding constants, stoichiometry, and atomic structure of complexes, driving forces for the association) have been utilised to understand the multifaceted sensation of astringency. Many techniques including circular dichroism (CD) [108], isothermal titration microcalorimetry (ITC) [109], fluorescence spectroscopy [50], dynamic light scattering (DLS) [110], and nuclear magnetic resonance (NMR) [111] have been employed to understand the formation mechanism of protein/polyphenol aggregates in solution. Generally, these studies focused on interactions between protein segment from human saliva PRPs proteins family and selected procyanidins, because it represents the easiest way to simulate such a complex phenomenon. They can reveal the hydrophobic interactions formed between the phenolic rings of the procyanidins and proline residues, and the hydrogen bonding between the hydroxyl groups on the phenolic B-ring and hydrogen acceptor sites of the peptide bond [52, 112]. The aggregation of procyanidin with peptide seems to be firstly mediated by hydrophobic forces, and then hydrogen bonding has been postulated to provide directional and robust bonding that stabilises the complex. The peptide is coated by polyphenols, which provides a crosslink between two or more peptides up to a critical point, after which precipitation begins. The stability of these complexes depends on the tannin dimension and number of free phenolic groups, as well as the nature of the protein involved [81, 109].
The driving factors that determine the binding between tannins and salivary proteins were identified to be the critical micelle concentration value (CMC), tannin structure preferences, and tannin colloidal state [113]. Below the values observed in wine (from 1.5 to 2.9 mM), procyanidins specifically interacted with peptide through hydrophilic recognition. A network of interactions can be formed depending on tannin conformation, and precipitation of the complex can occur, or if an intramolecular staking Π-Π of phenolic groups is preferred, the precipitation is not observed. Above these values, tannins spontaneously tend to form aggregates that, at first through specific interactions bind proteins, and then surrounded by the hydrophobic residues, stabilise the complex by hydrophobic bonding. To summarise, both hydrophilic and hydrophobic interactions contribute to form a complex network, which determines the precipitation of salivary proteins with tannins.
A method for measuring astringency remains one of the great analytical challenges in wine chemistry and oenology. The interest in investigating the mechanisms and interactions between polyphenols and proteins can allow us to find the optimal way to simulate and evaluate what happens during the red wine tasting. Quite often, sophisticated techniques rely on the purification of both tannin and protein fractions, the extrusion from the wine content, and the omission of matrix components during reactions, and all contribute to send away astringency from the reality that is: wine polyphenols interacting with salivary proteins in mouth, causing drying sensations.
Several procedures have been carried out during the last decades for measuring tannins. Additionally, analyses of soluble (turbidimetric analysis) and insoluble (precipitation protein assays) protein-polyphenols complex have been developed for assessing astringency. The sensory analysis represents the human response as an analytical tool to evaluate wine perception. Many training and tasting sections are necessary over a long period involving a high number of tasters to form a reliable panel. In the case of astringency, it is complicated to discern amongst tastes and brings on fatigue. A method capable of estimating tannin palatability has to be the most objective as possible and must correlate with sensory data in order to reflect the real phenomenon of wine tasting.
Amongst
In the past, many colourimetric techniques were developed to analyse phenolics compounds spectrophotometrically. The first one used the Folin-Denis reagent [114], which was successively modified [115, 116], and lastly into the Folin-Ciocalteau assay [117]. However, they were not specific for tannins but detected any phenolic compound. More specific colour reactions were used to measure condensed tannins and their precursors. Depolymerisation in HCl and n-butanol of proanthocyanidins yield anthocyanidins that can be quantified spectrophotometrically [118, 119]. Others used vanillin reagent for flavanols [6, 120], or
Other methods, based on the acid-catalysed condensation reactions with benzyl mercaptan (thiolysis) and phloroglucinol (phloroglucinolysis), can determine both the chain length (mDP) and composition by HPLC [124, 125]. Most of our current knowledge about the general composition and structure of grape and wine tannins have been obtained by depolymerisation [126]. Poor yields due to reaction product instability, reactions with non-proanthocyanidin compounds, and side reactions also contribute negatively to the utility of thiolytic methods [124]. The problem with phloroglucinolysis, on the other hand, is that it produces low yields, and only a fraction of the tannin is converted to known flavan-3-ol products [127]. Normal-phase HPLC (NP-HPLC) method has also been developed to quantify the proanthocyanidins into low and high molecular-weight polymers [128]. A simple method based on Fourier transform mid-infrared (FT-MIR) spectroscopy combined with multivariate data analysis, was successfully used to measure the tannin concentration of 86 red wines, previously purified by solid-phase extraction (SPE) [129].
Protein precipitation assays are of particular interest because the interaction of proteins with tannins can be used to model astringency perception [130]. The ability of gelatin to precipitate phenols, including tannins, has been observed since 1934 [131]. The same phenomenon was observed when hide powder or polyvinylpyrrolidone were used in high concentrations [132]. Bate-Smith [130] noted that protein of skin differed from proteins of saliva, which caused the “puckery” sensation induced by tannin. For measuring the relative astringency of tannins, a spectrophotometric technique based on the precipitation of the haemoglobin with tannin was then introduced [130]. Similarly, another spectrophotometric technique measured the inhibition of β-glucosidase after the precipitation with tannic acid and condensed tannins [133]. Alternatively, Hagerman and Butler [134] used bovine serum albumin (BSA) as a precipitant agent, which was successively taken by Harbertson et al. [135] for wine analysis. Glories [136] proposed the gelatin index, in which tannins were precipitated by gelatin protein. This procedure required the measure of proanthocyanidin concentration before and after precipitation with an excess of gelatin. Besides, gelatin is a heterogeneous mixture of proteins, and its composition may change amongst the different commercial products, leading to a source of variability and imprecision of data. For this, some researchers replaced gelatin with ovalbumin [137]. Another tannin assay used the methylcellulose to precipitate tannin (MCP) [138, 139]. The MCP tannin assay is based on the formation of an insoluble polymer-tannin complex, which can be separated by centrifugation. The total phenolic content (absorbance at 280 nm) is measured in control and treated samples. However, if the assays utilise synthetic agent or protein different from saliva, the binding reaction seems not to reproduce the physiological conditions during the wine tasting, because the binding affinity of the protein is not comparable to that of salivary protein. In the case of bovine serum albumin, it has been shown that the salivary protein has a higher affinity for tannin than BSA. In fact, in the presence of an excess of BSA, the tannin preferentially bound the salivary protein. Other proteins, including dietary proteins, may not complex any tannin in the presence of the salivary tannin-binding protein [8]. The use of salivary proteins has been proposed to represent the model system for astringency better. In precipitation assays, fractionated [8, 140] or whole [141, 142] human saliva has been used. Mixing whole saliva and grape polyphenols give rise to a “soft cloudy” precipitate, which gathered after centrifugation on the bottom of the tube so that the supernatant was easily recovered without disturbing this pellet. The binding reaction was performed at 25°C, and the complex formed was successively precipitated by centrifugation at 4°C in order to stop further reactions. The induced precipitation allowed to separate the proteins bound to polyphenols from whose remained in the solution that not reacted with them. Both the nature of condensed tannin [141] and salivary proteins [142] involved in the precipitation were analysed. In both works, the sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) of human saliva was carried out, Sarni-Manchado et al. [141], analysed the tannins in the supernatant and pellet. In contrast, Gambuti et al. [142], analysing the supernatant, revealed the proteins mainly reactive with polyphenols by comparison with the control saliva. Evidence of the qualitative and quantitative changes in salivary protein profile after tasting tannin solutions and wines was also made by HPLC [143]. Interactions and precipitation of low molecular weight salivary proteins with procyanidins confirmed the involvement of different families of salivary proteins in the development of astringency [144]. The use of salivary proteins involves the collection of human saliva from different healthy volunteers according to a specific protocol, and it must take into account the salivary flow to limit the effect of individual differences in astringency perception due to subjects’ saliva characteristics [145].
