Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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The scenario is dotted not only with old and new wounds but also with innovative strategies in an attempt to overcome existing delays. The chapters of the book are composed of scenarios full of case studies. The plans to be adopted to help the countries that have lagged behind fueled an intense debate since the obstacles to development, as evidenced by the extensive scientific literature available, now appeared to be the realities present in the socio-economic structures of a large number of villages. Although the data available are still few, it is assumed that the Covid-19 pandemic will make a landscape already full of criticalities even more fragile.",isbn:"978-1-83968-618-4",printIsbn:"978-1-83968-617-7",pdfIsbn:"978-1-83968-619-1",doi:"10.5772/intechopen.91068",price:119,priceEur:129,priceUsd:155,slug:"rural-development-education-sustainability-multifunctionality",numberOfPages:122,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"091e1f8266ec9fc776113a71c002cf7f",bookSignature:"Paola de Salvo and Manuel Vaquero Piñeiro",publishedDate:"February 2nd 2022",coverURL:"https://cdn.intechopen.com/books/images_new/10227.jpg",numberOfDownloads:783,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:0,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 31st 2020",dateEndSecondStepPublish:"September 28th 2020",dateEndThirdStepPublish:"November 27th 2020",dateEndFourthStepPublish:"February 15th 2021",dateEndFifthStepPublish:"April 16th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"289820",title:"Prof.",name:"Paola",middleName:null,surname:"de Salvo",slug:"paola-de-salvo",fullName:"Paola de Salvo",profilePictureURL:"https://mts.intechopen.com/storage/users/289820/images/system/289820.png",biography:"Paola de Salvo, Ph.D. in Social Systems and Public Policy Analysis, teaches sociology, promotion of territory, and urban and rural sociology at the Department of Political Science, University of Perugia, Italy. Her main research interest is focused on the study of local development, in particular to the study of territory development as a process that links the socio-economic and cultural aspects to sustainable development, which gives value to the sense of place, identity-local, narrations, and values.",institutionString:"University of Perugia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Perugia",institutionURL:null,country:{name:"Italy"}}}],equalEditorOne:{id:"289817",title:"Prof.",name:"Manuel",middleName:null,surname:"Vaquero Pineiro",slug:"manuel-vaquero-pineiro",fullName:"Manuel Vaquero Pineiro",profilePictureURL:"https://mts.intechopen.com/storage/users/289817/images/system/289817.png",biography:"Manuel Vaquero Piñeiro was awarded a Ph.D. in Modern and Contemporary History by the University of Cantabria (Spain); since 2006, he is a professor of economic history at the University of Perugia, Italy (manuel.vaqueropineiro@unipg.it). In 2010, he received the international award, Daria Borghese, for studies on the history of the Renaissance in Rome. Among his main research interests includes the socioeconomic transformation of agriculture.",institutionString:"University of Perugia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Perugia",institutionURL:null,country:{name:"Italy"}}},equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"281",title:"Sociology",slug:"sociology"}],chapters:[{id:"78539",title:"Dynamics of Rural Development in India",doi:"10.5772/intechopen.99887",slug:"dynamics-of-rural-development-in-india",totalDownloads:226,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"India is the second largest populous country in the world and more than half of its population lives in rural areas. This leads to widespread unemployment, low standard of living, inadequate productive skills and malnutrition in the country. In the developing countries especially like India, rural development is always been an important issue related to country’s economic progress. The rural development programmes are the key devices for the development of the rural areas in the country. As we know that, the people of rural area have seen difficulties from the time immemorial, the time has come to give them their deserving rights. India cannot shine without the shinning of the Rural India. National Development is almost synonymous with the Rural Development. This paper makes an attempt to measure actual performance and Government’s initiatives to accelerate the process of rural development through rural development programme in India and would be dealing with the changing life of the vulnerable people. The study reveals that the target number of houses to be constructed by the year 2021–2022, is 2.95 crore. The target set is to be achieved in phases and in the 1st phase 1 crore houses have been taken up for construction and in the 2nd phase 1.95 crore houses are being taken up for construction. 35.27 lakh houses have been constructed during 2020–2021 under Pradhan Mantri Awaas Yojana (PMAY-G) scheme. The pace of construction of PMGSY roads a nine years high of 135 kms per day in 2018–2019 as against an average of 73 kms during the period 2011 to 2014. Hence, the pace of construction has increased by 93%. Under PMGSY about 6, 26,910 Km road length completed where as 41000 Km road length constructed by using green technology and 14312 Km road length constructed by using plastic waste. MGNREGA has provided employment to 6.9 crore households by generating more than 305.71 crore person-days of wage employment covering 74.74 lakh works during financial year 2020–2021 and 5 crore works completed since inception. During COVID-19 pandemic, migrant workers were allowed to work under the scheme by being applying for job card. Approximately 1.44 crore Job Cards have been issued in FY 2020–2021. Total person-days generated in FY 2020–2021 have been 305.71 crore against approved LB for FY 2020–2021 of 333.09 crore. There has been 47% increase in person-days generated in comparison to FY 2019–2020. Further, the paper will give an idea how it will be beneficial for our country and how this little effort to rebuild the rural life and livelihood will make our country from developing to the developed country.",signatures:"Kazma Khan",downloadPdfUrl:"/chapter/pdf-download/78539",previewPdfUrl:"/chapter/pdf-preview/78539",authors:[{id:"331833",title:"Ph.D.",name:"Kazma",surname:"Khan",slug:"kazma-khan",fullName:"Kazma Khan"}],corrections:null},{id:"79838",title:"Poverty Reduction Strategies in Developing Countries",doi:"10.5772/intechopen.101472",slug:"poverty-reduction-strategies-in-developing-countries",totalDownloads:138,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The