\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6029",leadTitle:null,fullTitle:"Phenolic Compounds - Natural Sources, Importance and Applications",title:"Phenolic Compounds",subtitle:"Natural Sources, Importance and Applications",reviewType:"peer-reviewed",abstract:"Phenolic compounds as a large class of metabolites found in plants have attracted attention since long time ago due to their properties and the hope that they will show beneficial health effects when taken as dietary supplements. This book presents the state of the art of some of the natural sources of phenolic compounds, for example, medicinal plants, grapes or blue maize, as well as the modern methods of extraction, quantification, and identification, and there is a special section discussing the treatment, removal, and degradation of phenols, an important issue in those phenols derived from the pharmaceutical or petrochemical industries.",isbn:"978-953-51-2958-5",printIsbn:"978-953-51-2957-8",pdfIsbn:"978-953-51-5089-3",doi:"10.5772/67213",price:139,priceEur:155,priceUsd:179,slug:"phenolic-compounds-natural-sources-importance-and-applications",numberOfPages:456,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"348060e7a0fee08ee1feb9d82b330ec3",bookSignature:"Marcos Soto-Hernandez, Mariana Palma-Tenango and Maria del Rosario Garcia-Mateos",publishedDate:"March 15th 2017",coverURL:"https://cdn.intechopen.com/books/images_new/6029.jpg",numberOfDownloads:55501,numberOfWosCitations:252,numberOfCrossrefCitations:171,numberOfCrossrefCitationsByBook:9,numberOfDimensionsCitations:381,numberOfDimensionsCitationsByBook:15,hasAltmetrics:1,numberOfTotalCitations:804,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 1st 2016",dateEndSecondStepPublish:"June 22nd 2016",dateEndThirdStepPublish:"September 18th 2016",dateEndFourthStepPublish:"December 17th 2016",dateEndFifthStepPublish:"February 15th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"65790",title:"Prof.",name:"Marcos",middleName:null,surname:"Soto-Hernández",slug:"marcos-soto-hernandez",fullName:"Marcos Soto-Hernández",profilePictureURL:"https://mts.intechopen.com/storage/users/65790/images/system/65790.jpg",biography:"Dr. Marcos Soto Hernández is a pharmacist from the National University of México. He has obtained his PhD from the University of Wales Cardiff UK and he is now a full time Professor at Colegio de Postgraduados where conducts research in phytochemistry and bioactivity natural products. He has established collaboration with research groups in UK, The Netherlands, Spain and others groups in México. He has received several awards locally and abroad. Today his main line of research is the bio-guided isolation of secondary metabolites with importance in medicine and agriculture (the potential of the local aromatic plants are part of his recent research). He has published more than 150 research papers and several book chapters and conference proceedings. He is member of several professional societies (national or international).",institutionString:"Colegio de Postgraduados",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Colegio de Postgraduados",institutionURL:null,country:{name:"Mexico"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"193077",title:"Dr.",name:"Mariana",middleName:null,surname:"Palma-Tenango",slug:"mariana-palma-tenango",fullName:"Mariana Palma-Tenango",profilePictureURL:"https://mts.intechopen.com/storage/users/193077/images/system/193077.jpg",biography:"Dr. Mariana Palma-Tenango is an engineer agronomist from the Universidad Autónoma Chapingo and holds a PhD degree in Plant Physiology from the Colegio de Postgraduados, México. Dr. Tenango has teaching duties in the National Autonomous University of Mexico, and is an assistant professor of Phytochemistry at the Colegio de Postgraduados. She has participated in the organization of meetings and symposiums in México and is a supervisor of master and PhD degree students. Her research line is phytochemistry, medicinal, and aromatic plants.",institutionString:"Colegio de Postgraduados",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Colegio de Postgraduados",institutionURL:null,country:{name:"Mexico"}}},coeditorTwo:{id:"194366",title:"Dr.",name:"Rosario",middleName:null,surname:"García-Mateos",slug:"rosario-garcia-mateos",fullName:"Rosario García-Mateos",profilePictureURL:"https://mts.intechopen.com/storage/users/194366/images/system/194366.jpg",biography:"Dr. María del Rosario García-Mateos is a full Professor cat the Universidad Autónoma Chapingo, in Texcoco, Estado de México, Mexico. and holds a PhD degree from the Colegio de Postgraduados, Mexico. Her research line is related with the nutraceutical properties and biological activity of horticultural crops. She has supervised master and PhD thesis, has published more than 71 papers, and has been a reviewer of several scientific journals.",institutionString:"Universidad Autonoma Chapingo",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"500",title:"Phytochemistry",slug:"organic-chemistry-phytochemistry"}],chapters:[{id:"53295",title:"Chemical Structure of Phenols and Its Consequence for Sorption Processes",doi:"10.5772/66537",slug:"chemical-structure-of-phenols-and-its-consequence-for-sorption-processes",totalDownloads:3222,totalCrossrefCites:18,totalDimensionsCites:32,hasAltmetrics:0,abstract:"Sorption of phenolic compounds is a very complex process and many factors influence it. At the beginning, detailed chemical structure of phenols is presented with its consequence for physical properties, for example, values of melting and boiling points, solubility in water, pKa and Log P. Also influence of activating and deactivating substituents on the properties is explained. On this basis, interaction with the most frequently used sorbents, for example, chemically modified silicas, polymers and porous carbons, is described. Both sorbents characteristics including physical (porosity) and chemical properties (functional groups) and experimental conditions such as concentration of solutes, contact time, temperature, solvent effects and presence or absence of oxygen are taken into account. The explanations of irreversible adsorption and oxidative coupling phenomena are included. The mechanisms of phenolic compounds sorption are described.",signatures:"Magdalena Sobiesiak",downloadPdfUrl:"/chapter/pdf-download/53295",previewPdfUrl:"/chapter/pdf-preview/53295",authors:[{id:"193105",title:"Dr.",name:"Magdalena",surname:"Sobiesiak",slug:"magdalena-sobiesiak",fullName:"Magdalena Sobiesiak"}],corrections:null},{id:"53530",title:"Phenolic Compounds from the Natural Sources and Their Cytotoxicity",doi:"10.5772/66898",slug:"phenolic-compounds-from-the-natural-sources-and-their-cytotoxicity",totalDownloads:4212,totalCrossrefCites:6,totalDimensionsCites:14,hasAltmetrics:1,abstract:"Natural phenolic compounds are considered as one of the important secondary metabolites for their chemopreventive and chemotherapeutic effects in cancer. These compounds show potent activities for cancer prevention and its treatment. There are many phenolic compounds present in medicinal and edible plants such as, flavonoids, bioflavonoids, stilbenes, chalconoids, chromones, phenylpropanoids, curcuminoids, coumarins, tannins, lignans, neolignans, anthraquinones, quinones, xanthones, phenolic acids and their glycosides and many more. The antioxidant potential of phenolic compounds is almost bolded in the treatment and prevention of cancer. Mono phenolic, polyphenolic and phenolic acids compounds from a large variety of plants, foods, spices, insects, fungus, beverages, lichens, algae and mammals have been shown to inhibit or attenuate the initiation, progression and spread of cancers in cells in vitro and in animals in vivo. In this chapter, we try to cover general view and the recent literature to summarize structural information and cytotoxic effects of phenolic compounds on different cancer cell lines from medicinal herbs and plants.",signatures:"Shagufta Perveen and Areej Mohammad Al-Taweel",downloadPdfUrl:"/chapter/pdf-download/53530",previewPdfUrl:"/chapter/pdf-preview/53530",authors:[{id:"192992",title:"Prof.",name:"Shagufta",surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen"},{id:"192994",title:"Dr.",name:"Areej",surname:"Al-Taweel",slug:"areej-al-taweel",fullName:"Areej Al-Taweel"}],corrections:null},{id:"53539",title:"Phenolics in Foods: Extraction, Analysis and Measurements",doi:"10.5772/66889",slug:"phenolics-in-foods-extraction-analysis-and-measurements",totalDownloads:4128,totalCrossrefCites:17,totalDimensionsCites:26,hasAltmetrics:0,abstract:"The increasing consumers demands to acquire healthier fruits and vegetables as well as the urgency in looking to natural compounds with antioxidant activity and enhanced antimicrobial activity against antibiotic-resistant pathogenic bacterial strains have encouraged a quick expansion of research studies about enhanced phenolic extraction and identification methods. Considering the importance of phenolics as natural compounds with antioxidant and antimicrobial activity, this chapter aims to present the most updated information about extraction methods, ranging from the traditional to the most advanced processes, as well as the access to the modern methods used in the identification and quantification of phenolics. The main goal of this chapter is to provide the reader with a broad view on the different protocols used to extract, identify and quantify phenolic compounds from different kinds of foods, including fruits and vegetables.",signatures:"Alfredo Aires",downloadPdfUrl:"/chapter/pdf-download/53539",previewPdfUrl:"/chapter/pdf-preview/53539",authors:[{id:"175895",title:"Dr.",name:"Alfredo",surname:"Aires",slug:"alfredo-aires",fullName:"Alfredo Aires"}],corrections:null},{id:"53529",title:"Synthesis and Characterization of Phenolic Lipids",doi:"10.5772/66891",slug:"synthesis-and-characterization-of-phenolic-lipids",totalDownloads:2476,totalCrossrefCites:4,totalDimensionsCites:12,hasAltmetrics:0,abstract:"Omega-3 polyunsaturated fatty acids (ω3 PUFAs) from fish oils promote well-established health and antiaging benefits that justify their use as functional ingredients in dietary supplements, healthy foods, and nutraceutical products. Dietary supply is needed because human metabolism exhibits limited to synthesize ω3 PUFAs. However, the practical use of such lipids as food ingredients is often limited due to their high susceptibility to oxidation, which is responsible for the undesirable off-flavor and odor of rancid oils, associated with the loss of nutritional value. Produced phenolic lipids were a good solution for all these problems. These phenolic lipids are preferentially produced by enzymatic bioprocesses that exhibit high selectivity toward polyfunctional substrates and mild reaction conditions compared with chemical synthesis pathways. This chapter presents the acylation of phenolic compounds and lipids using enzyme under various operating conditions. In conclusion, the acylation of lipids with natural phenolic compounds resulted in the formation of a lipophilic ester that should be able to stabilize the oil, fats, and emulsions against oxidation. Acylation of lipids to phenolic compounds that have antioxidant properties thus protects the lipids from oxidation, and the phenolic lipid derivatives carry the combined health beneficial properties of lipids and the phenolic molecules.",signatures:"Mohamed Hussein Hamdy Roby",downloadPdfUrl:"/chapter/pdf-download/53529",previewPdfUrl:"/chapter/pdf-preview/53529",authors:[{id:"193349",title:"Dr.",name:"Mohamed",surname:"Roby",slug:"mohamed-roby",fullName:"Mohamed Roby"}],corrections:null},{id:"53528",title:"Anthocyanin Pigments: Importance, Sample Preparation and Extraction",doi:"10.5772/66892",slug:"anthocyanin-pigments-importance-sample-preparation-and-extraction",totalDownloads:7630,totalCrossrefCites:13,totalDimensionsCites:38,hasAltmetrics:1,abstract:"Anthocyanins are naturally occuring pigments belonging to the group of flavonoids, a subclass of the polyphenol family. They are common components of the human diet, as they are present in many foods, fruits and vegetables, especially in berries and red wine. There were more studies conducted on effect of processing and storage on changes and stability of colors of anthocyanins in foods such as fruits and also for their use as natural colorants. Besides, the interest on anthocyanins is still growing also owing to their strong antioxidant activity against many chronic diseases, numerous studies about their medicinal, therapeutical and nutritional value were also conducted. There are pieces of evidence regarding the positive association of their intake with healthy biological effects. They act as antioxidants both in the foodstuffs in which they are found and in the organism that take in foods rich in anthocyanins. Many efforts have been carried out to develop new analytical techniques for identification and quantification of anthocyanins in plant materials, as well as their effects in vivo and in vitro. With this in mind, an overview to general considerations concerning (i) polyphenol and flavonoid history; (ii) chemical structure, color and intake of anthocyanins and (iii) sample preparation and extraction methods are presented in this chapter.",signatures:"Julia Martín, María José Navas, Ana María Jiménez-Moreno and\nAgustín G. Asuero",downloadPdfUrl:"/chapter/pdf-download/53528",previewPdfUrl:"/chapter/pdf-preview/53528",authors:[{id:"190870",title:"Dr.",name:"Agustín G.",surname:"Asuero",slug:"agustin-g.