Nephelometry is a method that allows a direct estimation of the amount of protein/tannin complexes by measuring the scattered light in the solution that results from the gradual formation of a cloudy precipitate corresponding to the soluble aggregate. Chapon [146] proposed this technique by studying the interactions between beer polyphenols and proteins involved in the colloidal instability of beer. Similarly, the haze formed between salivary proteins and polyphenols represents the first step in the development of astringency and can be measured with a turbidimeter [147, 148]. A continuous flow method was also used to study the interactions between grape extracts and wine with BSA at different concentrations [149]. Globular proteins and PRPs were used to measure a relative tannin specific activity of procyanidin oligomers from grape seeds [30], and PRPs showed the strongest affinity. Human salivary proteins have been considered as the most suitable model proteins. For this reason, in turbidity measurement, whole human saliva [148] and mucin, a high molecular weight salivary protein [150], were used as model proteins for astringency assessment. Based on polyphenol/mucin reactivity, a micro-plate assay was also developed [151]. Tannic acid [150], grape seed extracts [151], wine extracts [63], tannin fractions added to model solutions [152] were analysed by nephelometry. The turbidity of the solution, formed by the tannin-protein aggregates, linearly correlated with astringency. However, no direct analysis of wines was carried out. Lastly, instead, wine samples were analysed trough nanotechnology such as localised surface plasmon resonance (LSPR) combined with surface imprinted polymers, as a measure of the interactions of polyphenol with salivary protein and then astringency [153].
The sensory analysis represents the human response to wine tasting. A sensory panel can provide information about the sensory properties of a product, but significant training is required before the panel becomes a reliable sensory instrument. Astringency is a difficult sensory attribute to evaluate, owing to particular characteristics of the sensation. Generally, it is evaluated by tasting but can suffer from individual subjectivity. The feeling can take over 15 seconds to develop fully and is known to build in intensity and become increasingly difficult to clear from the mouth over repeated exposures [19, 154]. Carry-over effects can occur. When wines or tannic solutions are evaluated by a well-trained panel using established sensory methodologies, the panel leader can expect to obtain reliable information about the intensity in the perceived astringency of the samples. Screening, selection, training, and panel maintenance are exercises that help the panel attain proficiency before sample evaluation. Classical methodologies widely applied are descriptive and rating sensory analyses. The first helps to distinguish between samples by a qualitative description of their sensory properties [75] and the second permits to scale samples according to the intensity of the perception. However, time-intensity (TI) is a temporal methodology widely used. This method consists of recording one by one the intensity evolution of given attributes [155]. However, TI showed some limitations because it is time-consuming due to the evaluation of only a few attributes at the same time [156]. Furthermore, carry-over effects can overcome when assessing the temporal perception of an attribute [157]. To overcome these drawbacks, Pineau et al. [156] developed a new method called temporal dominance of sensations (TDS), which consists of identifying and rating sensations perceived as dominant until the perception ends. Before the development of this method, a similar experimental approach was successfully used to describe the temporality of sensations in wines [158]. Astringency, a dynamic sensation, takes many seconds to develop after the basic tastes, and the duration depends on the wine. Notwithstanding, TDS can be difficult when panellists had select the dominant attribute and score its intensity, but proper training can overcome this problem [159].
It is also essential to discuss and familiarise with the terms associated with astringency. A vocabulary of 33 terms has been proposed by a combined panel of experienced tasters and winemakers to describe the mouthfeel characteristics of red wines [160]. The check-all-that-apply (CATA) question that consists of a list of subqualities from which the panellists have to select all the options they consider appropriate to that wine has been utilised for the characterisation of the astringency subqualities of Tannat wine [161]. Recently, a sensory method that combines CATA approach and training in astringency subqualities with touch-standards resulted very useful for investigating the astringency characteristics of red wines [24, 25, 162]. In any case, intense training is necessary to distinguish astringency from other tastes, especially bitterness, and to reveal the different qualitative attributes. Fatigue and loss of
Because astringency is one of the main attributes for wine quality, winemakers are interested in an analytical and objective method to evaluate it. No method can substitute entirely sensory analysis, but a method that results in a reproducible index has to correlate quite well with it. A statistically significant correlation between the sensorial and analytical methods is necessary.
The gelatin index has represented the almost widely analytical method for estimating astringency in red wine [136]. Besides, it furnished only approximate results [137]. Successively, a positive correlation (R2 = 0.56) between the gelatin index and time-intensity data was obtained only at a low concentration of polyphenols utilising 29 wines judged by 10 panellists [163]. A method that used the ovalbumin in alternatively to gelatin as a precipitation agent was proposed to determine astringency [137]. Ten wines were tested by 10 expert enologists evaluating the astringency on a scale from 1 to 100. The method resulted in more reproducible than the gelatin index and was positively correlated (R2 = 0.77) with sensory analysis. This method was also used to assess the astringency of Greek wines, and a good correlation was found (R2 = 0.93) [164]. Another predictive model for astringency estimation was based on phenolic compounds and colour analysis of 34 wines by 12 judges on a 9-point intensity scale [165]. Multiple regression generated three possible models to predict astringency from analytical data, the most simple depended on total phenolics and co-pigmented anthocyanins, besides the predicted astringency plotted versus observed astringency resulted in low but acceptable correlation from a sensory perspective.