existence of extreme poverty in several developing countries is a critical challenge that needs to be addressed urgently because of its adverse implications on human wellbeing. Its manifestations include lack of adequate food and nutrition, lack of access to adequate shelter, lack of access to safe drinking water, low literacy rates, high infant and maternal mortality, high rates of unemployment, and a feeling of vulnerability and disempowerement. Poverty reduction can be attained by stimulating economic growth to increase incomes and expand employment opportunities for the poor; undertaking economic and institutional reforms to enhance efficiency and improve the utilization of resources; prioritizing the basic needs of the poor in national development policies; promoting microfinance programs to remove constraints to innovation, entrepreneurship, and small scale business; developing and improving marketing systems to improve production; providing incentives to the private sector; and, implementing affirmative actions such as targeted cash transfers to ensure that the social and economic benefits of poverty reduction initiatives reach the demographics that might otherwise be excluded.",signatures:"Collins Ayoo",downloadPdfUrl:"/chapter/pdf-download/79838",previewPdfUrl:"/chapter/pdf-preview/79838",authors:[{id:"224658",title:"Dr.",name:"Collins",surname:"Ayoo",slug:"collins-ayoo",fullName:"Collins Ayoo"}],corrections:null},{id:"79862",title:"Rural Development System in Nigeria and the Veering Locus from China’s Successful Strategies",doi:"10.5772/intechopen.101471",slug:"rural-development-system-in-nigeria-and-the-veering-locus-from-china-s-successful-strategies",totalDownloads:65,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Development of the rural areas calls for the provision of basic infrastructure and social amenities with a view to enhancing the quality of life in the environments. Attainment of rural development, however, depends on pragmatic and conscientious planning, and the political-will to have the development plans was effectively implemented. The essentiality of these actions is highly reflected in the revolutionary transformation of China’s rural system, with the resultant rapid economic growth and poverty reduction in the country, put at 8–9% per annum. Nigeria though had transformation-oriented rural development programs that are similar to those of China, none of the programs had visible or sustainable impacts in the country’s rural life. A critical analysis of the causal failure of Nigeria’s rural development programs in relation to the recorded successes in China shows that implementations of Nigeria’s rural development programs veered from the locus of the political-will that forms the strength of the recorded successes by China. Rethinking the paradigm of rural development in Nigeria unequivocally calls for modeling the country’s rural program implementations alongside the strength of the political-will adopted by China for attainment of the much desired rural transformation and sustainable development in Nigeria.",signatures:"Okanlade Adesokan Lawal-Adebowale",downloadPdfUrl:"/chapter/pdf-download/79862",previewPdfUrl:"/chapter/pdf-preview/79862",authors:[{id:"282579",title:"Dr.",name:"Okanlade",surname:"Adesokan Lawal-Adebowale",slug:"okanlade-adesokan-lawal-adebowale",fullName:"Okanlade Adesokan Lawal-Adebowale"}],corrections:null},{id:"78053",title:"Information for Rural Development in Africa",doi:"10.5772/intechopen.99707",slug:"information-for-rural-development-in-africa",totalDownloads:56,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Information is one of the major contributors to sustainable development in Africa. Access to relevant information would offset the emerging challenges in market supply chains, climate change and shrinking natural resources. At the same time, information can help to reduce poverty and creating economic opportunity for the majority of the rural populace in Africa. Further growth of ICT can enhance lifelong learning and impart skills and knowledge for individual and societal growth. This growth in skills and knowledge would also impact access and use of information in agriculture, health, transportation and economy. Information is very important in business by presenting data in a way that can be interpreted by management and support growth in the industry. Relevant information can support teaching and learning and improve quality sustainable education development in Africa. The chapter proposes that access and use of information in Africa can be enhanced by designing and improving the existing information infrastructures such as the internet and information centres. Legislations such as Access to Information and others would compel African governments and institutions to reconsider the role of information for sustainable development and consequently use it for decision making and competitive advantage.",signatures:"Austine Phiri",downloadPdfUrl:"/chapter/pdf-download/78053",previewPdfUrl:"/chapter/pdf-preview/78053",authors:[{id:"331001",title:"M.A.",name:"Austine",surname:"Phiri",slug:"austine-phiri",fullName:"Austine Phiri"}],corrections:null},{id:"76380",title:"Improving the Cognitive Development of Children in Rural Areas as Development Tool",doi:"10.5772/intechopen.97476",slug:"improving-the-cognitive-development-of-children-in-rural-areas-as-development-tool",totalDownloads:147,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Good health is a crucial requirement for every child for proper growth and development. To increase their future prospects the exact nutritional intervention is needed to boost the thinking and self-confidence of children. Adequate levels of omega-3 essential fatty acids are vital for children during pregnancy, breastfeeding, and few years post-weaning. This is not just for their perfect growth but including their cognitive development. Poverty levels continue to be high in rural areas and there are nutritional interventions that can be used to reverse the trends. However, omega-3 fatty acids, known to have a greater impact on brain development are not cheap and available in forms that are accessible by the rural poor. With the many complications attached to a rural lifestyle, little is known about culturally accepted local sources of omega-3 fatty acids. Therefore, alternative sources of nutritional intervention including the provision of eggs enriched with appropriate fatty acids, which are readily available, accessible, cheaper, and culturally accepted should be explored for children.",signatures:"Jacob Alhassan Hamidu, Charlisa Afua Brown and Mary