-asuero",fullName:"Agustín G. Asuero"},{id:"190871",title:"Dr.",name:"Julia",surname:"Martín",slug:"julia-martin",fullName:"Julia Martín"},{id:"195470",title:"Dr.",name:"Mª José",surname:"Navas",slug:"ma-jose-navas",fullName:"Mª José Navas"},{id:"195471",title:"Dr.",name:"Ana María",surname:"Jiménez",slug:"ana-maria-jimenez",fullName:"Ana María Jiménez"}],corrections:null},{id:"54109",title:"Flavonoid Phenolics in Red Winemaking",doi:"10.5772/67452",slug:"flavonoid-phenolics-in-red-winemaking",totalDownloads:2686,totalCrossrefCites:5,totalDimensionsCites:9,hasAltmetrics:0,abstract:"This chapter reviews the chemical diversity of flavonoid phenolics in grapes (Vitis vinifera L.) with impact on the sensory properties of red wines. Anthocyanins, flavan-3-ols, tannins, and polymeric pigments are discussed from a chemical, technological, and sensory perspective. Anthocyanins, responsible for the color of red wines, reach a peak of extraction after 4 or 5 days of maceration, followed by a decrease in their concentration as maceration progresses. Flavan-3-ols and oligomeric tannins from skins are responsible for bitterness and extracted within the first days of maceration, whereas extraction of seed-derived tannins requires longer maceration times. Matrix effects, including the presence of anthocyanins, polysaccharides, and other cell-wall components affect the rate of retention of tannins into wine. Polymeric pigments, bearing astringent and bitter properties different from those of intact tannins, are formed from covalent reactions between anthocyanins and tannins, putatively accounting for the changes in mouthfeel and textural properties of red wines during maceration and aging. Different maceration techniques applied during red wine production affect the rate, quantity, and the chemical composition of wine phenolics. Understanding of the factors that modulate phenolic retention into wine should allow the winemaker to adjust maceration variables to meet stylistic and/or commercial specifications.",signatures:"L. Federico Casassa",downloadPdfUrl:"/chapter/pdf-download/54109",previewPdfUrl:"/chapter/pdf-preview/54109",authors:[{id:"193386",title:"Dr.",name:"Federico",surname:"Casassa",slug:"federico-casassa",fullName:"Federico Casassa"}],corrections:null},{id:"54008",title:"The Effect of Production Systems on Strawberry Quality",doi:"10.5772/67233",slug:"the-effect-of-production-systems-on-strawberry-quality",totalDownloads:2034,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Strawberry with high polyphenols is receiving much more attentions recently. Production systems were reported positive effect on improving crop quality. A complete randomized design with two replicates was employed, and the strawberry selection ‘SJ8976-1’ was planted under three production systems: matted row system (MRS), plastic mulch (PM) and plastic mulch with row covers (PMRC). Total yield, average fruit weight, soluble solids content (SSC), titratable acidity (TA), firmness, fruit postharvest quality, total phenolic content (TPC), total antioxidant content (TAC), oxygen radical absorbance capacity (ORAC) and phenolic composition analysed by high-performance liquid chromatography (HPLC) were evaluated at different harvest times. The results showed that PMRC advanced fruit maturity and improved average fruit weight compared to MRS. SSC, TA and firmness of the strawberry fruits from PMRC were significantly increased at all harvests. Fruit weight loss, juice leakage and presences of grey mould were lower, and fruit glossiness was higher for those harvested under PMRC than those from MRS. TPC, TAC and ORAC of strawberry were significantly changed under different production systems. The phenolic compounds assessed in strawberry were grouped into six major categories, which were anthocyanins, kaempferol, flavonols, hydroxycinnamic acids, ellagic acid and benzoic acids.",signatures:"Li Fan, Claudine Dubé and Shahrokh Khanizadeh",downloadPdfUrl:"/chapter/pdf-download/54008",previewPdfUrl:"/chapter/pdf-preview/54008",authors:[{id:"193841",title:"Dr.",name:"Li",surname:"Fan",slug:"li-fan",fullName:"Li Fan"},{id:"200129",title:"Mrs.",name:"Claudine",surname:"Dubé",slug:"claudine-dube",fullName:"Claudine Dubé"},{id:"200132",title:"Dr.",name:"Shahrokh",surname:"Khanizadeh",slug:"shahrokh-khanizadeh",fullName:"Shahrokh Khanizadeh"}],corrections:null},{id:"53825",title:"Phenolic Compounds in Maize Grains and Its Nixtamalized Products",doi:"10.5772/66893",slug:"phenolic-compounds-in-maize-grains-and-its-nixtamalized-products",totalDownloads:2661,totalCrossrefCites:5,totalDimensionsCites:21,hasAltmetrics:0,abstract:"Among the cereals most consumed by humans, maize grain is in the third position, surpassed only by rice and wheat. In several countries, maize grain is the main source of carbohydrates and proteins. Maize grain is ranked as one of the cereals with the highest content of phenolic compounds. The importance for human health of the consumption of phenolic compounds is due to their proved antioxidant activity. Diets with high amount of antioxidants have been associated with a reduced probability of suffering degenerative chronic diseases. In maize grain, the phenolic acids predominate, among which the main is ferulic acid, followed by p‐coumaric acid, which are highly abundant in their bound forms. However, other phenolics such as anthocyanins, flavonols, and flavanols have been identified in colored maize grains. Additionally, the processing of maize grain into different products for human consumption incorporates changes both in quantity and quality of some phenolic compounds. In the present chapter, we present the most recent information available regarding phenolic compounds in maize grain and their nixtamalized products.",signatures:"Yolanda Salinas-Moreno, Carolina García-Salinas, José L. Ramírez-\nDíaz and Ivone Alemán-de la Torre",downloadPdfUrl:"/chapter/pdf-download/53825",previewPdfUrl:"/chapter/pdf-preview/53825",authors:[{id:"193632",title:"Dr.",name:"Yolanda",surname:"Salinas-Moreno",slug:"yolanda-salinas-moreno",fullName:"Yolanda Salinas-Moreno"},{id:"196982",title:"Dr.",name:"Carolina",surname:"García-Salinas",slug:"carolina-garcia-salinas",fullName:"Carolina García-Salinas"},{id:"196983",title:"Dr.",name:"José Luis",surname:"Ramírez-Díaz",slug:"jose-luis-ramirez-diaz",fullName:"José Luis Ramírez-Díaz"},{id:"196984",title:"MSc.",name:"Ivone",surname:"Alemán De La Torre",slug:"ivone-aleman-de-la-torre",fullName:"Ivone Alemán De La Torre"}],corrections:null},{id:"53603",title:"Phenolic Compounds in Genus Smilax (Sarsaparilla)",doi:"10.5772/66896",slug:"phenolic-compounds-in-genus-smilax-sarsaparilla-",totalDownloads:2480,totalCrossrefCites:2,totalDimensionsCites:7,hasAltmetrics:1,abstract:"Smilax (Smilacaceae) is a genus of about 350 species, found in temperate, tropical and subtropical zones worldwide. The plants belonging to this genus are found throughout Asia, Europe, Oceania and the Americas. Species of the genus Smilax commonly called sarsaparilla are characterized as climbers, with long, thin thorny stem. The branches have tendrils which attach to other plants or objects and grow steadily upward. The roots of these plants have been used for centuries in Asia and the Americas as a tonic, diuretic and sudorific. The rhizome, roots, stems and leaves of sarsaparilla are used in traditional medicine. In the scientific literature, there are several reports on immunomodulatory properties, anticonvulsant, antibacterial, antifungal, anticancer, antidiabetic and antioxidant properties. However, there are no reports which explain the antioxidant activity of sarsaparilla extracts as a function of phenolic compound structures, such as flavonoids and phenylpropanoids. In this chapter, the relevance of phenolic chemical structure in antioxidant and anticancer activity of sarsaparilla extracts will be described. Special emphasis is placed on phenylpropanoid glycosides that consist of a sucrose core. These compounds are evidence of chemotaxonomy in the genus Smilax.",signatures:"Salas-Coronado Raúl, Hernández-Carlos Beatriz, Llaguno-Guilberto\nJoseoziel and Santos-Sánchez Norma Francenia",downloadPdfUrl:"/chapter/pdf-download/53603",previewPdfUrl:"/chapter/pdf-preview/53603",authors:[{id:"143354",title:"Dr.",name:"Raúl",surname:"Salas-Coronado",slug:"raul-salas-coronado",fullName:"Raúl Salas-Coronado"},{id:"148546",title:"Dr.",name:"Norma Francenia",surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez"},{id:"193718",title:"Dr.",name:"Beatriz",surname:"Hernández-Carlos",slug:"beatriz-hernandez-carlos",fullName:"Beatriz Hernández-Carlos"},{id:"193719",title:"BSc.",name:"Joseoziel",surname:"LLaguno-Guilberto",slug:"joseoziel-llaguno-guilberto",fullName:"Joseoziel LLaguno-Guilberto"}],corrections:null},{id:"53742",title:"Genotype, Environment and Management Practices on Red/ Dark-Colored Fruits Phenolic Composition and Its Impact on Sensory Attributes and Potential Health Benefits",doi:"10.5772/66881",slug:"genotype-environment-and-management-practices-on-red-dark-colored-fruits-phenolic-composition-and-it",totalDownloads:1934,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Phenolic compounds are secondary metabolites abundant in our diet. These compounds may affect positively or negatively the sensory characteristics of food with important impacts on color, flavor, and astringency. An adequate consumption of phenolic compounds may also offer health benefits. After the consumption of fruits, the colon is the main site of microbial fermentation, where high molecular weight phenolic compounds are transformed into low molecular weight phenolic compounds such as phenolic acids or lactone structures by intestinal microbiota, which produce metabolites with biological and antioxidant activity, with evidence on health benefits for humans. A large amount of different phenolic compounds are responsible for physicochemical and sensory characteristics of table grapes and wines. Also, sweet cherry (Prunus avium L.) is one of the most popular temperate table fruits; they contain flavonoids, flavan‐3‐ols, and flavonols in addition to non‐flavonoid compounds. Anthocyanins are the major polyphenols in blueberries, and this group of phytochemicals is thought to be responsible for many of the health benefits of berry consumption. Therefore, considering the importance of red/dark‐colored fruits phenolic composition, the purpose of this chapter is to make a review of the most recent publications about these fruits’ phenolic composition and their impact on sensorial properties as well as the effect of microorganisms on fruit phenolic composition.",signatures:"Fernanda Cosme, Berta Gonçalves, Eunice A. Bacelar, António Inês,\nAntónio M. Jordão and Alice Vilela",downloadPdfUrl:"/chapter/pdf-download/53742",previewPdfUrl:"/chapter/pdf-preview/53742",authors:[{id:"60559",title:"Prof.",name:"Berta",surname:"Gonçalves",slug:"berta-goncalves",fullName:"Berta Gonçalves"},{id:"60560",title:"Dr.",name:"Eunice",surname:"Bacelar",slug:"eunice-bacelar",fullName:"Eunice Bacelar"},{id:"181011",title:"Dr.",name:"Alice",surname:"Vilela",slug:"alice-vilela",fullName:"Alice Vilela"},{id:"186819",title:"Prof.",name:"Fernanda",surname:"Cosme",slug:"fernanda-cosme",fullName:"Fernanda Cosme"},{id:"186820",title:"Prof.",name:"António",surname:"Inês",slug:"antonio-ines",fullName:"António Inês"},{id:"186821",title:"Prof.",name:"António",surname:"M. Jordão",slug:"antonio-m.-jordao",fullName:"António M. Jordão"}],corrections:null},{id:"53672",title:"Perspective on Co-feeding of Phenolic Compounds into Existing Refinery Units",doi:"10.5772/66883",slug:"perspective-on-co-feeding-of-phenolic-compounds-into-existing-refinery-units",totalDownloads:1865,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Replacement of fossil materials by renewable feedstocks is forced by depletion and environmental concerns but requires new technologies for energy generation or production of chemicals. Co-processing of petroleum with renewable feedstocks in current refinery infrastructure is an attractive option in the mid-term to increase renewable fuel capacity, as the capital investment and operational costs would be marginal. In this chapter, various strategies for admixing of phenolic compounds as renewable feeds into standard refineries are described. Starting from the role of renewable resources (e.g. biomass, lignin and bio-oil) in the current and future energy and chemical community, an overview on the present energy supply situation and the role of phenolic compounds are discussed. Later, a summary on co-feeding of phenolic model compounds with conventional feeds in refineries are illustrated. The co-processing of upgraded bio-oil in refinery units [e.g. fluid catalytic cracking (FCC), hydrotreating] is summarized, showing the potential utilisation of bio-feeds via such processes. Finally, some concluding remarks address the perspectives for further research and development to overcome future challenges.",signatures:"Thuan Minh Huynh, Udo Armbruster and Andreas Martin",downloadPdfUrl:"/chapter/pdf-download/53672",previewPdfUrl:"/chapter/pdf-preview/53672",authors:[{id:"180804",title:"Dr.",name:"Andreas",surname:"Martin",slug:"andreas-martin",fullName:"Andreas Martin"},{id:"193492",title:"Dr.",name:"Thuan Minh",surname:"Huynh",slug:"thuan-minh-huynh",fullName:"Thuan Minh Huynh"},{id:"196964",title:"Dr.",name:"Udo",surname:"Armbruster",slug:"udo-armbruster",fullName:"Udo Armbruster"}],corrections:null},{id:"53731",title:"Modified Byproduct of Coke Phenols as Effective and Prospective Inhibitors for Petrochemical Industry",doi:"10.5772/66886",slug:"modified-byproduct-of-coke-phenols-as-effective-and-prospective-inhibitors-for-petrochemical-industr",totalDownloads:1711,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Experimental author results on the use of technogenic phenols as inhibitors are presented to undesirable polymerization in petrochemical manufactures. The basic ways of increase inhibiting properties of coke-chemical phenols are shown, such as rectification for separation of two-nuclear derivatives or some examples of their chemical modification, allowing to increase the inhibiting efficiency of phenolic mixes, for example, by novolak fenol-formaldehyde condensation or oxidative coupling reactions. The received derivatives of phenols are recommended both for processing pyrocondensates and for rectification of styrene.",signatures:"Alexey Fedorovich Gogotov",downloadPdfUrl:"/chapter/pdf-download/53731",previewPdfUrl:"/chapter/pdf-preview/53731",authors:[{id:"193412",title:"Prof.",name:"Alexey",surname:"Gogotov",slug:"alexey-gogotov",fullName:"Alexey Gogotov"}],corrections:null},{id:"53444",title:"Phenolic Wastewaters: Definition, Sources and Treatment Processes",doi:"10.5772/66366",slug:"phenolic-wastewaters-definition-sources-and-treatment-processes",totalDownloads:2169,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"This chapter aims the state of the art concerning the development of advanced oxidation processes (AOPs) for treatment of organic-aqueous effluent for the reuse of liquid water. It presents the major oxidative processes applied for industrial and domestic treatment, where the effluents are often contaminated by phenolic compounds. A special emphasis is given to a relatively new technique called direct contact thermal treatment (DiCTT) that has the advantages of conventional AOP without its inconveniences. The DiCTT process is characterized by the generation of hydroxyl radicals (•OH) by combustion of natural gas, its compact installation and easy operation, being able to be used in offshore oil-exploration platforms, where natural gas is available and the space is reduced. Also, in this chapter, original results on the treatment of the DiCTT technique are presented, which are considered unconventional, by evaluating the oxidation and the conversion of the total organic carbon (TOC) of phenolic compounds at low temperature and atmospheric pressure, with identification and quantification of the intermediate compounds, using high-performance liquid chromatography (HPLC), which may be more toxic than the original pollutants.",signatures:"Yana Batista Brandão, Julierme Gomes Correia de Oliveira and\nMohand Benachour",downloadPdfUrl:"/chapter/pdf-download/53444",previewPdfUrl:"/chapter/pdf-preview/53444",authors:[{id:"192946",title:"Dr.",name:"Yana",surname:"Brandão",slug:"yana-brandao",fullName:"Yana Brandão"},{id:"194009",title:"Prof.",name:"Mohand",surname:"Benachour",slug:"mohand-benachour",fullName:"Mohand Benachour"}],corrections:null},{id:"53679",title:"Removal of Phenolic Compounds from Water by Adsorption and