Monteleone et al. [150] proposed a predictive model by measuring the polyphenol-mucin reactivity in which the capability of polyphenolic extracts to induce astringency was estimated on their ability to develop turbidity in the
In a study by Kennedy et al. [166], 40 red wines were evaluated by a panel consisting of three winemakers and two enologists for the astringency intensity scored from zero to 10. The aim was to correlate astringency and tannin concentration measured by different analytical methods: absorption at 280 nm, phloroglucinolysis, gel chromatography, and BSA protein precipitation. The analytical method having the strongest correlations with perceived astringency was the protein precipitation one (R2 = 0.82). Protein precipitation represents the method the most similar to the physiological response to astringent
The SPI represents a useful tool to assess the physiological response to astringents, measuring the astringency of red wine indirectly. This index evaluated the precipitation of salivary proteins occurring during the tasting of an astringent stimulus. The SPI, analysing the salivary protein pattern by SDS-PAGE electrophoresis, has been improved considering the in-mouth temperature (37°C) for the binding reaction, the choice of resting saliva, and the ratio saliva:wine. The excess of saliva with respect to wine (2:1) in a static environment permits to measure the binding capacity of tannins better [167]. Successively, to reduce the time and solvents, the chip electrophoresis replaced the SDS-PAGE, providing similar results [168]. In the last years, the SPI has been used for different technological practices proving useful information for winemakers and enologists to manage the style and quality of red wines.
In winemaking, the clarification process is fundamental to stabilise and clarify the wine by adding exogenous proteins into wine [169]. Proteins used for fining interact with wine tannins by a mechanism similar to that occurring during the tasting. The interaction protein-tannin, binding, and precipitation determine a decrease in polyphenolic compounds responsible for the sensation of astringency [170]. The SPI was used to evaluate the efficacy of the fining of different proteins at different concentrations in Aglianico [171], and Sangiovese wines [172]. In Aglianico, the gelatin (animal protein) and patatin (plant protein) showed similar efficacy in diminishing wine polyphenols reactive towards salivary proteins, and then astringency, whilst in Sangiovese it depended on the polyphenolic content of the wine. The information provided by SPI was useful to understand that each wine, with peculiar polyphenolic composition, should be treated maintaining the ratio anthocyanins and tannins such as to assure a modulation of astringency and at the same time a correct evolution of the colour during ageing.
A common practice is the utilisation of enological tannins as a substitute for oak barrels to improve colour stability and taste and is authorised by the International Organisation of the Vine and Wine (OIV) for musts and wines clarification [173]. Commercial preparations of tannins of different origins showed different abilities in precipitating salivary proteins: condensed tannins resulted in higher SPI and astringency than hydrolysable tannins. The addition of tannins in wines modify the astringency or not depending on the wine phenolic content. The SPI was useful to understand the effect of tannins addition on wine astringency in order not to compromise overall wine quality [83]. Similarly, after a moderate oxidation (21 mg/L of oxygen equivalent), the addition of 2 g/L of enological tannins did not result in an increase in the reactivity of wine tannins towards salivary proteins after 30 days of treatment. This effect was also shown in the oxidation process in the presence of acetaldehyde [174]. The SPI seems to be sensitive to reaction-products such as polymers of flavanols and anthocyanins formed directly or via a molecular bridge (e.g., acetaldehyde) [31, 175], and new-formed proanthocyanidins [93, 176]. This may explain why during the oxidation of red wines, the SPI followed a different trend from BSA reactive tannins [174, 177, 178].
The decrease of astringency with time has been shown to depend on the reduced concentration of tannins due to precipitation [31, 68], but the trend is not strictly related to the age of wine [179]. The astringency of red wine decreases during ageing because of the changes in the structure of tannins due to cleavage reactions generating low molecular weight species [31], polymerisation without the participation of anthocyanins and subsequent precipitation [95], direct or indirect condensation with anthocyanins [180], and the formation of flavan-3-ol sulfonates by SO2 [181]. Wine becomes soft and mellow for the decline of tannin mean degree of polymerisation [182], velvet and mouth-coating for the formation of the polymeric pigments [24], or satin for lower content of flavans and astringent tannins (measured by SPI), and higher formation of polymers [183] after ageing. Studies on Sangiovese wine revealed that the astringency profile changed from an unripe, dry astringency towards rich, full-body, and mouth-coating sensations after about 2 years of ageing [184]. However, pucker sensations can appear if the oxidation is excessive [24, 25]. Astringency subqualities have been able to discriminate wines of different denominations with a chemical age of 3–5 years, more than other wine parameters [25]. Red wine benefits of a moderate oxygenation during ageing favouring changes in tannin structures that, affecting their reactivity towards proteins, can modulate wine astringency. The SPI was utilised to objectively evaluate changes in astringency as a function of oxygen uptake before and after bottling [185]. Although conflicting results were reported for astringency after micro-oxygenation of wines, a significant variation of wine reactivity towards salivary proteins and, then, in wine astringency was observed after 42 months of ageing in bottle only in low pH wines. Moreover, oxygen permeating towards closures determined changes in wine phenolics detectable only using SPI. It was significantly lower when the bottles were sealed with closures at high oxygen transfer rate (OTR). Such differences were not perceived by sensory analysis, demonstrating that SPI can be more sensitive in revealing slight differences in the reactivity of tannins. Lastly, the effect of ageing on the precipitation of salivary proteins is a function of ageing time, wine pH and phenolic composition, and oxygen level in red wine. The decisive role of pH on wine astringency has been confirmed in a recent work of Forino et al. [92], in which the SPI was used to measure wines with different pH levels (3.7–3.2) obtained by adding strong acids or bases, which made the wine unsafe to taste. The binding and precipitation of wine tannins with saliva proteins was favoured at low pH values, and this effect was dominant with respect to the tannins content. Previously, the tartaric acid addition in wine, modifying the pH, resulted in high SPI [186], due to the increase of tannins in the phenolate form, and therefore to an increase of hydrogen bonding with salivary proteins. It is also likely that at low pH increases the accessibility of the binding sites leading to enhanced Van der Waal interactions and hydrogen bonding between proteins and polyphenols [187]. However, other parameters, such as ethanol, fructose, and mannoproteins have been shown to influence astringency and SPI [186]. The effect of mannoproteins on the inhibition of salivary protein precipitation was also showed in Aglianico and Sangiovese wines after 12 months of ageing. The sensory analysis confirmed a reduction in wine astringency. Some mannoproteins interact with tannins forming higher molecular weight structures that prevent the binding with salivary proteins, and thus are not able to elicit astringency [94]. Mannoproteins can also act as steric stabilisers limiting the binding with tannins [112]. Wine polysaccharides inhibit tannin-salivary proteins interaction by a mechanism that involves the formation of protein-tannin complex firstly, probably ruled by hydrophobic interactions and stabilised by hydrogen bonds, and then the polysaccharides can act by a ternary mechanism through the encapsulation of this complex, increasing its solubility. However, the efficiency depends on the polarity of both salivary proteins and tannins [188]. Beyond the molecular mechanism, mannoproteins can highly influence the qualitative sensory perception of astringency, conferring positive subqualities of astringency to red wines [162].