Adjepong",downloadPdfUrl:"/chapter/pdf-download/76380",previewPdfUrl:"/chapter/pdf-preview/76380",authors:[{id:"331980",title:"Dr.",name:"Jacob",surname:"Alhassan Hamidu",slug:"jacob-alhassan-hamidu",fullName:"Jacob Alhassan Hamidu"},{id:"340947",title:"Ms.",name:"Charlisa",surname:"Afua Brown",slug:"charlisa-afua-brown",fullName:"Charlisa Afua Brown"},{id:"340948",title:"Dr.",name:"Mary",surname:"Adjepong",slug:"mary-adjepong",fullName:"Mary Adjepong"}],corrections:null},{id:"77441",title:"Rural Development on the Agricultural Institution Basis: Case of the Agricultural Development in Bali Province, Indonesia",doi:"10.5772/intechopen.97243",slug:"rural-development-on-the-agricultural-institution-basis-case-of-the-agricultural-development-in-bali",totalDownloads:64,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Presently, agricultural sector has great role in supporting and accelerating economic development in the developing countries. Development of agriculture is always related to the rural development since it is an integral part to rural economic development. Therefore, the agricultural and rural development should be designed to increase the productivity of land, crops, and human resources in order to improve the welfare of rural village. The implementation of agricultural and rural development should be done by involving the rural institutions. Several problems have still found in the agricultural and rural development that should be properly managed by considering various aspects. In Bali Province, the implementation of agricultural and rural development can be conducted to develop tourism, industry, environment, and other economic activities. The agro-tourism, agro-industry, integrated farming system (system of crops and livestock) and village-owned enterprises could be developed as a response of the rural development.",signatures:"Gede Sedana",downloadPdfUrl:"/chapter/pdf-download/77441",previewPdfUrl:"/chapter/pdf-preview/77441",authors:[{id:"330989",title:"Associate Prof.",name:"gede",surname:"Sedana",slug:"gede-sedana",fullName:"gede Sedana"}],corrections:null},{id:"79774",title:"Perspective Chapter: Water, Natural Disasters and Socio-Economic Development in the Early 21st Century",doi:"10.5772/intechopen.101628",slug:"perspective-chapter-water-natural-disasters-and-socio-economic-development-in-the-early-21st-century",totalDownloads:91,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter investigates the complex relationship between socio-economic development and environmental sustainability by focusing on one of the most vital natural phenomena: the water cycle. Considering the current public awareness of climate change and the growing number of natural disasters, focusing on this topic provides a better understanding of weaknesses and bottlenecks that 21st-century society faces daily. This work presents three case studies, different from each other but conceptually interconnected. The first case concerns the situation of lakes in the world, whose water in many cases is at risk of disappearing. In the second instance, we present the growing socio-economic risks generated by floods. Nowadays, floods play a fundamental role in influencing socio-economic development due to the dislocation of economic activities in Southeast Asian countries. Finally, we discuss desertification affecting large areas of the African continent. One aspect of great interest is the Grande Muraille Verte project promoted by numerous countries. Reforestation of large arid areas is the main issue; the attempt is to support local communities to implement agricultural and livestock activities. Socio-economic and environmental sustainability and resilience are the main challenges that countries, regions and local communities are facing.",signatures:"Manuel Vaquero Piñeiro and Paola de Salvo",downloadPdfUrl:"/chapter/pdf-download/79774",previewPdfUrl:"/chapter/pdf-preview/79774",authors:[{id:"289820",title:"Prof.",name:"Paola",surname:"de Salvo",slug:"paola-de-salvo",fullName:"Paola de Salvo"},{id:"289817",title:"Prof.",name:"Manuel",surname:"Vaquero Pineiro",slug:"manuel-vaquero-pineiro",fullName:"Manuel Vaquero Pineiro"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5810",title:"Socialization",subtitle:"A Multidimensional Perspective",isOpenForSubmission:!1,hash:"bfac2e9c0ec2963193e9d15d617c6a01",slug:"socialization-a-multidimensional-perspective",bookSignature:"Rosalba Morese, Sara Palermo and Juri Nervo",coverURL:"https://cdn.intechopen.com/books/images_new/5810.jpg",editedByType:"Edited by",editors:[{id:"214435",title:"Dr.",name:"Rosalba",surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10656",title:"Intellectual Property",subtitle:null,isOpenForSubmission:!1,hash:"135df9b403b125a6458eba971faab3f6",slug:"intellectual-property",bookSignature:"Sakthivel Lakshmana Prabu and Timmakkondu Narasimman Kuppusami Suriyaprakash",coverURL:"https://cdn.intechopen.com/books/images_new/10656.jpg",editedByType:"Edited by",editors:[{id:"91590",title:"Dr.",name:"Sakthivel",surname:"Lakshmana Prabu",slug:"sakthivel-lakshmana-prabu",fullName:"Sakthivel Lakshmana Prabu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10785",title:"Suicide",subtitle:null,isOpenForSubmission:!1,hash:"f8ad2d5c55e4551a2e1046369cae627f",slug:"suicide",bookSignature:"Robert W. 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\r\n\tIn eukaryotic cells, the endoplasmic reticulum is an organelle adjacent to the nuclear membrane. This organelle is essential for calcium homeostasis and lipid biosynthesis and protein assembly, folding, and post-translational modification. The interplay between the endoplasmic reticulum membrane and the outer mitochondrial membrane, called mitochondria-associated endoplasmic reticulum membranes (MAMs), permits a wide range of cellular activity, including the division and fusion of mitochondria and the dynamic passage of lipids, glycogen, and calcium ions. \r\n\tIt has been established that energy/nutrient depletion, calcium flux injury, or oxidative stress disrupt endoplasmic reticulum homeostasis and even induce accumulation of misfolded/unfolded proteins leading to endoplasmic reticulum stress. Under endoplasmic reticulum stress conditions, an adaptive mechanism of coordinated signaling pathways, defined unfolded protein response (UPR), is activated to return the endoplasmic reticulum to its healthy functioning state. The aging causes a decrease of the protective adaptive response of the UPR and an increase of the pro-apoptotic pathway together with endoplasmic reticulum ultrastructural injury. Controlling endoplasmic reticulum stress response, maintaining the appropriate endoplasmic reticulum ultrastructure and homeostasis, and retaining mitochondria interplay are crucial aspects for cellular health.