Photocatalysis",doi:"10.5772/66895",slug:"removal-of-phenolic-compounds-from-water-by-adsorption-and-photocatalysis",totalDownloads:3775,totalCrossrefCites:10,totalDimensionsCites:16,hasAltmetrics:0,abstract:"Phenolic compounds are important industrial wastes, and are classified as hazardous substances contaminating groundwater resources. Therefore, the removal or diminish of these organics compounds in order to reach the permitted levels before discharging becomes a challenging. Several processes have been developed to remove phenolic compounds from waters, including electrochemical oxidation, redox reactions, membrane separation and photocatalytic degradation. Recently, tendency of phenolic compounds removal involves adsorption and photocatalytic process, using synthetic or natural particles, such as carbon materials and clays. Actually, materials in nanometric scale play an important role in the processes previously mention due to their unique chemical and physical properties. In this book chapter, the first part shows the chemical properties of phenolic compounds that play an important role in the removal process. In the second part, different materials in macro, micro and nanosize used as adsorbents or photocatalysts are reviewed. In addition, other removal processes of phenolic compounds as electrochemistry and redox reactions are included. The removal conditions in these process, such as pH, adsorbate and adsorbent concentration are analyzed and discussed. Furthermore, special emphasis is included in micro and nanocarbon materials, used as adsorbents or photocatalyst to remove phenol from water in recently researches.",signatures:"Eduardo Enrique Pérez Ramírez, Miguel de la Luz Asunción,\nVeronica Saucedo Rivalcoba, Ana Laura Martínez Hernández and\nCarlos Velasco Santos",downloadPdfUrl:"/chapter/pdf-download/53679",previewPdfUrl:"/chapter/pdf-preview/53679",authors:[{id:"178665",title:"Dr.",name:"Carlos",surname:"Velasco-Santos",slug:"carlos-velasco-santos",fullName:"Carlos Velasco-Santos"},{id:"178667",title:"Prof.",name:"Ana Laura",surname:"Martínez-Hernandez",slug:"ana-laura-martinez-hernandez",fullName:"Ana Laura Martínez-Hernandez"},{id:"194010",title:"Dr.",name:"Veronica",surname:"Saucedo-Rivalcoba",slug:"veronica-saucedo-rivalcoba",fullName:"Veronica Saucedo-Rivalcoba"},{id:"194017",title:"Dr.",name:"Eduardo E.",surname:"Perez-Ramirez",slug:"eduardo-e.-perez-ramirez",fullName:"Eduardo E. Perez-Ramirez"},{id:"194018",title:"Dr.",name:"Miguel",surname:"De La Luz-Asunción",slug:"miguel-de-la-luz-asuncion",fullName:"Miguel De La Luz-Asunción"}],corrections:null},{id:"53487",title:"Phenolic Compounds Removal in Woodwaste Leachate by a Trickling Biofilter",doi:"10.5772/66755",slug:"phenolic-compounds-removal-in-woodwaste-leachate-by-a-trickling-biofilter",totalDownloads:1564,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Woodwaste produces large volumes of leachate, which often contains high concentrations of phenolic compounds. These compounds necessitate appropriate management. Biological methods are efficient, innovative, and economic. In particular, biofiltration process has various advantages compared to CAS and MBR technologies. Two pilot filters, with and without biological activity, were designed for continuous mechanisms to follow. This study supposes that the three mechanisms of volatilization, sorption, and biodegradation are present, confirm these assumptions, and determine the contribution of each mechanism. Good efficiency was obtained in the biofilter and 97–98.2% of COD and BOD removal were observed, respectively. Excellent performances were achieved and reached 99.9% of initial concentrations removal for all the phenolic compounds.",signatures:"Najat Kamal, Rosa Galvez, Gerardo Buelna and Rino Dubé",downloadPdfUrl:"/chapter/pdf-download/53487",previewPdfUrl:"/chapter/pdf-preview/53487",authors:[{id:"69386",title:"Prof.",name:"Rosa",surname:"Galvez-Cloutier",slug:"rosa-galvez-cloutier",fullName:"Rosa Galvez-Cloutier"},{id:"194007",title:"Ph.D.",name:"Najat",surname:"Kamal",slug:"najat-kamal",fullName:"Najat Kamal"},{id:"194311",title:"Dr.",name:"Gerardo",surname:"Buelna",slug:"gerardo-buelna",fullName:"Gerardo Buelna"},{id:"195942",title:"Mr.",name:"Rino",surname:"Dubé",slug:"rino-dube",fullName:"Rino Dubé"}],corrections:null},{id:"52914",title:"Degradation of Phenolic Compounds Through UV and Visible- Light-Driven Photocatalysis: Technical and Economic Aspects",doi:"10.5772/66134",slug:"degradation-of-phenolic-compounds-through-uv-and-visible-light-driven-photocatalysis-technical-and-e",totalDownloads:3628,totalCrossrefCites:7,totalDimensionsCites:26,hasAltmetrics:0,abstract:"Phenolic compounds are found in surface and groundwater as well as wastewater from several industries. It is necessary to eliminate phenols and phenolic compounds from contaminated water before releasing into water bodies due to their toxicity to human beings. Photocatalytic degradation seems to be a promising technology for the degradation of several phenolic compounds. Complete mineralization of phenol and phenolic compound has been achieved with TiO2-based photocatalysts under both UV and visible-light irradiation. This chapter will evaluate the conventional processes and advanced oxidation processes for the degradation of phenol and phenolic compounds. The process economics and efficiencies of different advanced oxidation processes will also be discussed. The main focus of the chapter is photocatalytic degradation processes under UV and visible light along with a detailed review of several factors affecting degradation of phenol and phenolic compounds. Photocatalytic degradation process is governed by reactions with hydroxyl radical or superoxide ion. The extent of degradation depends on light sources (UV, visible, and solar), the type of photocatalyst, and experimental conditions (pH, photocatalyst dosage, initial concentration of phenolic compounds, light intensity, electron donor concentration, etc.).Visible-light-active photocatalysts are applied by several researchers to exploit sunlight and to make the photocatalysis process sustainable. In the future, using sunlight in place of UV could make photocatalysis economically more efficient.",signatures:"Pankaj Chowdhury, Sharmistha Nag and Ajay K. Ray",downloadPdfUrl:"/chapter/pdf-download/52914",previewPdfUrl:"/chapter/pdf-preview/52914",authors:[{id:"194058",title:"Dr.",name:"Pankaj",surname:"Chowdhury",slug:"pankaj-chowdhury",fullName:"Pankaj Chowdhury"},{id:"194102",title:"Prof.",name:"Sharmistha",surname:"Nag",slug:"sharmistha-nag",fullName:"Sharmistha Nag"},{id:"195745",title:"Prof.",name:"Ajay",surname:"Ray",slug:"ajay-ray",fullName:"Ajay Ray"}],corrections:null},{id:"53973",title:"Phenolic Compounds in Water: Sources, Reactivity, Toxicity and Treatment Methods",doi:"10.5772/66927",slug:"phenolic-compounds-in-water-sources-reactivity-toxicity-and-treatment-methods",totalDownloads:7331,totalCrossrefCites:78,totalDimensionsCites:170,hasAltmetrics:1,abstract:"Phenolic compounds exist in water bodies due to the discharge of polluted wastewater from industrial, agricultural and domestic activities into water bodies. They also occur as a result of natural phenomena. These compounds are known to be toxic and inflict both severe and long‐lasting effects on both humans and animals. They act as carcinogens and cause damage to the red blood cells and the liver, even at low concentrations. Interaction of these compounds with microorganisms, inorganic and other organic compounds in water can produce substituted compounds or other moieties, which may be as toxic as the original phenolic compounds. This chapter dwells on the sources and reactivity of phenolic compounds in water, their toxic effects on humans, and methods of their removal from water. Specific emphasis is placed on the techniques of their removal from water with attention on both conventional and advanced methods. Among these methods are ozonation, adsorption, extraction, photocatalytic degradation, biological, electro‐Fenton, adsorption and ion exchange and membrane‐based separation.",signatures:"William W. Anku, Messai A. Mamo and Penny P. Govender",downloadPdfUrl:"/chapter/pdf-download/53973",previewPdfUrl:"/chapter/pdf-preview/53973",authors:[{id:"195237",title:"Dr.",name:"Messai",surname:"Mamo",slug:"messai-mamo",fullName:"Messai Mamo"},{id:"196465",title:"Dr.",name:"William Wilson",surname:"Anku",slug:"william-wilson-anku",fullName:"William Wilson Anku"},{id:"196466",title:"Dr.",name:"Penny",surname:"Govender",slug:"penny-govender",fullName:"Penny Govender"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5609",title:"Phenolic Compounds",subtitle:"Biological Activity",isOpenForSubmission:!1,hash:"008b92507ee2f757322ec4565e631bb5",slug:"phenolic-compounds-biological-activity",bookSignature:"Marcos Soto-Hernandez, Mariana Palma-Tenango and Maria del Rosario Garcia-Mateos",coverURL:"https://cdn.intechopen.com/books/images_new/5609.jpg",editedByType:"Edited by",editors:[{id:"65790",title:"Prof.",name:"Marcos",surname:"Soto-Hernández",slug:"marcos-soto-hernandez",fullName:"Marcos Soto-Hernández"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7688",title:"Plant Physiological Aspects of Phenolic Compounds",subtitle:null,isOpenForSubmission:!1,hash:"16f7407afbf69173f4fa419b4338a6c8",slug:"plant-physiological-aspects-of-phenolic-compounds",bookSignature:"Marcos Soto-Hernández, Rosario García-Mateos and Mariana Palma-Tenango",coverURL:"https://cdn.intechopen.com/books/images_new/7688.jpg",editedByType:"Edited by",editors:[{id:"65790",title:"Prof.",name:"Marcos",surname:"Soto-Hernández",slug:"marcos-soto-hernandez",fullName:"Marcos Soto-Hernández"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"11332",title:"Essential Oils",subtitle:"Advances in Extractions and Biological Applications",isOpenForSubmission:!1,hash:"742e6cae3a35686f975edc8d7f9afa94",slug:"essential-oils-advances-in-extractions-and-biological-applications",bookSignature:"Mozaniel Santana de Oliveira and Eloisa Helena de Aguiar Andrade",coverURL:"https://cdn.intechopen.com/books/images_new/11332.jpg",editedByType:"Edited by",editors:[{id:"195290",title:"Ph.D.",name:"Mozaniel",surname:"Santana De Oliveira",slug:"mozaniel-santana-de-oliveira",fullName:"Mozaniel Santana De Oliveira"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic 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Salih"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],ofsBooks:[]},correction:{item:{id:"79356",slug:"corrigendum-pregnancy-and-graves-disease",title:"Corrigendum: Pregnancy and Graves’ Disease",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/79786.pdf",downloadPdfUrl:"/chapter/pdf-download/79786",previewPdfUrl:"/chapter/pdf-preview/79786",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/79786",risUrl:"/chapter/ris/79786",chapter:{id:"75306",slug:"pregnancy-in-women-with-graves-disease-focus-on-fetal-surveillance",signatures:"Anca Maria Panaitescu",dateSubmitted:"December 16th 2020",dateReviewed:"January 27th 2021",datePrePublished:"February 18th 2021",datePublished:"December 1st 2021",book:{id:"10312",title:"Graves' Disease",subtitle:null,fullTitle:"Graves' Disease",slug:"graves-disease",publishedDate:"December 1st 2021",bookSignature:"Robert Gensure",coverURL:"https://cdn.intechopen.com/books/images_new/10312.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"16515",title:"Dr.",name:"Robert",middleName:null,surname:"Gensure",slug:"robert-gensure",fullName:"Robert Gensure"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"251062",title:"Ph.D.",name:"Anca",middleName:null,surname:"Panaitescu",fullName:"Anca Panaitescu",slug:"anca-panaitescu",email:"panaitescu.anca@yahoo.com",position:null,institution:null}]}},chapter:{id:"75306",slug:"pregnancy-in-women-with-graves-disease-focus-on-fetal-surveillance",signatures:"Anca Maria Panaitescu",dateSubmitted:"December 16th 2020",dateReviewed:"January 27th 2021",datePrePublished:"February 18th 2021",datePublished:"December 1st 2021",book:{id:"10312",title:"Graves' Disease",subtitle:null,fullTitle:"Graves' Disease",slug:"graves-disease",publishedDate:"December 1st 2021",bookSignature:"Robert Gensure",coverURL:"https://cdn.intechopen.com/books/images_new/10312.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"16515",title:"Dr.",name:"Robert",middleName:null,surname:"Gensure",slug:"robert-gensure",fullName:"Robert Gensure"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"251062",title:"Ph.D.",name:"Anca",middleName:null,surname:"Panaitescu",fullName:"Anca Panaitescu",slug:"anca-panaitescu",email:"panaitescu.anca@yahoo.com",position:null,institution:null}]},book:{id:"10312",title:"Graves' Disease",subtitle:null,fullTitle:"Graves' Disease",slug:"graves-disease",publishedDate:"December 1st 2021",bookSignature:"Robert Gensure",coverURL:"https://cdn.intechopen.com/books/images_new/10312.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"16515",title:"Dr.",name:"Robert",middleName:null,surname:"Gensure",slug:"robert-gensure",fullName:"Robert Gensure"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11433",leadTitle:null,title:"Human Migration in the Last Three Centuries",subtitle:null,reviewType:"peer-reviewed",abstract:"\r\n\tIn March 2022, another book on human migration seems important when the events or tragedies unfolding in Eastern Europe are considered. People have always migrated and have moved, but, specifically looking at the last three hundred years, involuntary migration is on the rise. Involuntary migration does not only affect Europe; Asia, Africa, and North as well as South America, have had their fair share of natural catastrophes, invasions, and wars.
\r\n\tThis book will intend to look at different migrant patterns, voluntary and involuntary migration, over the last three centuries. What influenced people to leave their home countries, family, and friends and settle somewhere else? The book may include histories of the 19th century, consider tragedies and movements activated by political events in the 20th century, and/or look at recent events of the 21st century. Push and pull factors are important points. While most of us may be influenced in a negative way by the current happenings in Eastern Europe, the Russian invasion and resulting tragedies also demonstrate some very positive human traits – the preparedness of Ukraine’s surrounding countries to help those in need and to provide a safe place for the present.
\r\n\tWhether one looks at voluntary or involuntary migration into any country, after a period of adjustment, migrants do play a positive role. The research found that migrants contribute to the economy (food, shelter, employment, tax) and enrich a country’s cultural norms. Prerequisites for successful settlements are that the host society adopts a tolerant approach and that the migrants recognize the law and the language of the host country. Nothing is ever easy or without controversy, but I am a migrant (German Australian), and life in Australia has been relatively harmonious. Issues that could be considered in the book are multicultural societies (do monocultural societies still exist?) and theories of acculturation versus integration (settlement processes).