Astringency is still a complex phenomenon, and despite the many efforts from researchers, it is not fully understood. However, the different
The authors declare no conflict of interest.
BSA | bovine serum albumin |
CATA | check-all-that-apply |
CD | circular dichroism |
CMC | critical micelle concentration |
DLS | dynamic light scattering |
FT-MIR | Fourier transform mid-infrared |
OIV | International Organisation of the Vine and Wine |
ITC | isothermal titration microcalorimetry |
LSPR | localised surface plasmon resonance |
M1 | high molecular-weight mucin |
M2 | low molecular-weight mucin |
mDP | mean degree of polymerisation |
MRs | mechanoreceptors |
MCP | methylcellulose precipitable-tannin |
NP-HPLC | normal-phase HPLC |
NMR | nuclear magnetic resonance |
OTR | oxygen transfer rate |
PRPs | proline-rich proteins |
SPI | saliva precipitation index |
SDS-PAGE | sodium dodecyl sulphate-polyacrylamide gel electrophoresis |
SPE | solid-phase extraction |
TDS | temporal dominance of sensations |
TI | time-intensity |
Reactive distillation is an operation that incorporates chemical reaction and physical separation in a single unit [1]. This process, when applicable, has several potential advantages for the industry when compared to conventional systems, such as the reduction of capital costs, improvement of component separations, use of fewer instruments for monitoring, reduction of energy costs, and improvement in reaction selectivity [2, 3, 4, 5, 6]. Among the possible applications of reactive distillation, the separation of azeotropic mixtures and compounds with similar boiling points can be carried out by adding a reagent that promotes the consumption of one component of the mixture and forms a product with markedly different physical properties, thereby favoring separation. However, the use of reactive distillation is more frequent in systems where product formation is limited by chemical equilibrium. This separation technique allows for the constant removal of one or more products promotes or increases reactant conversion [7].
The first patents for the reactive distillation process were published in the 1920s, developed by Backhaus for the production of esters [8, 9, 10]. However, few industrial applications were developed before the 1980s [11], when the commercial process for methyl acetate synthesis via reactive distillation with a homogeneous catalyst was patented by Agreda and Partin [12], in collaboration with the Eastman Chemical Company. This application of reactive distillation is considered an exemplary case because of the substantial reduction in process costs (∼80%) [13] achieved through the elimination of units, such as reactors and separation columns, and the possibility of heat integration. The conventional methyl acetate synthesis process (Figure 1), which comprises 11 different steps and 28 pieces of equipment, was replaced by only a highly integrated reactive distillation column (Figure 2), enabling the aforementioned reduction in process costs. Recent uses of reactive distillation in chemicals production such as acetic acid and methanol [14], alkyl carbonates [15], butadiene [16], butyl acetate [17], carboxylic acids and ether [18] and carboxylic esters [19] are described in patents.
Schematic representation of the conventional process for the synthesis of methyl acetate. Caption: R01: reactor; S01: mixer; S02: extractive distillation; S03: solvent recovery; S04: methanol recovery (MeOH); S05: extractor; S06: azeotropic column; S07-S09: flash columns; S08: acetic acid recovery; V01: decanter.
Schematic representation of the integrated process for the production of methyl acetate by reactive distillation.
Reactive separation conveniently combines the production and removal of one or more products, enabling improvements in reaction productivity and selectivity. However, despite the benefits of this process, the planning and control of reactive distillation columns are hindered by complex interactions between reaction and separation. Operation conditions resulting in the formation of equilibrium between liquid–liquid–vapor phases, low mass transfer rates between liquid and vapor phases or diffusion inside the catalyst (for heterogeneous reactions) and chemical kinetics with reduced reaction rate need to be avoided or minimized [20]. Additionally, since both separation and reaction take place simultaneously in the same unit, the temperatures and pressures required for the two steps must have similar values [21]. If the overlap of operating conditions is not significant, the use of reactive distillation is not recommended [2].
The trend toward the use of biofuels has resulted from increased attention to topics related to mitigating environmental impacts by reducing the consumption of fossil fuels [22]. In this context, biodiesel is considered the main substitute fuel for diesel oil due to its lower polluting potential and the possibility of being used in diesel engines without the need for significant modifications [23, 24].
Biodiesel (alkyl esters) can be obtained by several reaction routes. The most conventional of them is the transesterification of triglycerides (oils) with short-chain alcohols (Eqs. (1)–(3)), such as methanol and ethanol, with homogeneous alkaline catalysts [25]. However, the raw material needed for this reaction must have reduced levels of acidity and moisture to avoid saponification reactions [26]. Due to the high costs of obtaining feedstocks that meet these specifications and competition with the food industry, studies aimed at the production of biodiesel from residual oils with a high content of fatty acids have been published [27, 28, 29, 30].
One of the alternatives to reduce the typical acidity of residual oils is to carry out a previous fatty acid esterification step [25] (Eq. (4)). However, in this process, with reaction and separation occurring in different units, chemical equilibrium limits the yield because the reaction that forms one of the products, water is reversible [31].
Therefore, the use of a reactive distillation column can be justified and may result in higher fatty acid conversions compared to those achieved in conventional systems. Tables 1 and 2 provide an overview of the literature on esterification and transesterification reactions aimed at producing biodiesel.