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She is currently engaged as a researcher for the Scientific-Disciplinary Sector BIO/16 Human Anatomy at the Anatomy and Pathophysiology Division, Department of Clinical and Experimental Sciences, University of Brescia (Italy).\r\nDr. Favero focuses on aging-related morphological dysfunctions as the prelude to various pathophysiological processes in her research programs. The central hypothesis is that natural antioxidants and, in particular, melatonin may act as molecular "switches" that modulate cells and tissues by suppressing, at various levels, oxidative stress and inflammatory signalling cascades. These research approaches represent powerful tools for developing innovative preventive strategies and identifying novel prognostic biomarkers for several diseases. 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1. Introduction
\n
Most of the analysis techniques described in this chapter were recommended by the Agência Nacional de Vigilância Sanitária (the National Health Surveillance Agency) (ANVISA) [1], including the Official Analytical Methods for the Microbiological Analysis and Control of Products from Animal and Water Sources [2], those of the American Public Health Association; described in the fourth edition of the Compendium of Methods for the Microbiological Examination of Foods [3–5], the International Commission on Microbiological Specifications for Foods [6, 7]; the Food and Drug Administration (FDA), recommended by the Ministry of Agriculture for the analysis of foods of animal origin, in accordance with Normative Instruction number 62, dated August 26th, 2003 [2]; the Food Safety and Inspection Service of the US Department of Agriculture [8], the Association of the Official Methods of Analysis of AOAC International [9], the Bacteriological Analytical Manual (FDA) [10], the Microbiology Laboratory Guidebook [11] and the latest editions of the International Organization for Standardization [12, 13].
\n
Among the various parameters that indicate the quality and safety of honey, the most important are those that define its microbiological characteristics. Honey, as with any other raw material of vegetable or animal origin, naturally presents microbial contaminants of commercial importance formed by microorganisms adapted to the characteristics of the honey, such as high-sugar content, low acidity and the presence of natural antimicrobial substances. Because of these characteristics, the microbial load in honey is generally low, below 102 CFU/g, and can even reach 103–104 CFU/g. Consequently, it can cause undesirable changes by reducing the shelf-life of the product. It presents floral indicators of the possible presence of pathogenic microorganisms, and so can be harmful to the health of the consumer. Protecting food products from any kind of contamination or adulteration which can cause harm to public health or economic disorder is a global concern [8] and specific methods of analysis are required to evaluate this type of raw material. Moreover, the risks represented by the poor handling conditions used by workers responsible for the harvest, extraction and preparation of this product require effective interventions and procedures to minimize these risks [14].
\n
Aiming to control the quality of honey, the World Trade Organization recommends the adoption of standards, guidelines and norms developed by Codex Alimentarius—the revised codex standard for honey 2001 [15]. This is an international public agency created by the Food and Agricultural Organization (FAO) and the World Health Organization (WHO) [16], both of which form part of the United Nations Organization (UNO). MERCOSUL GMC resolution n° 15 1994 approved the Technical Regulations for the Identity and Quality of Honey, based on resolutions n° 18 (1992) and n° 91 (1993) of the Common Market Group [17], in which honey can contain a maximum of 100 colony forming units of fungus per gram (CFU/g). Normative instruction no 11 approved, on 20 October 2000, the Technical Regulations for the Identity and Quality of Honey [18] and normative instruction no 3 dated January 19th 2001 approved the Technical Regulations for the Identity and Quality of bee apitoxin, beeswax, royal jelly, lyophilized royal jelly, bee pollen, propolis and propolis extract [19], as previously microbiological standards had not been established for these apiculture products. To ensure the credibility of the results however, some steps must be observed. The methods in this chapter are described as simply as possible in order to be accessible to fully qualified professionals, lab technicians and students with varying levels of education and training. This chapter provides comprehensive material presented in a didactic manner, with texts and diagrams that facilitate understanding. The basic techniques of microbiology described are accompanied by a brief overview of the microorganism researched in order to provide a solid theoretical basis, which will be of great value for understanding the method and interpretation of results. This chapter, therefore, aims to present the most commonly used techniques for assessing the microbiological characteristics of honey to identify its contaminant flora, its significance and its control in this type of food.
\n
1.1. Sampling plan for the microbiological analysis of honey lots
\n
Sampling plans allow the evaluation of the microbiological conditions of honey lots. These were proposed by the International Commission on Microbiological Specifications for Foods [6] and their application supports the acceptance or rejection of a honey lot as a whole, describing the hygienic sanitary conditions under which this food was obtained, processed, stored, distributed for consumption, as well as its shelf life and the risk posed to consumer health.
\n
For the microbiological analysis of a honey lot, it is necessary to define some important concepts, such as: lot, n, c, m and M. A ‘lot’ is the total units of honey pots produced, handled or stored under the same conditions, within a certain period; ’n’ is the number of units taken randomly from a lot to be analysed individually. For honey, ’n‘ is equal to five sample units and constitutes a representative sample of the lot; ’m‘ is the set of microbiological standards established for a microorganism in a given food; ’c‘ is the maximum acceptable number of units in which microbial counts in the lot are above the minimum threshold (m) and below the maximum tolerated limit (M); ’ M‘ is the tolerable limit, above standard, which can be reached by (c) sample units, but cannot be exceeded by either [6].
\n
Brazilian legislation on the microbiological requirements of food includes Ordinance no 101 of 1993 of the Ministry of Agriculture, Livestock and Supply and RDC-12 Resolution 2001 of the National Health Surveillance Agency of the Ministry of Health [1]. In the case of honey (molasses and similar) a value of n = 5 is adopted, while values of c, m and vary according to the microorganism considered: coliforms at 45°C/g (n = 5, c = 2, m = 10 and M = 102) and Salmonella sp/25 g (n=5 c = 0; m = absent) under this legislation are more flexible than the levels established by Mercosul [17] in which honey must meet the following microbiological characteristics: Coliforms at 35°C/g (n = 5, c = 0, m = 0); Salmonella spp - Shigella spp/25 g (n = 10, c = 0, m = 0); Fungi and Yeast CFU/g (n = 5, c = 2 m=10, M = 100). Therefore, a maximum of 100 colony forming units of fungus per gram of honey (CFU/g) is acceptable.