\r\n\tTwo further issues are very important in relation to human migration. There is climate change, global warming, and the environment, which clearly affect people’s movement. Small island populations are very concerned about rising sea levels. 2021 has also seen floods costing human lives: Turkey (August 2021), Brazil (December 2021), Chile (January 2021), and South India (November 2021), to name but a few. In Australia (March 2022), farms and whole townships in New South Wales and Queensland have been flooded for the second time in five years, and plans to resettle these towns are considered. Official and social media provide ample coverage of the events, which leads me to the next issue. There is today’s very important role of the media, of the official and social media. We are constantly bombarded with images of human war tragedies and flood victims. People in industrialized, western countries must be the best-informed populace. How far do the images and up-to-date TV news influence us, make us change our behavior, and perhaps even consider us more generous than we have been?
\r\n\tClimate change and the media are relatively new to the human migration debate, but both issues play important parts, and some interesting discussions are appreciated.
\r\n\t
Recently, intense activities have been devoted to characterize rare–earth–doped double–clad fiber laser amplifiers. Owing to their compactness, superb beam quality, great thermal control, and high efficiency, they demonstrate to be important light sources in medicine, modern telecommunication [1], and industries [2, 3]. Progress has been made in developing a fiber–based source in which the mean power scales up to several kilowatts.
The purpose of the fiber amplifier is to intensify a less powerful optical beam, which propagates either inside another fiber or in free space. In this way, the amplifier is “seeded” with a low–power laser beam. As it is known from the main principles of laser physics, the laser oscillator is a device that contains an optical amplifier and puts inside a resonator to provide positive feedback.
The fiber amplifiers are fundamentally divided into core–pumped and cladding–pumped [4] according to the environment of the pump propagation. In the former classification, the amplifying light propagates in the same volume where laser gain media is located, while in the latter, the pump is coupled and propagates outside the core of the doped fiber. The same classification is valid for fiber laser oscillators. Cladding–pumped amplification technique is a hot topic in laser science and technology for high–power regimes, which is the focus of this chapter.
After the invention of the dual–clad fibers, the output powers of the doped fiber lasers have been lifted by several orders of magnitude, and immense activities have been devoted to relevant topics. These systems demonstrate several inherent features including [5] non–uniformly distributed population inversion due to end pumping as well as very high gain even in single–pass amplification by a large ratio of gain length to cross–sectional area.
In addition, both neodymium (Nd) and ytterbium (Yb) are suitable doping elements for high–power regime. Despite the fact that Nd has a relatively low laser threshold, Yb–doped double–clad fiber amplifiers present the prospects for a number of interesting applications due to the broad gain bandwidth with excellent beam quality, renowned as potential sources accessible to high–pump absorption, leading to ultimate efficiency [5, 6]. Furthermore, the benefits from efficient performance, small quantum defects, and superb spectroscopic characteristics as well as free competition processes are competitive with other rare–earth dopants. Particularly, the capability of generating high powers makes them very attractive wherein the output power up to 10/50 kW at CW single–mode/multi–mode schemes has been recently reported.
Besides, a fiber amplifier is characterized by a couple of significant coefficients, i.e., the small signal gain and the saturation quantity. In this chapter, a theoretical consideration of the CW and pulsed fiber arrays is reviewed, followed by the derived main formulas that are important for practical design. In Section 2, a brief description has been devoted to various types of laser amplifiers, operating based on the stimulated emission and optical nonlinearities. An extensive analysis is given in Section 3, regarding the set of coupled propagation rate equations accompanied with the effect of amplified spontaneous emission (ASE) parasitic noises for various pumping modes based on the atomic energy structure of dopants. The serious spectral line broadening mechanisms of gain media is described in Section 4. Eventually, the main issue of relevant amplification relation has been investigated in order to find the corresponding gain saturation parameters.
Different kinds of laser amplifiers operate based on the stimulated emission amplification principle, or established upon optical nonlinearities, e.g., Raman or Brillouin amplifiers as well as optical parametric amplifiers (OPAs).
Raman–based fiber laser amplifier is one approach that has attracted several groups [7–13]. Here, the governed amplification process is the stimulated Raman scattering (SRS) in the fiber which causes an energy transfer from the pump to the signal. In fact, the vibrational spectrum of core material defines the Raman shift. If the wavelength of laser beam signal is known, then the optimum wavelength for pump signal can be calculated.
Commonly, Raman fiber lasers and amplifiers are not considered as ways for generating high–power narrow–linewidth lasers. For high–efficient operations, typically hundreds of meters of fiber are necessary to provide enough gain.
On the contrary, the optical gain is obtained by stimulated Brillouin scattering (SBS) in the Brillouin fiber amplifiers. It is also pumped optically, and a part of the pump power is transmitted to the signal through SBS. Physically, each pump photon creates a signal photon, and the remaining energy is used to excite an acoustic phonon [14]. Classically, the pump beam gets scattered from an acoustic wave moving through the medium at the speed of sound.
Moreover, the fiber–based OPA is a well–known technique offering a wide gain bandwidth using only a few hundred meters of fiber [15]. This phenomenon is strictly operated based on four–wave mixing (FWM) and phase–matching nonlinearities [16] in which a couple of interacting photons over two wavelength bands, also called pump and idler, usually travel collinearly through a nonlinear optical crystal, creating signal and idler output photons. During this phenomenon, pump light beam becomes weaker and amplifies the idler wave.
For birefringent nonlinear crystals, the collinear incident beams may non–collinearly scatter outside the crystal. Non–collinear OPAs were developed to have additional degree of freedom for central wavelength selection, allowing constant gain up to second order in wavelength.
All rare–earth–doped fiber amplifiers intensify the seed signal light through stimulated emission. The local rate equations describe the dynamics of the emission and absorption processes of doping ions within its host material by making use of its atomic energy structure as well as spectroscopic properties [17]. The relation between the emission and absorption cross sections for two energy–level gain media is determined using McCumber theory, which satisfies the following formula [18]:
where
where
Here,
The first transition between two Yb manifolds is the absorption and emission of the pump. Afterward, the spontaneous decay from the 2F5/2 manifold, as well as the absorption, and stimulated emission of the signal simultaneously appear.
Energy–level diagram of Yb:silica glass
Absorption and stimulated emission cross sections of Yb–doped silica glass optical fibers are shown in the spectroscopic diagram in Figure 2. The absorption or fluorescence peak at 975 nm (A) represents the zero–line transition between the lowest energy levels of the ground state (
Typical absorption and emission spectra of Yb3+ ions in germanosilicate host [
In addition, the emission spectrum peak (D) corresponds to the energy transfer from level
Amplifier characteristics, comprising the operating wavelength and the gain bandwidth, are determined by the dopants rather than by the host medium. However, because of the tight confinement of light provided by guided modes, fiber amplifiers can provide high optical gains at moderate pump intensity levels over relatively large spectral bandwidths, making them suitable for many telecommunications and signal–processing applications [22].
Currently, the rate equations are known as the most powerful tools to foresee the laser amplifier features associated with the non–uniform pumping along the laser length [23–31]. Here, the rate equations for the quasi–three–energy–level structures are typically introduced with the definition of the relevant parameters. Using the fourth–order Runge–Kutta method, the effect of ASE parasitic noises and gain can be considered to assess the efficiency. The performance of amplifiers also depends on the rates of radiative and nonradiative decays caused by several mechanisms related to lattice vibrations, ion–ion interactions, and cooperative up–conversion to higher levels [32].
In the case of fiber amplifier as a single–pass array without reflectors, the backward signal intensity,
where the superscripts “±” describe the traveling directions of the light waves along the fiber propagation axis (
Furthermore,
In order to solve these equations, the cooperative up–conversion coefficient (
where
On the contrary, the local intensity per unit wavelength,
In low–power communication fiber amplifiers, the core and cladding structures are centrosymmetric having circular cross sections. In order to remove this power limitation, an intelligent solution was proposed in 1988 by Snitzer and coworkers, namely double–clad fibers. At this model, the fiber core is off–centered or the inner cladding form is triangular, rectangular, hexagonal, elliptical, D–shaped, and so on, where the rays scatter in many accessible directions [25, 38, 39]. Hereupon, the pump light helically propagates through the clad and interacts with the active core. The pump overlapping factor (
where
For single–clad fibers, the dimensionless coefficient
Here,
where
where
where
On the one hand, the normalized frequency is defined as
On the other hand, along with the signal, the spontaneous emission is also intensified. In fact, the parasitic ASE noises cause the degradation of the signal–to–noise ratio (SNR) [47–49]. It is due to the lack of reflectors such as fiber Bragg gratins (FBGs) or dichroic mirrors in the amplifier stage, while the selective wavelength oscillates in the oscillator. The evolution formula for the ASE intensities propagating in a given direction is written according to Eq. (7). The total ASE noise at point
The positive scattering parameter
In addition,
It is worth noting that the boundary conditions for the ASE channels are given by
Furthermore, Rayleigh backscattering (RBS) is another important issue that affects the performance of the signal source, unless the optical isolators are well employed [48, 49, 53]. When ASE and RBS are intense enough, these may restrict the amplifier gain leading to a drop in the efficiency in many applications. In the case of strongly pumped condition, RBS at the ASE wavelengths can be ignored mainly due to the reduction of ASE to get significantly weaker than the signal [54]. Here, we assume that
Thus, the rate equations at signal wavelength can be simplified as follows:
However, in the case of CW fiber amplifiers, disregarding ESA and cooperative up–conversion, the set of first–order coupled nonlinear time–independent steady–state (
It is worth mentioning that there are no significant nonlinear optical effects such as SBS and SRS as well as thermal damages up to 50 W–CW single–mode pump powers [5]. In addition, the dipole–dipole interaction, clustering, and quenching are serious only in highly doping elements.
Tremendous efforts were made to develop the fiber amplifiers during the recent decade. Indeed, the amplifier is characterized by very important and unique gain and saturation parameters [55–60] that are often used to analyze and compare different gain mediums in certain applications.
The advantages of operating in the gain saturation regime are mainly summarized as follows [4]:
Small fluctuations in the input signal do not reflect the same extent in the output amplified signal.
The fiber amplifier, which has multiple spectrally close input signals with varied intensity, may work as a gain equalizer because smaller input signal powers have higher gain (through less saturation), and higher input powers have lower gain (due to a higher degree of saturation).
A saturated optical amplifier demonstrates a high–energy extraction efficiency; therefore, the overall efficiency of the system is high.
According to theoretical analysis, high–input optical fluence does not increase indefinitely in an amplifier but rather saturates, leading to amplifier gain reduction. Obviously, even from the general consideration of energy conservation, the gain of the amplifier has to saturate, because one cannot extract more power from the amplifier than it was excited (pumped) with.
The emission and absorption cross sections are used to introduce the gain coefficient. According to Eq. (28), the gain for signal radiation after simplification can be defined as follows:
In a fiber amplifier, it depends on the distance
Due to high signal power, which saturates the gain, the first term of the dominator is small enough with respect to others. Assuming
where the signal saturation power is introduced as follows:
It decreases the small–signal gain by a factor of one–half equivalent to a 3 dB reduction in gain [49]. It is worth mentioning that not only
From another point of view, the gain coefficient in the steady–state condition can be explained by
where
Let us assume a doped dual–clad fiber amplifier with length
where
Here,
Hence
In the general case of the varied inversion along the gain fiber, the absolute gain factor should be integrated along the entire fiber length [4]:
Accordingly, the gain and saturation properties are strongly dependent on the pump power, dopant concentration, fiber length, as well as the pumping modes (co–/counter–propagation and bidirectional pumping) mainly due to the dominant effect of the filling factors [26, 61, 62].
In return, if optical pulses are amplified, the gain coefficient associates with the elapsed time
where
The nature of spectral line broadening plays a very important role in the performance of the fiber laser amplifiers. The stimulated emission cross section is conventionally rewritten by
where
The spectral line broadening at room temperature (300 K) smooths the overall line shape, which becomes resolved only at low temperatures [4]. With a temperature decrease, Stark level structure becomes more and more evident and determines the line shape characteristic profile.
In the case of rare–earth–doped fiber oscillator and amplifier, different types of broadenings exist. These include strong homogeneous as well as the inhomogeneous broadenings [56, 63]. The designation of whether the transition line shape is homogeneously or inhomogeneously broadened is based on whether the lines are from the same type of centers or from different sorts.
Usually, a homogeneous broadening is explained by the random perturbation of a similar type of optical centers, while that from different kinds is responsible for inhomogeneously broadened lines [4]. Such kinds of interaction result in the shortening of the excited state lifetime of the optical center.
Therefore, the homogeneous broadening is inherent to each atom in a medium as depicted in Figure 3(a). It can be spontaneous broadening, Stark broadening, collision broadening, thermal and dipolar, and so on. The spontaneous broadening is a primary broadening yielded from the lifetime of a pumping level and the frequency uncertainty. The Stark broadening is due to the degeneration of energy level caused by external electric field, making the lifetime very short and the spectral width very large. Moreover, the collision broadening is dominant for gas lasers as excimer lasers operate with high gas pressure discharge.
(a) Homogeneous and (b) inhomogeneous broadenings
Conversely, the inhomogeneous broadening of the optical center’s line shape during a transition between energy levels originates from a local site–to–site variation in the optical center’s surrounding field in the lattice environment. The strength and symmetry of the field around the rare–earth ion determine the spectral properties of the optical transitions, as well as the transition strength. According to Figure 3(b), in the case of inhomogeneous broadening, the overall shape of the spectral line is a superposition of all individual, homogeneously broadened lines corresponding to different types of the optical centers. Hence, this is a phenomenon that various frequencies are generated by a variety of influences, and the spectrum is broadened.
The inhomogeneous broadening can be a strain caused by lattice defect or inhomogeneity of magnetic or electric field (crystalline field) for solid–state lasers and Doppler broadening for gas lasers.
In a crystal of solid–state laser, the crystalline field is not uniform since the crystal is imperfect. As a result, a large inhomogeneous broadening is yielded. In a glass doped with rare–earth ions, the inhomogeneous width is relatively largely broadened compared with the crystalline solid–state laser. The amorphous nature of the glass, random distribution of dopants, and host material imperfection lead to site–to–site variations of the local electric field, producing a degree of inhomogeneous broadening in the system which results in spectral hole burning (SHB).
Indeed, the optical transition line shape in the case of homogeneous broadening is described by a Lorentzian function given by
where
Lorentzian–shaped and Gaussian–shaped spectra [
Subsequently, the line shape of the inhomogeneously broadened optical transition corresponds to the Gaussian line profile, which can be expressed as the following function of frequency:
where
The state–of–the–art fiber amplifiers are significantly related to the determination of amplifying parameters (gain and saturation) which are not explicitly introduced into the rate equations; however, these can be obtained by using a suitable amplification relation. It is applicable for the homogeneous line broadening in Yb:silica fiber amplifiers [63] as well as the Er–doped silica fiber lasers with inhomogeneous line broadening.