Ref. | Feedstock | Catalyst | Temperature | Simulation software | FFA conv. |
---|---|---|---|---|---|
[31]a | Oleic acid | H2SO4 (1–3 FFA wt%) | 130–150°C | — | 79.6% |
[32]a | Dodecanoic acid | Solid acid catalyst (0–5 FFA wt%) | 120–180°C | AspenOne 2004 | ∼95.0% |
[33]a | Dodecanoic acid | Metal oxides (0–10 FFA wt%) | 120–180°C | AspenOne 2004 | ∼72.0% |
[34]a | Non-edible oil mixture | Tin (II) chloride (1–9 oil wt%) | 40–60°C | Aspen Plus V8.8 | 78.3% |
[35]b | Dodecanoic acid | Sulfated Zirconia | 130°C (FFA feed) | Aspen Plus V9 | 99.9% |
[36]a | Fatty acids mixture | Nb2O5 (5 FFA wt%) | 90–170°C | Aspen Plus V7.3 | 96.0% |
[37]b | Fatty acids mixture | Sulfuric acid | 417°C (FFA feed) | Aspen Plus | — |
[38]b | Fatty acids | Solid acid catalysts | 58, 145, 207°C (FFA feed) | Fortran Algorithm | >98.0% |
[39]b | Hydrolyzed soybean oil | Niobium oxide | 207°C (FFA feed) | Fortran Algorithm | >98.0% |
[40]a | Fatty acids | Sulfuric acid (0.33–0.66 wt%) | 50–70°C | Aspen Plus V10 | ∼90.0% |
Reactive distillation studies aimed at the production of biodiesel through esterification.
Experimental tests performed by the authors.
No experimental tests performed by the authors.
Ref. | Feedstock | Catalyst | Temperature | Simulation software | Yield |
---|---|---|---|---|---|
[41]a | Soybean oil | NaOH (0.5–1.5 oil wt%) | 50°C (oil feed) | — | 98.2% |
[42]a | Canola oil | KOH, KOCH3 (0.73–1.83 oil wt%) | 100–160°C (reboiler) | — | 94.9% |
[43]a | Canola oil | KOH | 95–150°C (reboiler) | — | 94.4% |
[44]a | Cooking oil | 12-Tungstophosphoric acid hydrate | 20–30°C (oil feed) | — | 93.8% |
[45]a | Palm oil | KOH (0.5–2 oil wt%) | 85–120°C (reboiler) | — | 92.3% |
[46]b | Triolein | NaOH | 55°C (oil feed) | CHEMCAD/MATLAB | 90.3% |
[47]b | Soybean oil | NaOH | 25–90°C (oil feed) | HYSYS | ∼97.0% |
[48]b | Algal oil | H2SO4 | <150°C (reboiler) | MATLAB | — |
[49]b | Trilinolein | NaOH/Magnesium methoxide | 60–150°C (column) | Aspen Plus | 98.3% |
[50]b | Soybean oil | NaOH/CaO + Al2O3 | 60°C/25°C | Aspen Plus V8.4 | — |
Reactive distillation studies aimed at the production of biodiesel through transesterification.
Experimental tests performed by the authors.
No experimental tests performed by the authors.
In general, the main objective of the evaluated studies on esterification in reactive distillation columns is to increase the yield of the biodiesel production process by shifting the chemical equilibrium of the reaction. However, for transesterification studies, reactive distillation systems are considered mainly due to the possible reduction in the reaction time or in the purification costs of the biodiesel alkyl esters formed. This difference in purpose presumably originates from the necessity of milder transesterification reaction conditions when compared to the esterification route for the attainment of high biodiesel yields.
Mathematical modeling of a reactive distillation column was developed in [51] with considerations described in [52]. In their study, the authors assume the absence of chemical equilibrium in the stages and steady-state operation, with reaction rates being explicitly considered in the model of each stage, Murphree separation efficiency equal to 100% and feeding performed by single-phase streams.
Such methodology, presented in this section, was used in the studies of the references [2, 38, 39,53]. The nomenclature for the terms used in the equations is described in Table 3.
Symbol | Description |
---|---|
Ci,j | Molar concentration |
CLp,i | Molar specific heat in the liquid phase |
Ej | Relationship between vapor and liquid streams |
Fi,j | Feed molar flow |
f eqi,j | Phase equilibrium function |
f mi,j | Mass balance function |
f vlj | Function that correlates liquid and vapor streams |
f rk,j | Reaction rate function |
H Ij | Total enthalpy of the vapor stream |
HjII | Total enthalpy of the liquid stream |
k k,j | Kinetic reaction constant |
nIi,j | Molar flow of vapor |
nIIi,j | Molar flow of liquid |
nc | Number of components |
nr | Number of chemical reactions |
Psati,j | Liquid saturation pressure |
Pj | Pressure |
Qj | Heat added or removed |
R | Universal gas constant |
Rj | Liquid withdrawal fraction |
Tj | Temperature |
Tref | Reference temperature (298.15 K) |
vIIj | Liquid molar volume of pure compound |
xIi,j | Molar fraction in vapor phase |
xIIi,j | Molar fraction in liquid phase |
Zj | Lateral vapor withdrawal fraction |
αi,k | Kinetic order of reaction |
Δhvapi | Molar enthalpy of vaporization |
γvapi,j | Activity coefficient in the liquid phase |
νi,k | Stoichiometric coefficient |
ξ k,j | Reaction rate |
Nomenclature of terms used in mathematical modeling.
(references: i = component, j = column stage, k = reaction).
The generic plate scheme adopted by the authors is represented in Figure 3.
Configuration of each stage j in the reactive distillation column. Source: [
Eq. (5), which represents the mass balance of component i in stage j of the column as a residual function, is given by:
Assuming that the streams that leave the stage are in phase equilibrium, Eq. (6) relates the mole fractions in the liquid and vapor phases:
In this expression, the Poynting correction and the fugacity coefficient of the pure saturated compounds are neglected. In addition, and the vapor phase is considered to be an ideal gas mixture as a consequence of the assumption that the column operates at low pressure, close to atmospheric conditions.
The equation for the rate of reaction k in stage j is represented by Eq. (7), which can be expressed as a residual equation by applying the logarithm function (Eq. (8)).
Assuming that the molar volume of the liquid phase is that of an ideal solution and describing Eq. (8) as a function of the activity coefficients of the components in the liquid phase, Eq. (9) is obtained.
Eq. (10), which describes the energy balance of stage j, is needed to calculate the temperature, which is different at each stage of the reactive distillation column. Positive and negative values of Qj correspond to the heat being supplied to or removed from the column, respectively.
The ratio between the molar flows of vapor and liquid leaving each stage of the column is represented using Eq. (11). This equation is intended to make the condenser and reboiler specifications more flexible by associating the relationship between the liquid and vapor streams that leave the column stages.