\n
For the analysis of Salmonella sp/25 g, a two-class plan is applied, as this trial investigates the presence or absence of this microorganism. In this case, ‘c‘ is equal to zero, absence is acceptable and the presence of any sample unit is unacceptable. In these tests a single sample analysis is performed. For analysis of coliform 45°C, a three-class plan is applied, which classifies lots into three categories: acceptable, intermediate and unacceptable. In this case, the standard is not absence but values within a range between m and M. In the two-class plan, M separates acceptable from unacceptable lots. In a three-class plan this value separates an acceptable lot from an intermediate lot [10, 11].
\n
1.2. Transport of samples
\n
Samples of food concentrates such as honey are microbiologically stable and can be transported and stored at ambient temperature. Nevertheless, they should be protected against moisture and excessive heat [20].
\n
1.3. Analytical unit
\n
This is the amount of food sample used in conducting one or more tests. The sample unit must be greater than that required for analysis, with sufficient quantities for the counter-sample.
\n
In Brazil, tests for the quantitation of microorganisms in honey comprise mould and yeast counts, the count of total and faecal coliforms and Salmonella analysis, the trials of which are usually done with an analytical unit of 25 g of honey (in special cases at least 10 g of honey can be used). Analytical units of 25 g meet the requirements of ISO 6887-1 [20], and those of the Compendium, for all tests. Two analytical units are required for analysis of a honey sample—one for mould and yeast quantification, total and thermotolerant coliform count and the other test for the absence or presence of Salmonella.
\n
1.4. Homogenization of the honey sample and withdrawal of the analytical unit
\n
Disinfect the area outside the packaging with 70% ethanol and remove the jar lid aseptically. Observe and note the presence of abnormalities in the packaging or in the internal content such as bloating, leakage, the presence of foreign bodies, odour and/or strange appearance.
\n
Before the withdrawal of analytical units, the content of the sample should be homogenized to ensure that the removed portion is representative of all the material. In the case of honey in a jar with enough room for agitation, the package should be inverted 25 times. If there is no free space for agitation, use a second sterile vial and transfer the sample from one vial to another three times. Remove the analytical unit with a sterile spatula (ISO 6887-5: 2010) [21].
\n
2. Description of methods
\n
To increase the reliability of the results obtained, all tests should be performed in triplicate, following the methods described below.
\n
2.1. Total and thermotolerant coliforms
\n
The bacteria are in Gram-negative bacilli form, are facultative, not sporogenic anaerobes, capable of fermenting lactose with gas production, and are temperature dependent. Total coliforms, also known as coliforms at 35°C, are a sub-group of the Enterobacteriaceae family. The second edition of Bergey\'s Manual of Systematic Bacteriology [22] includes 44 genera and 176 species in this sub-group. The total coliform group includes only enterobacteria that can ferment lactose with the production of gas, for 24–48 hours at 35°C. More than 20 species fall into this category, including bacteria originating from the gastrointestinal tract of humans and other warm blooded animals such as Escherichia coli, and non-enteric bacteria such as Citrobacter, Enterobacter, Klebsiella and Serratia, among others [23].
\n
Lactose fermentation capacity is analysed through the formation of gas and/or acid in the lactose-containing culture media. These characteristics are used in traditional methods of total coliform counting.
\n
With modern methods, it is possible to directly detect the activity of the β-galactosidase enzyme involved in the fermentative metabolism of lactose, incorporating the substrates for the enzyme in culture media. One of these substrates is ONPG (ortho-nitrophenyl-β-D-galactopyranoside) which when degraded by β-galactosidase results in a product that is yellow in colour. It also possesses the X-GAL (5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside) substrate, which results in a product with an intense blue staining, and Salmon-Gal (6-chloro-3-indolyl-β- D-galactopyranoside), whose degradation product is a salmon red colour [23, 24].
\n
The thermotolerant coliform group, also known as coliforms at 45°C but usually called faecal coliforms, is a subgroup of total coliforms which are restricted to members capable of fermenting lactose in 24 hours at 44.5–45.5°C with gas production [23–25]. While this definition aims in principle to select only enterobacteria that originate from the gastrointestinal tract (E. coli), it is currently known that the group includes members of non-faecal origin (various strains of Klebsiella pneumoniae, Pantoea agglomerans, Enterobacter aerogenes, Enterobacter cloacae and Citrobacter freundii) [23].
\n
Escherichia coli have as their natural habitat the intestinal tract of warm-blooded animals, but can be introduced into food from non-faecal sources. They can grow in eosin methylene blue agar where their growth characteristics allow them to be distinguished from other coliforms [23, 25–27].
\n
These bacteria by themselves do not generally represent a major risk, but can indicate poor quality food that may contain harmful agents. According to the International Commission on Microbiological Specifications for Foods [7], total coliforms, thermotolerant coliforms and Escherichia coli are microorganisms with a low or indirect risk to health. Their presence may indicate inadequate hygiene and sanitation, demonstrating failures during post-processing, as they are easily inactivated by sanitizers and heat treatment.
\n
Method: This is based on the most probable number (MPN) technique involving inoculation in tubes with lauryl sulphate tryptose broth (LST). This technique is the most used for coliform-bacteria counting. The most probable number in a sample is determined by using a confidence interval table at 95% probability for the various positive tube combinations in three or five tube series [27, 28].