In general, the laser amplification consists of a couple of distinct transient (short pulse) and steady–state (long pulse) regimes. The gain and saturation values can be estimated by numerically solving the amplification relations performing the best fitting based on least square method (LSM).
In the transient form of the amplification relation, the input pulse is considerably shorter than the fluorescence lifetime of the medium, such as Q–switched or mode–locked pulses with inhomogeneous gain media. The propagation rate equations are the best procedure to simulate the amplified signal energy released from the stimulated emission. This process depends on the energy stored in the upper laser level prior to the synchronization situation of the pumped atoms and incident seed signal. Furthermore, the time–dependent photon transport equation can be utilized to evaluate how an inverted population affects the distribution of a pulse traversing through the amplifier. Hopf [65] and Frontz–Nodvik [66] solved the nonlinear equations for various types of input pulse shapes to obtain the inverted electron population and the photon flux, while the input signal to the amplifier was taken as a square pulse of duration
where
For the pulses whose shapes are sufficiently near the eigen–function for the propagation in the amplifying medium, the change in the average temporal width of the pulse can be reasonably expected to be negligible. In such cases, the approximation
where
Here,
For the seed signal, which is long compared to the fluorescent time including the free running oscillator or the CW seed laser [5, 67, 68] under homogeneous line broadening, a Hargrove [69] steady–state gain characterizes the amplification mechanism as follows [5, 6]:
Here,
where
In addition, in the limiting cases
These relations indicate that for low–input powers, the overall gain (
A straightforward integration shows that the CW gain of the amplifier is considered by
The origin of gain saturation lies in the power dependence of the gain coefficient. Since
One of the most widely applied analytical techniques in a broad variety of application areas is high-performance liquid chromatography (HPLC). It stands out due to its high precision, efficacy, and robustness. Despite its undeniably good aspects, the susceptibility of analyteʼs retention to a diversity of experimental setup parameters makes HPLC method development a time-consuming and expensive process. Unfortunately, the selection of an appropriate combination of chromatographic conditions related to both a stationary and a mobile phase, as a starting point for the analysis of a particular drug chemical entity, is often done using a trial-and-error approach [1]. At the same time, one of the major goals of contemporary chromatographic analysis is to efficiently identify optimal working conditions for a better success rate in the method development. Luckily, a tailored, pragmatic approach denoted as quantitative structure–retention relationship (QSRR) modeling was introduced [1, 2]. With the assistance of computerized statistical methods, QSRR models are supposed to mathematically relate the molecular structural properties with the chromatographic response of a drug generated within a set of defined experimental conditions. The molecular structure encodes its physicochemical information in the form of numerical quantities denoted as molecular descriptors. This approach offers great assistance in understanding the analyteʼs chromatographic behavior and enables the discovery of physicochemical processes involved. As expected, statistical QSRR studies are, therefore, recognized as a supreme chemometric approach leading to the timely enhanced, high-quality separation, and efficient analytical method development [1, 2].
QSRR models are commonly associated with the retention prediction of a new and non-analyzed compound. However, QSRR models are much more useful since they are applied in revealing the molecular descriptors with the greatest retention predictive potential as well as in revealing the mechanisms that govern the separation in a specific chromatographic system on a molecular level. Based on a reliable QSRR model that accounted for different sets of chromatographic data within the same type of stationary phase (e.g. reversed-phase (RP)), the quantitative comparison of chromatographic columns can be achieved [1, 3]. The additional value of the same data refers to the direction where to look for a chromatographic column with equivalent performance and orthogonal selectivity as well as to upgrade chromatographic performances that are the most responsible for retention parameters inclusive of a short overall run time [4]. Besides all the aforementioned, many authors assert that the retention in an HPLC system, especially in RP- and micellar chromatographic systems, can be closely related to the biological activity of a drug. This can be understandable in terms of a compound’s lipophilicity and pKa value because its chromatographic distribution between stationary and mobile phases is highly similar to its bodily distribution between the cell membrane and intracellular or extracellular fluids. As a result, the chromatographic data can be related to the description of biological processes of drug absorption, distribution, and excretion as well as drug-receptor interactions. Looking at the QSRR study within these wider frames, this approach can be used as a valuable in silico method for the prediction of the analyteʼs lipophilicity and biological activity of potentially new drug molecules. In such a manner, the utilization of less effective experimental methods and animal models can be reduced [3].
Because of their wide applicability, the QSRRs methodologies have been quite extensively studied over the past two decades. The first article, in which Tamf and Kamlet mention QSRR in a similar context known nowadays, dates from 1977. However, an intense interest in this topic has arisen over the last two decades after the work of Roman Kaliszan [5]. The first theory used to describe chromatographic retention was the theory of linear free-energy relationships (LFER), according to which the analyteʼs retention parameters reflect the free-energy changes associated with the chromatographic distribution [6]. In that regard, a chromatographic column is recognized as a “free-energy transducer,” which translates the chemical structure differences of compounds into quantitative differences in the retention parameters. In order to provide the proper knowledge about a chromatographic system, a relatively large set of reliable input data, coming from a group of structurally heterogeneous compounds and retention data, is needed. The early introduced QSRR models were based on a priori selection of a small set of structural descriptors derived from a molecular formula or a molecular graph reflecting physicochemical properties. These sets of structural descriptors are well known to chemists since they originate from the accepted theories of chromatographic separation and the interpretation of fundamental intermolecular interactions [7]. Since the representation of the separation process solely in terms of intermolecular interactions is questionable, an approach based on linear solvation energy relationships (LSER) was introduced by Abraham [6, 7]. He pointed out a new notably expanded set of molecular descriptors indicating the difference in interactions as a consequence of the solvent properties of the mobile and stationary phase. In parallel with these considerations, to provide reproducible quantitative input chromatographic data, two main methodological directions may be observed in the literature. The retention data can be determined under the same experimental condition or by varying chromatographic conditions, such as mobile phase compositions, flow rates, column temperatures, etc. [1]. The latter approach, also known as mixed QSRR modeling, is found to be advantageous. It enables the recognition of patterns in analyteʼs retention changes within observed experimental ranges of chromatographic parameters and consequently an in-depth understanding of complex chromatographic systems. In addition to proper input data, statistically significant and physically meaningful QSRR modeling finally relies on solid mathematical analysis. The usual technique for the mathematical description of correlations between all gathered data is multiple linear regression (MLR). However, the advances in liquid chromatography (LC) and an increase in the amount of chromatographic data generated over time make the conduction of a QSRR study difficult to handle traditionally. In that regard, QSRR models have shifted from a priori selection of simple descriptors and traditional regression analysis to the generation of a large pool of molecular descriptors and machine learning algorithms (MLAs) based on linear and/or nonlinear regression analysis [1]. For the sake of obtaining chemically valid interpretations, useful and reliable QSRR models demand a selection of the most informative and predictive descriptors among often mutually correlated ones. Therefore, the need for suitable selection techniques for input information data emerged accompanied by QSRR model validation strategies used to evaluate model prediction performance [2]. High-performance calculations at all the stages have made the process of LC method development more efficient and sustainable. They have also improved the fundamental knowledge of the separation processes. In accordance with numerous benefits, the anatomy of the QSRR modeling is reviewed below in conjunction with the guidance of modern requirements and tendencies.
The power of QSRR comes from the characterization of compounds via molecular descriptors (MDs) that depict the physicochemical information of molecules in a numerical manner. The concept of MDs has come a long way in the last 50 years as it witnessed constant progress in computational chemistry. The accompanying advances in hardware enabled the calculation of over 5000 descriptors for a single molecule [8, 9]. Depending on the classification criteria, molecular descriptors can be divided into several groups. Some descriptors are obtained experimentally, while the others are purely theoretical. According to the data type, molecular descriptors can be Boolean, integer, real, vector, etc. According to the structural dimensions (D), on the other hand, molecular descriptors can range from 1D to 6D [10, 11]. Based on the references outlining their application in QSRR studies, the extensively used descriptor-calculation software are AlvaDesc [12, 13, 14], Dragon [15, 16, 17, 18, 19], Molinspiration Cheminformatics [20, 21]. and Chem3D Ultra [19]. The latest and freely available software, PaDEL-Descriptor [14, 18] and Mordred [22, 23], allow MDs to be computed under open science practice representing a valuable addition to the palette of commercial software. For more simple descriptors (e.g. compositional or topological descriptors), a simplified molecular input line entry system string (SMILES) or a 2-D map can be used to represent molecules under study. If descriptors give more information, molecular geometry has to be determined prior calculation process. The accuracy of the most descriptors subsequently depends on the method used to build a 3-D molecular structure. Given a variety of the computational methods used for optimizing the geometry of analytes for QSRR studies and the availability of resources, researchers can opt to perform empirical force field methods (e.g., molecular mechanics), semi-empirical optimization (e.g., AM1, PM3), or sophisticated
A small set of predictors (i.e., input variables or factors) with well-known physicochemical meaning can be pre-defined when modeling separation in systems with fully elucidated retention mechanisms. For complex chromatographic modes, such as micellar liquid chromatography (MLC) and mixed-mode liquid chromatography (MMLC), a priori attribute selection may compromise the accuracy of QSRR predictions making it a poorly acceptable strategy for retention modeling [27, 28]. Alternatively, a large set of independent variables can be formed; the most significant attributes can be extracted from it and used to build a model for retention time prediction. Clearly, the predictive ability of these models depends on the efficiency of the mathematical algorithm used to select predictor variables [7, 29].
The choice of the most informative features for a particular regression problem poses one of the main challenges in machine learning (ML). Determining the appropriate method of variables selection, in this regard, has been an interesting topic in a broad range of domain applications, including studies for which the datasets with hundreds or thousands of attributes become available along with the development of molecular modeling software. Faced with plenty of noisy and irrelevant features, contemporary QSRR studies call for variable selection without exception. The purpose of variable selection methods is to handle space dimensionality by discarding the features that are redundant and irrelevant in predicting endpoint values. A feature is irrelevant if it is unpredictive for the dependent variable or response. A reduction is needed if it is highly correlated with other features. The adoption of feature selection techniques ultimately avoids overfitting, improves a model’s predictive power, and enhances an understanding of the underlying patterns preserved in data. A decreased computational burden placed on modeling techniques as well as easier data visualization happens to be additional benefits associated with feature selection techniques [29, 30, 31].
In a typical MLA-empowered QSRR pipeline, a minimal feature subset is determined after the pre-processing of raw data and before the modeling. Among various techniques, MLR, genetic algorithm (GA), and Relief method have been quite eagerly used in QSRR studies [31, 32]. Other important feature selection methods are least absolute shrinkage and selection operator (Lasso), artificial neural network (ANN), and random forest (RF) [24, 27, 33]. The last two algorithms will be discussed later in the text, as part of Section 2.4.
MLR founds a linear relationship between a dependent variable and two or more independent variables (regular attributes). It is basically the extension of the Ordinary Least Squares (OLS) method.
The general MLR model can be written using Eq. (1):
where
In the OLS method, the slope coefficients that minimize the loss function come from Eq. (2):
The use of MLR makes sense only if: a) there is a linear relationship between predictors and dependent variable, b) the correlation between variables is not too high, c) the instances are chosen randomly from the population, and d) the residuals are normally distributed. MLR estimator is burdened with a great variance, especially in the cases, where the number of attributes approaches the number of observations [27].
Lasso regression is one of the most popular regularization methods for selecting significant independent variables. The concept of regularization has been introduced to avoid overfitting in MLR modeling. In brief, the regularization refers to adding a “penalty” term to the best model built upon a training dataset and to achieve a smaller variance and control the influence of the predictor variables over the response. In Lasso regression, this is done by penalizing the absolute value of the magnitude of coefficients (Eq. (3)).
In Eq. (3), λ is the tuning parameter that controls the amount of shrinkage. If λ is large, the slope coefficients are penalized highly toward 0, and more features are eliminated. If λ is 0, all features are considered and the residual sum of squares criterion is applied. As λ increases, the bias increases. Otherwise, the variance increases [27, 34].
GAs are methods that generate a solution for optimization and search problems by simulating the mechanism of natural selection and the survival of the fittest. In the initial stage, the GA creates a random population of chromosomes. Each chromosome, usually represented by a binary string, encodes a potential solution to the problem under study. In the case of feature selection, individual chromosomes make up a random subset of variables, where the presence or absence of a variable in the chromosome is denoted by 1 or 0, respectively. Using individuals in the current generation, the GA creates a sequence of new populations. To achieve this goal, the algorithm first evaluates each chromosome of the current population by determining its fitness value. The fittest individuals are selected to pass their genes to the next generation. Offsprings are, in fact, produced by subjecting the selected parents to crossover (gene exchange) and mutation (gene change in individuals). In addition, some of the population’s members with the best fitness values are chosen as elite children and added directly to the next population. The subsequent generation is formed after children with inherited good characters replace the current population of parents. The GA loops until one of the stopping criteria is met (e.g. a predefined number of generations). The flowchart (Figure 1) outlines the main GA steps.
GA flowchart.
In terms of the prediction accuracy of constructed QSRR models, the GA showed superiority in selecting the most relevant features compared to other variable selection methods [18, 35]. Lately, the GA has been used for the non-polynomial hard problem of feature selection [36], the selection of molecular descriptors for localized QSRR models [37], and the development of a QSRR model intended to improve the structural annotation of triterpene metabolites in an LC-HRMS system [38].
In this method, each attribute is assigned a relevance weighting according to its ability to distinguish between class labels. Attributes with weight above the user-defined threshold τ are considered significant and included in the set of selected features. The underlying principle is that the instances belonging to the same class should be closer than those of different classes. The algorithm cycles over
Originally, the Relief algorithm has been intended for classification problems and could be fooled by an insufficient number of cycles. Nowadays, it has been adapted for predicting continuous decision variables and as such is being used for QSRR studies (e.g. to predict retention parameters). The differences between Relief, ReliefF, and RReliefF are presented in detail in [39].