When written in the residual form, as in Eq. (12), the equation has the following form:
The values of the Ej parameter for each form of operation of the condenser and reboiler (partial or total) are shown in Table 4.
Reboiler (stage 1) | Condenser (stage N) | ||
---|---|---|---|
Partial | Total | Partial | Total |
Z1 = 0 | Z1 ≠ 0 | ZN = 0 | ZN = 0 |
R1 = 0 | R1 = 0 | RN = 0 | RN ≠ 0 |
E1 ≠ 0 | E1 → ∞ | EN ≠ 0 | EN = 0 |
Characteristics of column heaters (reboiler and condenser).
In the study developed by [2, 36, 37, 48], all cases of simulated reactive distillation column configurations used a partial reboiler and total condenser (E1 ≠ 0 and EN = 0).
Solving the set of equations that describe a reactive distillation column is an arduous task, and rigorous mathematical models aimed at a computer simulation of this type of equipment were not developed until the 1970s [54].
In recent decades, commercial software that has specific models and algorithms for reactive distillation operations has been widely used, as shown previously in Tables 1 and 2. The simulations developed in the subsequent section, referring to case studies applied to biodiesel production and co-product valuation, use the RADFRAC module present in the commercial Aspen Plus software, which solves the equations of mass balance, energy balance, phase equilibrium and the sum of molar fractions (MESH) [55] through the “inside-out” algorithm [54].
The kinetic parameters for the esterification of fatty acids (FFA) present in corn distillers oil from DDGS (dried distillers grains with solubles) were estimated by a model fitting of the FFA conversion data (Table 5) obtained by our group. The reaction (Eq. (13)) was carried out at the temperatures of 150, 175 and 200°C, with ethanol and NbOPO4 (catalyst), following a molar alcohol:FFA ratio of 10:1 and catalyst load of 10% (FFA mass).
Temperature (°C) | Time (min) | FFA conversion (%) |
---|---|---|
150 | 15 | 1.68 |
30 | 3.99 | |
60 | 5.25 | |
120 | 9.68 | |
180 | 12.23 | |
240 | 19.44 | |
360 | 28.39 | |
175 | 15 | 10.13 |
30 | 15.36 | |
60 | 19.36 | |
120 | 24.21 | |
180 | 34.74 | |
240 | 43.06 | |
360 | 37.88 | |
200 | 15 | 9.46 |
30 | 17.97 | |
60 | 29.41 | |
120 | 38.35 | |
180 | 45.89 | |
240 | 48.53 | |
360 | 49.55 |
FFA conversion of the esterification reaction kinetic tests.
The methodology applied aims to estimate the pre-exponential factor (k0) and the activation energy (Ea) of the reaction. To fit the kinetic parameters, the objective function to be minimized is the squared difference between the experimental values of the FFA conversion and those calculated with a reversible pseudo-homogeneous model (Eq. (14)). The reaction rate (rF) equation was applied to Eq. (15), which describes a batch reactor in terms of the FFA conversion (x) in a given time (t).
A Nelder–Mead [56] simplex algorithm and a 4th order Runge–Kutta [57] method were used to perform the objective function minimization and conversion equation integration steps, respectively. The reaction rate (rF) was obtained through the model and the specific reaction rate constants k (L/mol.s) were expressed by the Arrhenius equation (Eq. (16)). Through this methodology, the parameters Ea and k0 for the reaction rate constants of the forward and reverse esterification reactions were estimated.
In these equations,
T = Absolute temperature at which the kinetic test was performed (K).
R = Universal gas constant (J/K.mol).
F = Fatty Acids (FFA).
A = Alcohol (ethanol).
E = Ethyl Esters (FAEE).
W = Water.
Cn = Concentration of compound n (mol/L).
t = time (s).
The compounds defined for the simulation of the fatty acid esterification with ethanol were specified in the Aspen Plus V.12 process simulator, with the fatty acid and oil fraction represented by oleic acid and triolein, respectively. A similar approach was used by other researchers as a simplification of the numerous components of the acid and oil fraction of the feedstock [58, 59, 60]. The NRTL thermodynamic model [61] was selected to evaluate the activity coefficients of the components of the reaction mixture and the NRTL binary interaction parameters missing from the simulator database were estimated directly through the Aspen Plus estimation tool that uses the UNIFAC model [62].
The flowsheet developed for the process simulation is shown in Figure 4 and consists of two columns, the first being responsible for the reactive distillation of the reactants fed to the process (C-EST) and the second for removing approximately 95% of the ethyl esters (FAEE) produced (C-DIST).
Flowsheet of the FFA esterification process (F = feed, P = product, S = intermediate stream, H = heat exchanger, B = pump, V = valve, C = column).
The simulated reactive distillation column has 22 total stages, of which 14 compose the reactive zone (5 to 18), while the C-DIST column consists of 10 total stages. The columns operating parameters are presented in Tables 6 and 7. Both the distillation columns have kettle-type reboilers, however the C-EST column is equipped with a total condenser, while the C-DIST with a partial condenser to separate the ethyl esters from the remaining oil and excess ethanol. It is noteworthy that the liquid phase composition and temperature profile graphs, as well as the conversions obtained follow the data referring to the process after the optimization described later.
Parameters | Before optimization |
---|---|
Stages | 22 |
Oil feed stage | 5 |
Ethanol feed stage | 18 |
Absolute pressure (bar) | 4 |
Distillate: feed molar ratio | 0.62 |
Reflux molar ratio | 0.08 |
Reactive distillation column operating parameters (C-EST).
Parameters | Distillation column |
---|---|
Stages | 10 |
Feed stage | 4 |
Absolute pressure (bar) | 0.03 |
Distillate: feed molar ratio | 0.51 |
Reflux molar ratio | 1.5 |
Distillation column operating parameters (C-DIST).
The H-1 and H-2 heat exchangers are responsible for heating the oil (F-OIL) and ethanol (F-ETOH) streams up to 200°C and 50°C, respectively, shown in Table 8, while the pumps B-1 and B-2 increase the pressure of the feed streams from 1 bar to 10 bar.
Stream | F-OIL | F-ETOH |
---|---|---|
Temperature (°C) | 20.00 | 20.00 |
Absolute pressure (bar) | 1.00 | 1.00 |
Enthalpy (kW) | −4058.33 | −2461.46 |
Mass Flow (kg/h) | ||
Oleic acid (FFA) | 900 | — |
Ethanol | — | 1467.86 |
Triolein | 5100 | — |
Ethyl oleate (FAEE) | — | — |
Water | — | — |
Properties of the oil feed and ethanol streams (F-OIL and F-ETOH).