\n
This method enables the density of the viable organisms present in a sample under analysis to be estimated and is based on the principle that the bacteria present in a sample can be separated by agitation, resulting in a suspension of bacterial cells, evenly distributed in the sample. It is based on the inoculation of an increasing sample volume in a suitable culture medium for the growth of microorganisms, with each volume being inoculated into a series of tubes. Inoculum is obtained by sampling successive dilutions, the streaking of which provides positive and/or negative results allowing the calculation of the density of bacteria investigated by the application of probability calculations.
\n
According to the methods studied, total and thermotolerant coliform and E. coli counting by the most probable number method is conducted in four steps [5, 7, 9, 23, 26–28]:
\n
Presumptive test for total coliforms: using lauryl sulphate tryptose broth the observation of growth with gas production is considered suspect (presumptive) for the presence of coliforms. The presence of the surfactant in the lauryl sulphate tryptose broth inhibits the growth of the cytoplasmic membrane of Gram-positive bacteria and enables the presence of lactose fermentation, which releases carbon dioxide. The presence of this gas is evident in the Durham tube.
Confirmation of total coliform test: using brilliant green bile broth (BGBB) there is notable development of bacteria of the coliform group, which is again confirmed by the formation of gas. This occurs because this broth is selective due to the presence of bovine bile and a triphenylmethane dye derivative which inhibits Gram-positive bacteria and sporulated lactose fermenting bacteria. This step of the examination reduces the possibility of false positive results arising from the activity of sporulated bacteria and Gram-positive lactose fermenting bacteria. Observation of growth through gas production in brilliant green bile tubes is considered confirmatory for the presence of total coliforms.
Confirmation test for thermotolerant coliforms: this method uses Escherichia coli broth (EC) containing lactose, a selective medium containing a mixture of phosphate which maintains the pH of the medium at an appropriate amount. This selectivity is due to bile salts, which inhibit the growth of the Gram-positive microorganism. If there is gas formation in these conditions the thermotolerant coliform is confirmed [29]. The positive Escherichia coli tubes for thermotolerant coliforms are suspect for the presence of E. coli.
Confirmation testing for E. coli: this method uses eosin methylene blue agar, which is a selective differential medium that distinguishes E. coli from other thermotolerant coliforms. If there is development of typical colonies of E. coli in this agar, these colonies are isolated for the biochemical proof of indole, methyl red, Voges-Proskauer and citrate (IMViC).
\n
Sample preparation: weigh 25 g of honey and add to 225 mL of peptone water 0.1% and homogenize the sample. This provides a 10−1 dilution; where 1 mL of the same corresponds to 0.1 g of the sample. A quantity of 1.0 mL of this solution (10−1) is transferred using a new sterile pipette to a 9.0 mL of dilution water, thus obtaining a second decimal dilution (10−2), where 1 mL corresponds to 0.01 g of the sample. In the same way, a 10−2 dilution provides a 10−3 solution (Figure 1).
Presumptive Test: for presumptive evidence, 1 mL of the three subsequent dilutions should be inoculated in a series of three test tubes containing broth lauryl sulphate tryptose, with one series for each dilution. The tubes should be incubated at 35°C for 24–48 hours. If after this time there is turbidity of the medium and the formation of gas in the Durham tube, the presumptive test is positive for the presence of coliforms and should be subjected to confirmatory tests. If there is no turbidity in the medium or gas formation during the incubation period, the analysis ends at this stage and the test result is negative.
Confirmation test of total and thermotolerant coliforms: to confirm the total coliform transfer, with a previously heated and cooled platinum inoculation loop, three loops from each positive tube and inoculate in a corresponding tube containing the bright green bile broth and incubate at 35°C for 24–48 hours. At the same time, perform a confirmatory test for the coliforms, similarly transferring the same ratios to tubes of Escherichia coli broth and incubating at 44.5°C for 24–48 hours. After this time, for the two tests, the formation of gas is observed in the Durham tube. If there is clouding of the medium and gas formation in the Durham tubes in the bright green bile broth the presence of total coliforms is confirmed, while the Escherichia coli broth confirms faecal coliforms. If there is no turbidity of the medium and no gas formation in the Durham pipes, the test is considered negative.
Biochemical test to confirm E. coli: the tubes that present positive results for thermotolerant coliforms and/or tubes positive for coliform 35°C should be plated with a platinum loop with streaking on the surface of the Levine eosin methylene blue agar culture medium. The plates should be incubated at 35°C for 24 hours. Two colonies characteristics of E. coli (which are semi-nucleated with black centres and the presence or absence of metallic green brightness) must be isolated and subjected to biochemical tests of indole, methyl red, Voges-Proskauer and citrate [29]. The cultures with the profiles + + - - (biotype 1) or - + - - (biotype 2) are considered confirmed (Figure 2).
Figure 1.
Decimal dilutions prepared (10−1; 10−2 and 10−3) from honey sample.
Figure 2.
Presumptive and confirmatory tests of coliforms at 35 and 45°C.
\n
Reading of test using most probable number (MPN): the most probable number technique is based on the statistical probability related to the frequency and occurrence of the most probable positive results in terms of the real number of microorganisms present. Three sets of three tubes are inoculated, employing dilutions 0.1; 0.01 and 0.001 mL/g of honey. Thus, the number of tubes per series of three consecutive dilutions is three, giving a total of nine tubes. The number of microorganisms in the original sample is determined using the most probable number tables (Tables 1 and 2), according to the Brazilian Association of Technical Standards [30].