When implementing supervised algorithms, it is a good practice to examine data distribution. The distortion of the symmetry of normal distribution around its mean is denoted as skewness. A general impression of skewness can be gained by drawing a histogram or computing the skewness coefficient. If the distribution’s shape has one peak and a long tail on the right side of the curve, the distribution is positively skewed. In contrast, the distribution has a negative skew if a long tail is on the left side of the curve. In numerical terms, the skewness for a normal distribution is (approximately) 0. Negative coefficients are related to negative skewness and vice versa. The coefficient values between – 0.5 and + 0.5 indicate moderately skewed data, and if they are less than −1 or greater than + 1, the distribution is highly skewed. A highly skewed dataset can contaminate a model’s predictive performance because the algorithm has to deal with scattered endpoints at extreme values. In the case of right-skewed data, for instance, MLAs are likely to predict points with lower values better than those with high values. Therefore, skewed distribution is one of the major obstacles to the application of MLAs to real-world data and should be addressed prior to the modeling. A common strategy for dealing with skewed variables is to transform them. Logarithmic, square root, and cube root transformations are recommended when data follow the power-law distribution, while in the opposite case, it is better to opt for square, higher powers, or cube root transformations [27, 40, 41].
The choice of regression technique for correlating molecular descriptors and chromatographic conditions with a chromatographic parameter has a huge impact on the performance of any QSRR model. Due to its simple and explainable character, MLR received considerable attention in mechanistic research long ago [24]. However, if researchers amass vast troves of data and cannot make sense of it in a reasonable amount of time, the process is the main candidate for modeling through more sophisticated MLAs. MLAs fall under the umbrella of artificial intelligence and can process and understand data faster. These algorithms learn to resolve issues by drawing firm conclusions from observation data they are supplied with. Along with improvements in technology and computing power, QSRR can take advantage of machine learning in a fundamental and practical manner. By acknowledging nonlinearity in LC data, MLAs play an important role in the accuracy of property predictions. However, no currently available MLA can deliver optimal performance for every modeling task. A variety of MLAs should be used before selecting a particular regression technique. The common MLAs are ANNs, support vector regression (SVR), and ensemble methods [42].
ANN is a series of machine learning algorithms that mimic the process of natural thinking by making experience-based decisions. Modeled on the human brain, the ANN refers to a massive composition consisting of some primitive processing elements (i.e. artificial neurons). Most operative neural nets are constructed by grouping neurons into layers. An individual neuron might be connected to several nodes in the layer beneath, i.e., above it. Data passing through layers in only one direction makes up a feedforward neural net (or multi-layer perceptron). Apart from the layers, the main components of ANN include the adaptive coefficients –weights, assigned to each of the connections between the layers, as well as the transfer functions, which convert received raw data into output. The transfer functions, learning rules, and architecture itself define the behavior of each ANN [43]. When a neural net is being trained, all of the weights are first randomly assigned to synapses between neurons. Then the input-output pairs of data are fed to the net in an attempt to train an algorithm to recognize the underlying patterns between variables. This strategy pertains to the process of supervised learning. In a supervised feedforward backpropagation algorithm, the training is performed by comparing the processed signals with the desired outputs and adjusting the inputs’ weights until the margin of error is minimal. Herein, the weights are updated in the steepest descent fashion. Higher weights are attributed to the inputs that contribute the most to achieving the right target [44, 45].
Neural nets are a valuable tool for analytical R and D due to the ability to learn nonlinear relationships encountered between predictors and dependent variables in most corresponding systems. Contemporary applications of ANN in the pharmaceutical sciences are broad, ranging from interpretation of analytical data to drug design. Over the past decade, there has been an impressive increase in the number of publications on QSRR studies that used ANN as a modeling technique. In particular, the single-hidden layer neural nets provided a satisfactory level of prediction accuracy [46, 47, 48, 49, 50, 51]. After the improvement in computer power and the rise of big data, ANNs began to flourish in the form of deep learning (DL) algorithms [52, 53, 54, 55]. Deep neural networks are the ones that have more than one hidden layer. With each additional layer, the DL algorithm can model increasingly complex relationships. As compared to other ML techniques, ANN architecture is characterized by great flexibility and can process raw data and automatically extract a set of the most informative features. Unfortunately, the DL is not free of limitations; in general, these algorithms are data-hungry and require massive training sets. The question to be raised, in that respect, is whether the analytical domain can provide big data without losing valuable resources [56, 57]?
SVR is another promising machine learning algorithm that acknowledges the nonlinearity in data. It is built on the principles of statistical learning and the concept of constructing a line (or hyperplane in high-dimensional space) that fit the data. Among an infinite number of possible solutions, SVR finds a hyperplane with the greatest distance to the nearest training instances. Finding such a hyperplane is based on minimizing the l2-norm of the coefficient vector,
In Eq. (6),
For the endpoints that reside outside the
The SVR hyperplane is constructed after the inputs are mapped into a space of higher dimension(s) than the original using the kernel function (e.g., polynomial, splines, radial basis function, etc.). Then, using a simple linear function, the SVR helps predict the target value. By projecting the optimal hyperplane back into the input space, it takes on a nonlinear form. Due to its remarkable generalization ability, the SVR has gained popularity in QSRR studies [31, 44, 55, 58, 59, 60, 61, 62, 63]. In most publications, the empirical performance of SVR matches with or is considerably better than the performance of other MLAs studied.
In ensemble learning, algorithms with high bias or too much variance (so-called weak learners) are merged to produce the most popular result. The underlying idea of aggregating predictions is to create a much more accurate and robust model. Bagging (also known as bootstrap aggregation) and boosting are the most prominent classes of ensemble methods [64].
Weak learners that are used widely in ensemble learning are decision trees (DTs). DTs are nonlinear machine learning techniques that can handle either regression or classification tasks. They are simple, intuitive, and can deal with missing values and large datasets with elegance. The classification and regression tree (CART), introduced in 1984, is a typical DT algorithm [65]. It is presented as a tree-shaped diagram containing a set of nodes and branches growing downwards. This topology gives the idea of a binary and hierarchical algorithm that adopts the recursive partitioning method. It is an iterative procedure that seeks to find the best split (the best splitting feature and the best input data) at each step. Performance metrics, e.g., Gini index, information gain, or error rate, are utilized to assess the quality of the split. Fundamentally greedy nature and poor ability to cope with the penalties on tree complexity (while growing the tree) are the main disadvantages of the top-down approach. Pruning is done to prevent an overfitting phenomenon [66].
RF was introduced as a DT-based ensemble in 1984 [65]. It is a collection of unpruned DTs (grown to the maximum extent) that are trained by the bagging method. In bagging, base models are grown on bootstrapped subsets of the data and the individual predictions of all base models are averaged to get the final output. As a result, the ensemble model has less variance than its building elements. While sharing the main idea with bagging, the RF adopts an additional level of randomness – each node of each tree deliberately takes into account only a random subset of features (e.g., the square root of a number of descriptors) for the splitting procedure. An RF model benefits from this tactic in terms of efficacy. In addition, it is important to mention that the internal validation is built into the forest growth. According to the concept of bootstrapping, some of the data are omitted from the samples intended for tree growth, while the others are repeated in the samples. The former is denoted as Out-Of-Bag (OOB) data. Given the fact that the OOB sample is not included in the tree fitting, it is used to estimate the model error. Usually, it makes up to one-third of the available data, while the other two-thirds of the data is used for training. In order to achieve a small OOB error, it is necessary to optimize the number of base models and the size of a subset of features [66, 67].
In QSRR studies, the RF algorithm is readily used as a modeling technique [42, 55, 66, 68] as well as a feature selection method [69, 70]. The latter is due to the ability of the algorithm to quantify the importance of variables under study. The importance of each feature is determined by observing a change in prediction error when the OOB set for that feature is permuted (and the other features are kept constant).
Gradient boosted trees (GBT) is an extremely powerful ensemble algorithm based on boosting and gradient descent approach. Unlike the bagging, which combines weak learners in parallel, the boosting merges base models linearly. The focus here is especially on shallow DTs that have low variance and high bias.
A correlation between base models (arising from the same data) is precluded by an incremental change of the training set. This is done by assigning weights to each example. Initially, all weights are set to be equal and the first decision tree is trained on the original dataset. Accurately predicted instances have their weights decreased, while the others have their weights increased. The trees that enter the ensemble in subsequent iterations are thus applied to the reweighted data and their goal is to correct the errors made by the previous model. Boosting, which decreases the bias of individual base models, is viewed as one of the groundbreaking concepts introduced in ML over the last decades. The GBT algorithm minimizes a loss function via a gradient descent procedure. The predictive power of the GBT ensemble correlates with the number of base models and the size of learning rate. A larger ensemble will very quickly over-fit, while a combination of too few DTs might lead to poor predictive performance. Lower values of learning rate (a parameter that controls the length of incremental step) may resolve the problem of overfitting, but a prolonged convergence toward the solution can place a lot of computational burden on the model in question [71, 72]. Due to the ability to create highly accurate QSRR models (and the fact that it quite often outperforms many other regression algorithms), the GBT is popular in analytical R&D. The successor to the gradient boosting, regularized gradient boosting (i.e., XGBoost), is increasingly used to provide state-of-the-art solutions to many LC challenges as it yields improved generalization capabilities and better avoids over-fitting [27, 32, 42, 73, 74].
It seems that it is feasible to build mathematical models that fit the data very well. But, there is still a possibility that it may happen due to chance correlations or overfitting. In that case, the models are not considered appropriate for their intended application. Therefore, proper statistical validation of models is of great importance in QSRR studies. The two main concepts, denoted as internal and external validation, will be discussed herein. The internal validation procedures include leave-one-out (LOO) and leave-many-out (LMO) cross-validation (CV), y-randomization, and bootstrapping.
LOO-CV is performed by excluding each sample (compound) once and building a model with the remaining data and predicting the value of the response for the eliminated sample. Due to the presence of repetitive cutting data set activities, the LOO is also known as rotation estimation and jack-knife validation method. This approach indicates that the eliminated sample serves as a temporary test set taken from the overall training set. Each cycle of this repetitive procedure is followed by calculating the differences between experimentally observed response values and estimated (predicted) ones by the model. These values are afterward included in Eqs. (9) and (10) corresponding to the root mean square error of CV (
In the aforementioned Eqs. (9)–(11),
To perform the LMO-CV, the initial dataset is divided into blocks of samples; afterward, each block is eliminated once from the model building in each cycle in a similar manner as applied in the LOO-CV. The prediction of response is made for the block under consideration. It should be noted that the blocks may consist of the same number of constituents, but that is not an obligation. The LMO may also have different validation cycles. In that respect, as an example, the original dataset can be divided into 10 parts indicating that each data block accounts for 10% of the data, 10 validation cycles are needed and the respective method may be referenced as 10-fold CV as well. In comparison with the LOO, this procedure is more time-effective. The validation metrics are similar to those presented for the LOO-CV with adjustments in relation to a sample or a block of samples. It is worth mentioning that the same appropriate adjustments must be implemented in the used equations. Also, since there is no truly new compound under consideration within none of the variations of the internal validation procedures, it is advisable to perform as many as possible internal validation tests for the final justification that the model is of good quality, relevant, and suitable for its intended use. This recommendation especially stands in the case of the modeling based on small datasets where any omission of data from the original dataset may lead to the inability to perform the modeling procedure at all [75, 76].
Y-randomization is used to ensure the robustness of the developed model. For example, it can check whether there is a molecular descriptor statistically well correlated with the response value y; but, in reality, there is no cause-effect relationship originating from the physical and/or chemical meaning of a molecular descriptor and the respective retention measurement. The model validation is performed by keeping the so-called X matrix with original unchanged descriptors while the vector of the response values y is randomized or scrambled. Since the new models are built based on the same input dataset but associated with changed (false) responses, it is expected that they are of poor quality as reflected by the values of
Bootstrapping procedure suggests the random splitting of a complete dataset into training and test sets several times and the building of respective models afterward. While in the LOO and LMO procedures each sample is excluded from the modeling only once, in the bootstrapping there is an equal chance for a sample to be eliminated once, several times, or even never. The corresponding
The predictive power of a QSRR model is evaluated by the external validation, with
The statistical parameters for model evaluation include the multiple coefficients of determination of external validation, also called
Apart from the inspection of the statistical parameters computed from the respective validation procedure, to assure the quality and practical usefulness of QSRR models, it is worth getting insight into the possible sources of prediction errors (residuals). In that respect, besides the calculation of
In addition to the statistical model assessments, the predictive power of a robust and validated QSRR model must be expressed in terms of the applicability domain. The model interpretability is affected by its characteristics as well. This domain refers to a theoretical space defined by a range of the molecular descriptors of compounds used for model training purposes and respective chromatographic conditions as well as a range of the modeled responses. It is obvious that the applicability domain strongly reflects the physicochemical and structural properties and chromatographic behavior of compounds from a training set. In order to make the best response predictions, the training set must be similar to the target molecule [24, 44, 75]. The aim of narrowing the space for making predictions actually serves to avoid unjustified and inaccurate model extrapolations. The dedicated approaches for the definition of applicability domain based on the range of response variables or the range in the descriptor space (geometrical methods and distance-based and probability density distribution-based methods) were thoroughly described by Roy et al. [82, 83]. As the issue is closely related to the applicability domain, the same authors elaborate the strategy for a proper selection of data to be introduced in the training and/or test dataset out of the original dataset as well [83]. It is perfectly reasonable to state that the lack or poorly conducted selection of compounds increases modeling errors and calls into question the success in all predefined QSRR modeling goals or application areas.
After summing all the previous considerations into a graphical presentation, the QSRR flowchart may look like the one in Figure 2.
QSRR workflow.
The application of the QSRR approach is directly driven by its definition. As the QSRR represents a mathematical relationship between molecular retention behavior and its properties inherent in molecular structures (molecular descriptors), they are primarily used to predict the retention behavior of molecules omitted during model development. In addition, it can be used to single out important features, by which the retention behavior is governed and it is possible to gain insight into the retention mechanisms. It can also be applied for stationary phase characterization or their comparison in terms of separation characteristics [5]. In some cases, they can provide drug or xenobiotics classification or an assessment of their bioactivity [2]. By incorporating experimental parameter values into a QSRR model, their application can be expanded on HPLC method development and optimization [84].
Since various highly adaptable mathematical tools are suitable for structuring statistical QSRR models, the QSRR approach shows compatibility with a broad spectrum of HPLC properties. Although it has a place in the modeling of conventional unimodal HPLC, which was discussed in more detail by Haddad et al. [84], it is also a valuable tool in the case of defining more complex HPLC systems. Complicated molecular retention patterns are often generated from mobile or stationary phase modification. Taking into consideration such HPLC system modification, the predictive abilities of QSRR can not only reduce experimental requirements but also provide a deeper insight into the retention mechanism. The following section is not a comprehensive literature review but rather a demonstration of the beneficial properties of QSRR used for characterizing complex HPLC systems applied for the analysis of small molecule substances.