The optimization of the process parameters of the esterification reactive distillation column was performed using the MATLAB® R2020b software by implementing the MEIGO package (Metaheuristics for Bioinformatics Global Optimization) [63], an optimization supplement for global optimal search which can be used to optimize industrial processes [64, 65]. The results obtained through Aspen Plus simulations were provided to MATLAB, where the optimization algorithm was performed, and new obtained values of the variables evaluated were used to carry out new simulations iteratively.
The Particle Swarm Optimization (PSO) method was applied to minimize the objective function that describes the conversion of fatty acids (Eq. (17)), starting from an initial population of 50 particles (solution vectors) defined by the algorithm in the pre-defined search intervals.
For the simulation, the varied parameters were molar reflux ratio, internal pressure, molar ratio between distillate stream and total feed, and oil and ethanol feed stages. As restrictions, the reboiler temperature, the recovery of the desired product (ethyl esters) at the bottom of the column and the feed stages of the reagents were evaluated with Eqs. (18)–(20). The reboiler temperature upper limit was defined as 200°C to avoid degradation of the reagents or products and excessive use of the hot utility. It is observed that the minimization of the negative value of the conversion corresponds to the maximization of its positive value.
In these equations,
The kinetic constants obtained through the discussed methodology are presented in Table 9, with the direct reaction of ethyl esters formation indicated by the subscript “1” and the reverse reaction of fatty acids formation indicated by the subscript “2”. Figure 5 shows the comparison between the experimental and calculated conversions, along with the R2 coefficient of the fit for each temperature.
Parameter | Value |
---|---|
K0,1(L/mol.s) | 252.786 |
K0,2(L/mol.s) | 207.093 |
Ea1 (J/mol) | 51,357.1 |
Ea2 (J/mol) | 39,244.1 |
Estimated kinetic constants for the esterification reaction.
Experimental (−) and calculated FFA conversion at 150°C (∆), 175°C (
Observing the results presented, it is noted that the data fitting at 200°C presented a high coefficient of determination, while the data fitting at 175°C obtained a reduced R2. However, as the temperature in the reactive section of the esterification column is, on average, close to 195°C, it was concluded that due to the excellent results achieved in the data fitting at 200°C, the use of the estimated kinetic parameters would not hinder the development of a simulation faithful to the real behavior of the reaction.
The composition and temperature profiles along the stages of the reactive distillation column (column C-EST in Figure 4) are presented in Figures 6 and 7.
Liquid phase mass composition profile (C-EST).
Column temperature profile (C-EST).
The liquid phase composition profile of the C-EST column (Figure 6) indicates that the major component for all stages with values higher than 5 (closer to the bottom) is triolein, while in the others there is a predominance of oleic acid, ethyl oleate (FAEE), ethanol and water, since only negligible amounts of triolein are evaporated along the column, as seen in the vapor phase mass composition profile. Additionally, there is a significant increase in the fraction of ethanol and water in the liquid state in the first stage due to the use of a total condenser in the reactive distillation column.
The composition of the streams that characterize the main products of the process (S-FAEE, P-OIL and P-FAEE) are presented in Table 10 and, based on the simulation results, there is a final fatty acids conversion (mol) of 83.97% inside the column, 94.00% of which is recovered in the P-FAEE stream, while 5.83% is recovered in the P-OIL stream. The remaining 0.17% of FAEE is located at the P-ETOH2 stream. The resulting stream of the desired product (P-FAEE) has a purity (FAEE) greater than 98%, resulting in an ester content superior to the value described in Brazilian and European specifications [66, 67].
Stream | S-FAEE | P-FAEE | P-OIL |
---|---|---|---|
Temperature (°C) | 162.39 | 90.00 | 305.08 |
Absolute pressure (bar) | 8.13 | 0.03 | 0.03 |
Enthalpy (kW) | −3813.85 | −506.62 | −2532.30 |
Mass Flow (kg/h) | |||
Oleic acid (FFA) | 144.22 | 11.80 | 132.41 |
Ethanol | 186.96 | 0.58 | — |
Triolein | 5100.00 | — | 5100.00 |
Ethyl oleate (FAEE) | 830.34 | 780.97 | 48.43 |
Water | 0.58 | — | — |
Composition of the main product streams.
In Table 10, it is possible to observe that there are still traces of ethyl esters present in the oil stream. However, this amount corresponds to less than 1% of the total mass fraction of the stream. P-OIL, therefore, was considered to be non-significant. Furthermore, of the 900 kg/h of FFA fed to the process, only 132.41 kg/h remain, characterizing a reduction of 85.29% of the total fatty acid mass. Finally, the energy demands for H-1, H-2, condensers and reboilers of columns C-EST and C-DIST are presented in Table 11.
Equipment | Energy demand (kW) |
---|---|
H-1 | 619.71 |
H-2 | 31.60 |
C-EST (condenser) | −295.57 |
C-EST (reboiler) | 330.86 |
C-DIST (condenser) | −581.94 |
C-DIST (reboiler) | 1095.36 |
Energy demand of the process equipment.
Table 12 shows the limits and initial estimates for the variables evaluated for the optimization of the esterification process. Table 13 displays the constraints imposed on the reboiler temperature, ethyl ester recovery fraction (FAEE), and conversion. The values chosen as initial estimates were obtained by manually setting different values for the reflux molar ratio, condenser pressure, and distillate feed molar ratio, and adopting the best result obtained.
Variable | Molar reflux ratio | Condenser pressure (bar) | Distilled molar ratio: feed | Oil feed stage | Ethanol feed stage |
---|---|---|---|---|---|
Lower Limit | 0.005 | 0.01 | 0.50 | 5 | 5 |
Upper Limit | 2 | 10 | 0.70 | 18 | 18 |
Initial Estimate | 0.08 | 4 | 0.62 | 5 | 18 |
Lower, upper limits and initial estimates for the variables evaluated in the esterification reaction optimization process.
Restrictions | Reboiler temperature (°C) | Recovery fraction of FAEE | Conversion (%) |
---|---|---|---|
Lower Limit | −273.15 | 0.99 | 0 |
Upper Limit | 200 | 1.00 | 100 |
Initial constraints for the response variables for the variables evaluated in the esterification reaction optimization process.
The results obtained are shown in Figure 8, with a maximum conversion of 83.97% and the final values of the variables are added to Table 14.