\n
Combination of positive tubes
Combination of positive tubes
0.1g
0.01g
0.001g
MPN
0.1g
0.01g
0.001g
MPN
0
0
0
<3
2
0
0
9.1
0
0
1
3.0
2
0
1
14.0
0
0
2
6.0
2
0
2
20.0
0
0
3
9.0
2
0
3
26.0
0
1
0
3.0
2
1
0
15.0
0
1
1
6.1
2
1
1
20.0
0
1
2
9.2
2
1
2
27.0
0
1
3
12.0
2
1
3
34.0
0
2
0
6.2
2
2
0
21.0
0
2
1
9.3
2
2
1
28.0
0
2
2
12.0
2
2
2
35.0
0
2
3
16.0
2
2
3
42.0
0
3
0
9.4
2
3
0
29.0
0
3
1
13.0
2
3
1
36.0
0
3
2
16.0
2
3
2
44.0
0
3
3
19.0
2
3
3
53.0
1
0
0
3.6
3
0
0
23.0
1
0
1
7.2
3
0
1
39.0
1
0
2
11.0
3
0
2
64.0
1
0
3
15.0
3
0
3
95.0
1
1
0
7.3
3
1
0
43.0
1
1
1
11.0
3
1
1
75.0
1
1
2
15.0
3
1
2
120.0
1
1
3
19.0
3
1
3
160.0
1
2
0
11.0
3
2
0
93.0
1
2
1
15.0
3
2
1
150.0
1
2
2
20.0
3
2
2
210.0
1
2
3
24.0
3
2
3
290.0
1
3
0
16.0
3
3
0
240.0
1
3
1
20.0
3
3
1
460.0
1
3
2
24.0
3
3
2
110.0
1
3
3
29.0
3
3
3
>1100.0
Table 1.
Most probable number (MPN) with 95% confidence limits for various combinations of positive results, using three tubes per series to inoculate 1 mL of dilutions 0.1; 0.01 and 0.001 g of honey/ml.
Examples using dilution (g) combining 0.1; 0.01; 0.001 g/mL.
\n\n
\n
2.2. Yeast and mould counting
\n
Counting of viable fungi is applicable to honey as it is an acidic food, with a pH of less than 4.5 and relatively low moisture. Fungi are affected little by variations in the pH range 3.0–8.0. The moulds grow below pH 2.0 and several yeasts below 1.5. When the pH deviates from the optimal, which is generally close to 5.0, the growth rate of colonies decreased and, if there are other inhibition factors, such as water or nutrient temperature activity, its restrictive effect on the growth rate becomes stronger [23].
\n
Its presence at high levels in honey can provide various types of information; for example, the poor hygienic conditions of equipment, multiplying in the product due to failures in processing and/or storage. MERCOSUL GMC resolution n° 15 of 1994 approved the Technical Regulations for the Identity and Quality of honey, in view of resolutions n° 18 of 1992 and n °91 of 1993 of the Common Market Group [17], in which, in terms of hygiene, honey must be free of foreign inorganic or organic substances in its composition, such as insects, larvae and grains of sand, and should not exceed the maximum levels tolerable for microbiological contamination or toxic waste. Its preparation should be carried out according to the General Principles of Food Hygiene recommended by the Codex Alimentarius Commission—FAO/WHO [15]. In terms of fungi, up to 100 colony forming units per gram are tolerated in honey (CFU/g) [17].
\n
Moulds are filamentous, multicellular fungi, and may be present in the soil, air, water and raw organic decomposition. They are generally aerobic and less demanding than other yeasts in terms of humidity, pH, temperature and nutrients. They can absorb any carbon source derived from food. As a nitrogen source, they can use nitrate, ammonia and organic nitrogen. They only grow on the surface of honey when in contact with air, as it is a food rich in carbohydrates and acids [23, 31].
\n
Yeasts are classified as non-filamentous fungi whose form is unicellular and can be spherical, ovoid, cylindrical or triangular. They are usually spread by insect vectors and by wind and air currents [32]. For growth yeasts require moisture more than that required by moulds and less than that required by bacteria, with an ideal temperature range for growth at around 25 and 30°C. The growth of the osmophilic yeasts which are part of the micro-biota of importance of honey is favoured as the liquid substrate provides a greater opportunity for the development of anaerobic conditions, due to possessing the ideal acid pH for use in the fermentation by which the yeast is transformed into sugar, which is used as an energy source in alcohol, when the water activity value is at least 0.65 [31, 33]. According to Pitt and Hocking [34], most osmophilic yeasts are of the genus Zygosaccharomyces, including Z. rouxi, Z. bailii and Z. bisporus. To control these microorganisms in honey the application of good hygiene practices is required, and it must be ensured that water activity or moisture content is within acceptable limits [15]. When the honey extracted from the beehive has a lower water activity than 0.60 the multiplication of osmophilic yeast does not occur.
\n
Method: based on the verification of the ability of these microorganisms to develop in a culture media with a pH around 3.5 and incubation temperature 25 ± 1°C. The use of acidified mediums selectively favours the growth of fungi, inhibiting most of the bacteria present in food [2].
\n
Plate preparation process: dilute the potato dextrose agar (PDA) medium; cook in a water bath to 46–48°C; acidify the medium to pH 3.5 by adding 1.5 mL of tartaric acid 10% solution for each 100 mL of the medium; pour 15–20 mL into the plates; wait to solidify on a flat surface; identify the plates, before use, dry the semi-open plates in a kiln at 50°C for about 15 minutes or in a laminar flow exposing the surface for the time required for complete drying.
Sample preparation: aseptically remove 25 g of the sample, open the packaging in an aseptic chamber, close to the flame of the Bunsen burner and taking care so that all the tools and utensils used are sterilized and flamed at the time of use.
Preparation of dilutions: add 225 mL of peptone water 0.1% and mix, obtaining the first dilution (10−1). For the second dilution (10−2), transfer 10 mL of the first dilution to 90 mL of peptone water 0.1% and for the third dilution (10−3), using the same procedure (Figure 3).
Inoculation: carry out surface plating, adding 0.1 mL of each dilution to the plates with the potato dextrose agar or dichloran-glycerol agar; with the help of Drigalski spatula or a hockey stick shaped rod spread the inoculum over the agar surface until its complete absorption.