Increasing the retention of poorly retained analytes in RP-HPLC is often achieved by modifying mobile phase properties. The addition of modifiers can provoke changes in the retention behavior by imposing an additional equilibration process.
Compromised retention of basic solutes can be promoted by introducing ion-interaction agents into the mobile phase. Ion interaction chromatography (IIC) involves a series of equilibration processes between chromatographic phases and analytes, which necessitates the understanding of the separation process [85, 86]. An IIC system, with added chaotropic salts, was assessed by Čolović et al. [63]. A mixed QSRR-SVR model was developed based on the retention data of 34 analytes as independent variables were selected i.e., three mobile phase parameters (concentration of NaPF6, pH, and acetonitrile content) and four molecular descriptors (Branching index EtaB with ring correction relative to molecular size (ETA_EtaP_B_RC), calculated octanol/water partition coefficient (XlogP), 3D topological distance-based autocorrelation – lag 9/weighted by polarizabilities (TDB9p) and radial distribution function – 045/weighted by relative polarizabilities (RDF45p) descriptor). The importance of analytes’ steric effects and voluminosity were indicated by ETA_EtaP_B_RC, while XlogP implied the significance of hydrophobicity, which was in line with the RP retention mechanism. However, TDB9p and RDF45p indicated the participation of electrostatic interactions during the retention process. Thus, the hypothesis on the complementarity of the analytes’ electronic structure and the electrical bilayer created in the stationary phase was supported.
In MLC, a modification of mobile phase features is attained by adding surfactants. When surfactants are present at a concentration above the critical micellar concentration, micelle formation occurs. Surfactant molecules can coat the stationary phase as the absorbed monolayer. Moreover, surfactant interaction with both analyte and stationary phase implies the presence of secondary equilibration. Thus, the exploration of the MLC retention process is challenging [85, 87]. A QSRR-MLR modeling approach was performed by Ramezani et al. for testing anthraquinones. These authors linked molecular descriptors (partition coefficient calculated from hydrophobic fragmental constants (logP), Geary autocorrelation of lag 8 weighted by van der Waals volume descriptor (GATS8v), the mean topological charge index which represented the effect of analyte charge in the MLC separation (JGI4), and descriptors based on
Complementation of the available knowledge on MLC was attained by Krmar et al.; numerous mixed QSRR models were developed using different types of algorithms. Not only was the GBT identified as the most suitable but also the most significant properties relevant for the separation of aripiprazole and impurities were extracted. QSRR models, in addition to MDs, contained experimental parameter values (concentration of non-ionic surfactant Brij L23, pH, and the content of ACN) in line with the Box-Behnken design. Steric effects and dipole-dipole interactions were identified to be the most important thermodynamic molecular parameters relevant for retention behavior [27].
Shifting the analytes’ retention behavior in RP-HPLC can also be provoked by adding cyclodextrins (CD) to the mobile phase. Molecular retention patterns are modified because of CD-analyte complex formation, in addition to the adsorption process of CD on the stationary phase surface [85].
Maljurić et al. developed a QSRR-ANN model for the retention property analysis of risperidone, olanzapine, and related impurities in a CD-modified RP-HPLC system. The values of MSs, complex association constants, and chromatographic factors were used in the model. The most influential molecular descriptors and complex association constants were polarizability (POL), solvent-excluded volume (SEV), octanol/water partition coefficient (logP), dipole-dipole energy (DEN), binding energy (BE), electrostatic energy (EE), and unbound energy (UE) [48]. In a later study, a developed model was employed for determining a change in retention factor, the stability constants, and thermodynamic parameters of complex formation [88].
Another QSRR-ANN model for revealing separation processes in a CD-modified RP-HPLC system was developed by Đajić et al. The experimental parameters were acetonitrile percentage, aqueous phase pH, β-CD concentration, and column temperature. The most important molecular descriptors were identified as radial distribution function – 075/weighted by mass (RDF075m), signal 04/weighted by mass (Mor04v), and CATS2D positive-lipophilic at lag 08 (CATS2D_08_PL). It was found that the molecular size, shape, and lipophilicity of analytes significantly affect their retention. The retention behavior is also governed by the size and lipophilicity of the added CDs as it determines the structural agreement with the tested analytes [89].
Non-straightforward retention behavior resulting from the application of an unconventional stationary phase can be defined similarly as in the previous examples. As the QSRR successfully reveals additional interactions shaped by mobile phase modifiers, it can also expose multiple retention mechanisms provided by the stationary phase.
The characteristics of the stationary phase used in immobilized artificial membrane (IAM) chromatography are in line with its structure based on phosphatidylcholine residues covalently bound to silicon dioxide. In this way, the column mimics a phospholipid membrane monolayer and exhibits biomimetic properties [90].
In the research of Ciura et al., the general conclusions about the molecular retention mechanisms of isoxazolone on an IAM chromatographic system were derived from a QSRR model. The purpose of this research was to assess isoxazolone derivatives’ affinity toward phospholipids. The model was developed using differential evolution combined with partial least squares regression (PLS). Molecular descriptors carrying the information referred to van der Waals volume as well as those defined based upon the weighted holistic invariant
In another study, Buszevski et al. tried to gain insight into the biological activity of 30 flavonoids using IAM chromatographic analysis. The GA-PLS algorithm was used for QSRR model development. The conclusion about retention mechanisms was made upon quantum chemical descriptors, indicating that hydrophobic forces, dispersion effect, and electrostatic interactions govern the retention behavior of flavonoids in IAM chromatographic separation [36].
A promising application of QSRR models has also been shown by the explanation of MMLC, where multiple functionalities in charge of providing different intermolecular interactions are integrated into a single stationary phase.
Obradović et al. developed QSRR models to characterize an MMLC system in which RP and hydrophilic interaction (HILIC) modes participate equally. Forty-three substances, serotonin, and imidazole receptor ligands were tested. Interestingly, separate QSRR models for four different types of responses were developed. The retention factor in pure eluents and the turning point for modality shifting were used as selected outputs. For characterizing the partition process in the RP mode, atomic mass, lipophilicity, and intermolecular hydrophobic interactions were proved to be important. The partition process in the HILIC modality was characterized by lipophilicity, distribution of ionic forms, and electrostatic properties. Adsorption, on the other hand, was driven by molecular geometry, electronegativity, polarizability, van der Waals volume, and atomic mass of the tested analytes. For the turning point and modality expressions, distribution of ionic forms, hydrogen bonding properties, and electronic properties, as well as atomic mass, were significant [91].
Russo et al. used a QSRR model developed by PLS in combination with block relevance (BR) to detect retention mechanisms provided by the arginine stationary phase. Due to the diverse interaction ability of the stationary phase, analytes with diverse ionization capacity (neutral, acids, and bases) were selected. It was noticed that the analyte’s size and hydrogen donor capacity were important for the retention of neutral substances. For acidic molecules, descriptors calculated with VolSurf+ software and VS+ descriptors, did not describe the electric charge well enough; the MLR strategy was used for confirmation of the electrostatic background of acidic analytes’ retention. Also, with the constructed QSRR model, it is possible to recognize the turning point for modality shifting. The basic substances did not show a sufficient degree of retention, so it was not possible to qualitatively define the retention mechanisms involved in their separation [92].
With the use of adequate mathematical tools for linking input variables (both molecular descriptors and experimental parameters) with suitable responses, the statistical approach of QSRR modeling does not recognize limitations regarding the type of HPLC system that needs to be characterized. For this reason, it is considered that especially mixed QSRR models can significantly improve the understanding and development of HPLC methods when complex retention patterns are present due to a possible reduction of the requirements for experimental work.
It can be concluded from the literature that QSRR models have been widely applied in chromatographic science, this topic is, therefore, of great interest to researchers in different scientific areas. This chapter has presented the QSRR models with structuring possibilities in detail, the importance of molecular descriptors, and machine learning algorithms selection, as well as different approaches to conducting these important tasks. It can be also used as a guideline when choosing internal and external validation approaches to apply in the consideration of their main advantages and disadvantages. Special attention was put into disclosing the most important QSRR model applications, by pointing out the possibilities of investigating modified HPLC systems that are of great interest to analysts working with different kinds of compounds.
This work was supported by the Ministry of Education, Science and Technological Development, the Republic of Serbia through a grant agreement with the University of Belgrade – Faculty of Pharmacy No: 451-03-68/2022-2114/200161.
We declare that there is no conflict of interest.
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Adsorption kinetics is one of the main factors that must be understood before the applicability of any adsorbent. In every adsorption process, linear or non-linear analysis of the kinetics is applied. The goodness of fit index (coefficient of correlation or sum of squares) is applied to access the best model. The usage of linear or non-linear from of the adsorption kinetics has an impact on the distribution of error function. Almost in every adsorption study, linear forms have been used to conclude the best kinetic model that influence the adsorption mechanism—which might be an error. Therefore, this review highlights the mistakes in the usage of linear and non-linear models. The applicability of the adsorption kinetics in wastewater treatment is also illuminated.",book:{id:"7486",slug:"advanced-sorption-process-applications",title:"Advanced Sorption Process Applications",fullTitle:"Advanced Sorption Process Applications"},signatures:"George William Kajjumba, Serkan Emik, Atakan Öngen, H. 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De Oliveira, D.A.C. Albuquerque, T.G.S. Cruz, F.M. Yamaji and F.L. 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However, its large band gap energy, ca. 3.2 eV limits its absorption of solar radiation to the UV light range which accounts for only about 5% of the solar spectrum. Furthermore, the photocatalytic activity of TiO2 is also limited by the rapid recombination of the photogenerated electron-hole pairs. When used in water treatment applications, TiO2 has a poor affinity toward organic pollutants, especially hydrophobic organic pollutants. Several strategies have been employed to reduce its band gap energy, its electron-hole recombination rates as well as enhance its absorption of organic pollutants. In this chapter, we review some of the most recent works that have employed the doping, decoration, and structural modification of TiO2 particles for applications in photocatalysis. Additionally, we discuss the effectiveness of these dopants and/or modifiers in enhancing TiO2 photoactivity as well as some perspective on the future of TiO2 photocatalysis.",book:{id:"7478",slug:"photocatalysts-applications-and-attributes",title:"Photocatalysts",fullTitle:"Photocatalysts - Applications and Attributes"},signatures:"John Moma and Jeffrey Baloyi",authors:[{id:"250026",title:"Dr.",name:"John",middleName:null,surname:"Moma",slug:"john-moma",fullName:"John Moma"},{id:"250963",title:"Mr.",name:"Jeffrey",middleName:null,surname:"Baloyi",slug:"jeffrey-baloyi",fullName:"Jeffrey Baloyi"}]}],mostDownloadedChaptersLast30Days:[{id:"68496",title:"Moisture Sorption Isotherms and Isotherm Model Performance Evaluation for Food and Agricultural Products",slug:"moisture-sorption-isotherms-and-isotherm-model-performance-evaluation-for-food-and-agricultural-prod",totalDownloads:2001,totalCrossrefCites:6,totalDimensionsCites:14,abstract:"Moisture sorption characteristics of agricultural and food products play important roles in such technological processes as drying, handling, packaging, storage, mixing, freeze-drying and other processes that require the prediction of food stability, shelf life, glass transition and estimation of drying time and texture and prevention of deteriorative reactions. They are useful in the computation of thermodynamic energies of moisture in the products. An understanding of moisture sorption phenomena in products, moisture sorption isotherm (MSI) determination techniques and moisture sorption isotherm model evaluation procedures would be useful in the development or selection, modeling and controlling as well as optimization of appropriate processes to make for enhanced efficiency. The phenomena addressed in this chapter are equilibrium moisture content (EMC)-water activity (aw) relationships and MSI types, temperature influence on isotherms and occurrence of moisture sorption hysteresis. MSI measurement techniques highlighted are the gravimetric, vapor pressure manometric (VPM), hygrometric and inverse gas chromatographic and the use of AquaLab equipment. Commonly used moisture sorption isotherm models (BET, GAB, modified GAB, Hailwood-Horrobin, modified Hailwood-Horrobin, modified Halsey, modified Henderson, modified Chung-Pfost and modified Oswin) were selected, and their evaluation procedures using moisture sorption data were outlined. Static gravimetric technique involving the use of saturated salt solution appears to be the most widely used and recommended method of determining the EMC of agricultural and food products. Most of the MSI models can be fitted to moisture sorption data thorough linearization by logarithmic transformation, while others can be solved using such expression as second-order polynomial. Model goodness of fit can be determined using standard (SE) error of estimate, coefficient of determination (R2), mean relative percentage deviation (P) and fraction explained variation (FEV). The acceptance of a model depends on the nature of its residual plots. A model is considered acceptable if the residual plots show uniform scatter around the horizontal value of zero showing no systemic tendency towards a clear pattern. A model is better than another model if it has lower SE, lower P, higher R2 and higher FEV. Although it appears as if a generalized MSI model is yet to exist, it is recommended that the Ngoddy-Bakker-Arkema (NBA) model should be given thorough going and extensive testing on the MSI of different categories of food as it could prove true to its generalized model posture due to the fundamental nature of its derivation.",book:{id:"8012",slug:"sorption-in-2020s",title:"Sorption in 2020s",fullTitle:"Sorption in 2020s"},signatures:"Ndubisi A. Aviara",authors:[{id:"303694",title:"Prof.",name:"Ndubisi",middleName:null,surname:"Aviara",slug:"ndubisi-aviara",fullName:"Ndubisi Aviara"}]},{id:"63788",title:"Disinfection Methods",slug:"disinfection-methods",totalDownloads:3236,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Water must be made safe to drink, and an important step in ensuring water safety is disinfection. Disinfectants are added to water to kill disease-causing microorganisms. Ground water sources can be disinfected by “The Water Treatment Rule,” which requires public water systems for