Evolution of the FFA conversion as a function of the number of optimization iterations.
Variable | Molar reflux ratio | Condenser pressure (bar) | Distilled molar ratio: feed | Oil feed stage | Ethanol feed stage |
---|---|---|---|---|---|
Result | 0.1130 | 8.1314 | 0.6806 | 5 | 18 |
Response vector of input variables for the esterification reaction optimization process.
An additional simulation performed in a CSTR reactor achieved an FFA conversion of 51.06%, while the maximum average conversion in the kinetic tests (200°C) was 49.55%. The simulated CSTR operated at a constant temperature of 200°C with the residence time of 3 h (same duration of the experimental tests) and was fed with streams following equal mass flows and compositions to the RDC column feed streams. Thus, the optimization results represent a significant improvement of 64.45% and 69.46% compared to the CSTR and experimental tests, respectively, inferring that the use of a reactive distillation column could be beneficial to the process.
As biodiesel production increases so do the production of glycerol as for each liter of biodiesel produced, approximately 100 mL of crude glycerol are obtained [68]. Among the transformation processes for glycerol to viable chemical intermediates, glycerol ketalization for the production of solketal has gained prominence. Solketal can be used as an additive to increase the octane and fluid dynamic properties of the fuel. The addition of up to 5% by volume of solketal to gasoline leads to a significant decrease in gum formation [69]. With this motivation, this study aims to simulate the operation of a reactive distillation column for the production of solketal from glycerol with acetone using heterogeneous catalysis, with high conversion of reagents and separation of the components of the reaction.
The applied methodology considers the ketalization reaction of glycerol (G) with acetone (A), forming solketal (S) and water (W). The reaction is considered reversible and elementary, being described by Eq. (21):
A pseudo-homogeneous model was used to describe the reaction kinetics through a system of differential equations of concentration over time, at different temperatures, in which the kinetic constants of the direct and inverse reaction are represented, respectively, by k1 and k−1(L/mol.s), while the molar concentrations (mol/L) of the species involved are given by CG, CA, CS and CW (Eq. (22)).
The solution of the system of differential equations using a 4th order Runge Kutta method [57] and the fitting of the kinetic parameters, k1 and k−1, and subsequent estimation of the Arrhenius equation parameters were performed by a Nelder–Mead simplex algorithm [56]. The experimental data used was retrieved from the study of [70].
The kinetic parameters evaluated were later used to predict the solketal formation reaction in a reactive distillation column, using the rigorous RADFRAC distillation model of the Aspen Plus commercial simulation software. The system considered in this study is shown in Figure 9.
Flowsheet of the solketal production process used in this study.
Using the estimated kinetic parameters, the glycerol ketalization reaction for the production of solketal was modeled in the Aspen Plus software. For the process simulation, the pressure inside the column was set at 10 atm. The feeding of the 13-stage column, RDC in Figure 9, are streams GLI-02 and ACE-02, originated from the heating of the currents GLI-01 and ACE-01 up to 95°C and 55°C, by the heat exchangers H1 and H2, respectively.
The ACE-01 stream is composed only of acetone, while GLI-01 contains 80% glycerol and 20% water by mass, disregarding other components such as methanol or dissolved salts normally present in glycerol from biodiesel production processes [71]. The products of reactive distillation are characterized by TOP-P and BOT-P streams, which correspond, respectively, to the streams rich in the most volatile and least volatile substances in the process.
Figure 10 shows the concentrations as a function of time according to the fitted kinetic parameters data.
Experimental and calculated concentrations (80°C).
Analyzing Figure 10, it is observed that the curves generated using the fitted parameters represented the experimental data satisfactorily. Table 15 presents the process specifications obtained after a sensitivity analysis, aiming to simulate a column with optimal operating conditions. Figure 11 shows the composition profile in the liquid phase as a function of the column stage number (1 = condenser and 13 = reboiler).
Parameter | Description |
---|---|
Number of stages | 13 |
Condenser type | Total |
Reboiler type | Kettle |
Molar reflux ratio | 0.69 |
Reboiler/condenser heat duty | 55.000 / -43.723 cal/s |
Column pressure | 10 bar |
Glycerol feed | 3rd stage |
Acetone feed | 11th stage |
Feed properties | 95 and 55°C - 1 bar |
Glycerol feed molar flow | 2.500 kmol/h |
Water feed molar flow | 0.625 kmol/h |
Acetone feed molar flow | 15.000 kmol/h |
Ketalization reaction stages | 3 to 11 |
RDC column specifications.
RDC column liquid phase composition profile.
The conversion of glycerol obtained for the operational conditions defined for the simulation was 98.2%, indicating the reaction occurred inside the column.
The SOLKETAL stream in Figure 9 has 99.53% solketal and the WATER stream consists of 99.82% water, on a mass basis. Thus, the simulations show that the methodology employed results in a high purity solketal product stream with solketal conversion superior to 98%. However, additional studies are needed to assess the effect of possible intermediate reactions on the process yield.
In this chapter, a general introduction regarding reactive distillation technology and its application to the biodiesel production process was presented. A literature-based mathematical model to describe reactive distillation columns was discussed, along with experimental and simulation studies developed by the authors of this chapter, using commercial software such as Aspen Plus.
In the case study of biodiesel production through the esterification of a low-cost feedstock, the application of an optimized reactive distillation column promoted an improvement of approximately 70% about FFA conversion. The resulting product stream attained purity above 98% in relation to alkyl esters. Additionally, the production of solketal aiming at the valorization of a co-product of the biodiesel production process (glycerol), was studied through the development of a flowsheet in the Aspen Plus simulator, resulting in a solketal stream with purity above 99%.
The results obtained through the developed studies indicate that the reactive distillation technology, applied to fatty acid esterification reactions for the production of biodiesel and ketalization of glycerol for the production of solketal, is promising and attractive in technical terms, however, further studies are necessary to analyze the economic feasibility of both processes.
The authors thank UTFPR and Sinochem Petroleum Brazil Limited (project 001/2019) for financial support.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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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. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of 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:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{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:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{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:"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:"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:"Igor Victorovich Lakhno 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.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in 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 been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), 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 a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). 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: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",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. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"338222",title:"Mrs.",name:"María José",middleName:null,surname:"Lucía Mudas",slug:"maria-jose-lucia-mudas",fullName:"María José Lucía Mudas",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}}]}},subseries:{item:{id:"41",type:"subseries",title:"Water Science",keywords:"Water, Water resources, Freshwater, Hydrological processes, Utilization, Protection",scope:"