disinfection. Chlorination, ozone, ultraviolet light, and chloramines are primary methods for disinfection. However, potassium permanganate, photocatalytic disinfection, nanofiltration, and chlorine dioxide can also be used. Organic material is naturally present in water. Certain forms of chlorine can react with these organic materials and result in the formation of harmful by-products; the U.S. Environmental Protection Agency has anticipated maximum levels for these contaminants.",book:{id:"7478",slug:"photocatalysts-applications-and-attributes",title:"Photocatalysts",fullTitle:"Photocatalysts - Applications and Attributes"},signatures:"Muhammad Saqib Ishaq, Zobia Afsheen, Amjad Khan and Amjad\nKhan",authors:[{id:"228353",title:"Dr.",name:"Muhammad Saqib",middleName:null,surname:"Ishaq",slug:"muhammad-saqib-ishaq",fullName:"Muhammad Saqib Ishaq"},{id:"246559",title:"Dr.",name:"Zobia",middleName:null,surname:"Afsheen",slug:"zobia-afsheen",fullName:"Zobia Afsheen"},{id:"246561",title:"Mr.",name:"Amjad",middleName:null,surname:"Khan",slug:"amjad-khan",fullName:"Amjad Khan"},{id:"271289",title:"Dr.",name:"Amjad",middleName:null,surname:"Khan",slug:"amjad-khan",fullName:"Amjad Khan"}]},{id:"41887",title:"Microbial Techniques for Hydrocarbon Exploration",slug:"microbial-techniques-for-hydrocarbon-exploration",totalDownloads:6618,totalCrossrefCites:3,totalDimensionsCites:6,abstract:null,book:{id:"2351",slug:"hydrocarbon",title:"Hydrocarbon",fullTitle:"Hydrocarbon"},signatures:"M.A. Rasheed, D.J. Patil and A.M. Dayal",authors:[{id:"143475",title:"Dr",name:"Mohammed Abdul",middleName:null,surname:"Rasheed",slug:"mohammed-abdul-rasheed",fullName:"Mohammed Abdul Rasheed"},{id:"144630",title:"Dr.",name:"Dayal",middleName:null,surname:"Anurodh",slug:"dayal-anurodh",fullName:"Dayal Anurodh"}]},{id:"58999",title:"The DFT+U: Approaches, Accuracy, and Applications",slug:"the-dft-u-approaches-accuracy-and-applications",totalDownloads:4543,totalCrossrefCites:21,totalDimensionsCites:45,abstract:"This chapter introduces the Hubbard model and its applicability as a corrective tool for accurate modeling of the electronic properties of various classes of systems. The attainment of a correct description of electronic structure is critical for predicting further electronic-related properties, including intermolecular interactions and formation energies. The chapter begins with an introduction to the formulation of density functional theory (DFT) functionals, while addressing the origin of bandgap problem with correlated materials. Then, the corrective approaches proposed to solve the DFT bandgap problem are reviewed, while comparing them in terms of accuracy and computational cost. The Hubbard model will then offer a simple approach to correctly describe the behavior of highly correlated materials, known as the Mott insulators. Based on Hubbard model, DFT+U scheme is built, which is computationally convenient for accurate calculations of electronic structures. Later in this chapter, the computational and semiempirical methods of optimizing the value of the Coulomb interaction potential (U) are discussed, while evaluating the conditions under which it can be most predictive. The chapter focuses on highlighting the use of U to correct the description of the physical properties, by reviewing the results of case studies presented in literature for various classes of materials.",book:{id:"6193",slug:"density-functional-calculations-recent-progresses-of-theory-and-application",title:"Density Functional Calculations",fullTitle:"Density Functional Calculations - Recent Progresses of Theory and Application"},signatures:"Sarah A. Tolba, Kareem M. Gameel, Basant A. Ali, Hossam A.\nAlmossalami and Nageh K. Allam",authors:[{id:"175824",title:"Dr.",name:"Nageh",middleName:"K.",surname:"Allam",slug:"nageh-allam",fullName:"Nageh Allam"},{id:"398157",title:"Dr.",name:"Sarah A.",middleName:null,surname:"Tolba",slug:"sarah-a.-tolba",fullName:"Sarah A. Tolba"},{id:"398158",title:"Dr.",name:"Kareem M.",middleName:null,surname:"Gameel",slug:"kareem-m.-gameel",fullName:"Kareem M. Gameel"},{id:"398162",title:"Dr.",name:"Basant A.",middleName:null,surname:"Ali",slug:"basant-a.-ali",fullName:"Basant A. Ali"},{id:"398163",title:"Dr.",name:"Hossam A.",middleName:null,surname:"Almossalami",slug:"hossam-a.-almossalami",fullName:"Hossam A. Almossalami"}]},{id:"40233",title:"Ammonia as a Hydrogen Source for Fuel Cells: A Review",slug:"ammonia-as-a-hydrogen-source-for-fuel-cells-a-review",totalDownloads:9336,totalCrossrefCites:15,totalDimensionsCites:40,abstract:null,book:{id:"2795",slug:"hydrogen-energy-challenges-and-perspectives",title:"Hydrogen Energy",fullTitle:"Hydrogen Energy - Challenges and Perspectives"},signatures:"Denver Cheddie",authors:[{id:"141157",title:"Dr.",name:"Denver",middleName:null,surname:"Cheddie",slug:"denver-cheddie",fullName:"Denver Cheddie"}]}],onlineFirstChaptersFilter:{topicId:"86",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81713",title:"Transition Metals-Based Metal-Organic Frameworks, Synthesis, and Environmental Applications",slug:"transition-metals-based-metal-organic-frameworks-synthesis-and-environmental-applications",totalDownloads:41,totalDimensionsCites:0,doi:"10.5772/intechopen.104294",abstract:"This work illustrates examples of metal-organic frameworks (MOFs) derived from transition metals and their environmental applications in areas of catalysis, sorption, and hydrogen evolution. Explanation of some of the techniques employed for their synthesis has been discussed. On the other hand, the advantages of the use of hybrid materials such as the metal-organic frameworks are exposed in this book as well a detailed description of the different linkers and metals used for the synthesis of this kind of porous materials going through the methodologies and techniques utilized by different authors to obtain good-quality crystalline applicable materials. Adjustments of linker geometry, length, ratio, and the functional group can tune the size, shape, and internal surface property of an MOF for a targeted application. The uses of MOFs are exploring new different areas of chemistry such as catalysis, adsorption, carrier systems, hydrogen evolution, photocatalysis, and more. Different examples of MOFs from Scandium to Zinc are well described in this book, and finally, a brief description of some common environmental applications such as metals and azo dyes sorption, hydrogen evolution, and catalyst in the transesterification process of vegetable oils to produce biodiesel is explored and commented.",book:{id:"11216",title:"Sorption - From Fundamentals to Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11216.jpg"},signatures:"Lidia E. Chiñas-Rojas, Guadalupe Vivar-Vera, Yafeth F. Cruz-Martínez, Seth Limón Colohua, José María Rivera and Eric Houbron"},{id:"81332",title:"Adsorption of Chromium from an Aqueous Solution onto Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)",slug:"adsorption-of-chromium-from-an-aqueous-solution-onto-chitosan-beads-modified-with-sodium-dodecyl-sul",totalDownloads:25,totalDimensionsCites:0,doi:"10.5772/intechopen.104093",abstract:"The goal of this research is to make chitosan beads that have been treated with sodium dodecyl sulfate (SDS) to remove chromium (Cr) from an aqueous solution effectively. The successful synthesis of the SDS-chitosan was proven through characterization, which were carried out using by scanning electron microscopy–energy dispersive X–ray spectroscopy (SEM-EDS), Fourier transform-infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). The adsorption of Cr on the SDS material was investigated by varying experimental conditions such as pH, contact time and adsorbent dosage. The maximum adsorption capacity of SDS-chitosan for Cr(III) was estimated to be 3.42 mg·g−1 and 3.23 mg·g−1 for Cr(VI). Based on the results of adsorption kinetics and isothermal models, the adsorption process conform to the pseudo-second-order and Langmuir isotherm models. This indicates that the adsorption of Cr on SDS-chitosan is mainly dominated by chemical adsorption and monolayer reaction. In addition, according to thermodynamic analyses, the adsorption of Cr is an endothermic reaction. These results show that the new adsorbent has obvious application prospect for removing Cr.",book:{id:"11216",title:"Sorption - From Fundamentals to Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11216.jpg"},signatures:"Naoki Kano, Zou Ming, David Eva Vanessa Anak and Muhammad Nabil Md Sari"},{id:"81111",title:"Coconut Shell Charcoal Adsorption to Remove Methyl Orange in Aqueous Solutions",slug:"coconut-shell-charcoal-adsorption-to-remove-methyl-orange-in-aqueous-solutions",totalDownloads:34,totalDimensionsCites:0,doi:"10.5772/intechopen.102898",abstract:"Activated charcoal was prepared and characterized from residues of coconut peel (CACC) to remove by adsorption the Methyl Orange (AM) dye in aqueous solution. The charcoal was activated with phosphoric acid. The morphology and structure of the pores of the carbon obtained were analyzed by Scanning Electron Microscopy (SEM) and a surface analyzer. The adsorption data were evaluated by the BET, Langmuir and Freundlich isotherms, finding the Langmuir type I model. The surface area of the activated carbon was 526 m2/g with a pore volume of 0.234 cm3/g and an average pore diameter of 1.78 nm, according to BET, which indicates the presence of micropores. The calculated thermodynamic parameters showed that the adsorption of the AM dye in CACC is a spontaneous process at room temperature and that physisorption and chemisorption are probably involved. The adsorption tests were followed by UV–visible spectrophotometry. The effects of the adsorbate concentration (AM) and the heat treatment (450–500°C) with an air atmosphere were investigated, keeping constant the stirring time and the H3PO4/sample weight ratio. The results obtained indicate that the activated carbon obtained could be used as an alternative low-cost adsorbent in the removal of AM from effluents in aqueous solution.",book:{id:"11216",title:"Sorption - From Fundamentals to Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11216.jpg"},signatures:"Isabel Cristina Páez-Pumar Romer, Isabella Victoria Plazola Santana, Rosa María Rodríguez Bengoechea and Miguel Manuel Pérez Hernández"},{id:"80319",title:"Sorption Isotherms and Some Functional Properties of Cowpea Varieties Flour",slug:"sorption-isotherms-and-some-functional-properties-of-cowpea-varieties-flour",totalDownloads:58,totalDimensionsCites:0,doi:"10.5772/intechopen.101902",abstract:"In sub-Sahara, preservation of processed cowpea flour remained a challenge, and there are no standard isotherm conditions for drying cowpea flour. This study aims to define the optimum isotherm conditions for cowpea flour and assess their functional properties. Adsorption isotherms of three varieties of cowpea at temperatures 30, 40, and 50°C and in each case with six different applications depending on the constant relative humidity of the medium were executed. Water and oil absorption capacities including swelling index were determined. Results show that water content at equilibrium is inversely proportional to the temperature, and at the same temperature, the water content increases when water activity augments. The adsorption isotherms are of type II according to the fitted BET and GAB models. The absorption capacities ranged from 1.06 ± 0.01, 1.08 ± 0.02, and 1.09 ± 0.01(mL/g), respectively, for CS133, CS032, and control. However, the swelling index was significantly separated (P < 0.05). The adsorption isotherm curve of the sample CS032 at 50°C shows a stronger correlation (R2 = 0.9274) than the other varieties regardless of the mathematical isotherm model used. It can be concluded that depending on some functional properties of cowpea variety flour, these varieties seemed to behave separately vis-a-vis their sorption isotherm.",book:{id:"11216",title:"Sorption - From Fundamentals to Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11216.jpg"},signatures:"Issoufou Amadou"}],onlineFirstChaptersTotal:4},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"
\r\n\tGlobally, the ecological footprint is growing at a faster rate than GDP. This phenomenon has been studied by scientists for many years. However, clear strategies and actions are needed now more than ever. Every day, humanity, from individuals to businesses (public and private) and governments, are called to change their mindset in order to pursue a virtuous combination for sustainable development. Reasoning in a sustainable way entails, first and foremost, managing the available resources efficiently and strategically, whether they are natural, financial, human or relational. In this way, value is generated by contributing to the growth, improvement and socio-economic development of the communities and of all the players that make up its value chain. In the coming decades, we will need to be able to transition from a society in which economic well-being and health are measured by the growth of production and material consumption, to a society in which we live better while consuming less. In this context, digitization has the potential to disrupt processes, with significant implications for the environment and sustainable development. There are numerous challenges associated with sustainability and digitization, the need to consider new business models capable of extracting value, data ownership and sharing and integration, as well as collaboration across the entire supply chain of a product. In order to generate value, effectively developing a complex system based on sustainability principles is a challenge that requires a deep commitment to both technological factors, such as data and platforms, and human dimensions, such as trust and collaboration. Regular study, research and implementation must be part of the road to sustainable solutions. Consequently, this topic will analyze growth models and techniques aimed at achieving intergenerational equity in terms of economic, social and environmental well-being. It will also cover various subjects, including risk assessment in the context of sustainable economy and a just society.
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She is also a member of the Analytic Hierarchy Process (AHP) Academy.",institutionString:"Parthenope University of Naples",institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null,series:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580"},editorialBoard:[{id:"179628",title:"Prof.",name:"Dima",middleName:null,surname:"Jamali",slug:"dima-jamali",fullName:"Dima Jamali",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSAIlQAO/Profile_Picture_2022-03-07T08:52:23.jpg",institutionString:null,institution:{name:"University of Sharjah",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"170206",title:"Prof.",name:"Dr. Orhan",middleName:null,surname:"Özçatalbaş",slug:"dr.-orhan-ozcatalbas",fullName:"Dr. Orhan Özçatalbaş",profilePictureURL:"https://mts.intechopen.com/storage/users/170206/images/system/170206.png",institutionString:null,institution:{name:"Akdeniz University",institutionURL:null,country:{name:"Turkey"}}},{id:"250347",title:"Associate Prof.",name:"Isaac",middleName:null,surname:"Oluwatayo",slug:"isaac-oluwatayo",fullName:"Isaac Oluwatayo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRVIVQA4/Profile_Picture_2022-03-17T13:25:32.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},{id:"141386",title:"Prof.",name:"Jesús",middleName:null,surname:"López-Rodríguez",slug:"jesus-lopez-rodriguez",fullName:"Jesús López-Rodríguez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRBNIQA4/Profile_Picture_2022-03-21T08:24:16.jpg",institutionString:null,institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}},{id:"208657",title:"Dr.",name:"Mara",middleName:null,surname:"Del Baldo",slug:"mara-del-baldo",fullName:"Mara Del Baldo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLMUQA4/Profile_Picture_2022-05-18T08:19:24.png",institutionString:"University of Urbino Carlo Bo",institution:{name:"University of Urbino",institutionURL:null,country:{name:"Italy"}}}]},onlineFirstChapters:{paginationCount:26,paginationItems:[{id:"83087",title:"Role of Cellular Responses in Periodontal Tissue Destruction",doi:"10.5772/intechopen.106645",signatures:"Nam Cong-Nhat Huynh",slug:"role-of-cellular-responses-in-periodontal-tissue-destruction",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Periodontology - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11566.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"82654",title:"Atraumatic Restorative Treatment: More than a Minimally Invasive Approach?",doi:"10.5772/intechopen.105623",signatures:"Manal A. 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