Incubation: incubate at 25°C for 5 days, without inverting the plates, in stacks of no more than three plates, in the dark. After incubation, check the presence of colonies of yeasts and moulds, count them and carry out the calculations (Figure 4).
Count the colonies and calculate results: select the plates with 15–150 colonies with a colony counter. In the selected plate count and note separately the colonies with a filamentous appearance, characteristic of moulds. On the same plate count the other colonies, which can be yeast or bacteria, eventually capable of growth. Select at least five of these colonies and verify the morphology of the cells with a microscope observing if the culture is of yeasts, bacteria or a mixture of both. Colonies which present yeasts or mixtures of yeasts and bacteria are considered confirmed.
Figure 3.
Procedure for the preparation of dilutions 10−1; 10−2 and 10−3.
Figure 4.
Procedure for the preparation and inoculation of dilutions 10−1; 10−2 and 10−3 on the plates of potato dextrose agar.
\n
Determine the number of yeast colonies on the plate based on the confirmed percentage. For example, of 30 colonies counted, five were submitted to confirmation and three were confirmed as yeast (60%), so the number of yeast colonies on the plate is 30 × 0.6 = 18. To calculate the number of colony forming units per gram (CFU/g) of yeasts and moulds, multiply the number of colonies by ten and by the inverse of the dilution. The total calculation of both is carried out by adding the number of mould colonies and the number of colonies confirmed as yeast and multiplying by the inverse of the dilution according to Eq. (1).
\n
CFU/g = number of colonies x Dilution reverse x 10E1
\n
For example:
Dilution 10−2 (inoculated 0.1 mL)
Total typical colonies of mould on plate = 30
Presumptive colonies of yeast on plate = 40, five to submit for confirmation, confirmed four (80%)
Total yeast colonies on plate = 40 × 0.8 = 32
CFU/g moulds = 30 × 102 × 10 = 3.0 × 104
CFU/g yeast = 32 × 102 × 10 = 3.2 × 104
CFU/g of yeasts and moulds = (30+32) × 102 × 10 = 6.2 × 104
\n
2.3. Salmonella sp
\n
Species of the Salmonella genus are agents of human and animal intestinal infections. Among the agents of foodborne illnesses, Salmonella is one of the most responsible for fatalities and clinical complications. Moreover, the high morbidity and mortality rate and incidence in humans and animals result in significant spending on medications and hospitalizations. The inspection and monitoring of food is aimed at the control and prevention of members of this group and the effects of their presence in food. Compliance with good manufacturing practices and control programs should include a certificate of compliance with the measures adopted, especially for this bacterial genus [23].
\n
Method: the method for detecting Salmonella in food is based on its presence or absence, developed to guarantee detection even in unfavourable situations. The procedures recommended by various regulatory bodies basically follow five steps that can be applied to any type of food [2, 3, 5, 10, 11, 13].
\n
Pre-enrichment in non-selective broth: the objective is the recovery of injured cells, obtained by incubating the sample in non-selective conditions for at least 18 hours. The most commonly used medium is buffered peptone water and lactose broth. This step consists of aseptically weighing 25 g of honey in 225 mL of BPW 1% and incubating at 35°C for 18–24 hours. Finally, adjust the pH to 6.8–6.9 (Figure 5)
Selective enrichment broth: the objective is to inhibit the multiplication of the accompanying micro-biota and promoting the preferential increase of the number of Salmonella cells by incubating a pre-enriched sample in selective broth for 18–24 hours. The use of two different media is recommended because of the resistance of Salmonella to different selective agents of the medium which varies from strain to strain. The most recommended means are Rappaport-Vassiliadis soy broth (RVS) and selenite cystine broth (SC) as follows (Figure 6):\n
Inoculation in Rappaport Vassiliadis broth: pipette out 1 mL of pre-enriched samples and transfer to tubes containing 10 mL Rappaport Vassiliadis broth. Incubate the tubes at 41 ± 0.5°C in a water bath, preferably with continuous agitation or circulation of water, for 24–30 hours.
Inoculation in selenite cystine broth: pipette out 1 mL of pre-enriched samples and transfer to tubes containing 10 mL of selenite cystine broth. Incubate the tubes at 41 ± 0.5°C in a water bath for 24–30 hours.
Selective differential plating: the aim is to promote the preferential development of Salmonella colonies, whose typical characteristics differentiate them from competitors, for subsequent biochemical and serological confirmation. The use of more than one type of culture medium is recommended. The most commonly used media are those that differentiate Salmonella by the non-fermentation of lactose and by H2S production, such as hektoen enteric agar (HE), xylose lysine deoxycholate agar (XLD) and xylose lysine tergitol-4 agar (XLT-4). As there are Salmonella strains which ferment lactose or do not produce H2S, it is important that the second or third plating medium is not based on these characteristics. One option is the brilliant green phenol red lactose sucrose agar (BPLS) or brilliant green agar (BG) based on the fermentation of lactose but not the production of H2S, and bismuth sulphite agar (BS), which is based on H2S production and not lactose fermentation. Rambach agar can also be used in this step. Add 0.1 mL of novobiocin solution 4% to 100 mL of brilliant green phenol red lactose sucrose agar. Incubate all plates at 35°C for 24 hours (Figure 7).
Isolation and selection: depending on the medium, the Salmonella colonies present different colours after the incubation period: in hektoen enteric agar the colonies are green or bluish green, revealing or otherwise the production of hydrogen sulphide (H2S) (dark centre); in xylose lysine deoxycholate agar the colonies are red with the production of hydrogen sulphide (H2S) (dark centre); in xylose lysine tergitol-4 agar the colonies are red; in brilliant green phenol red lactose sucrose agar the colonies are colourless or have a pink colour, between clear and slightly opaque and when surrounded by fermenting microorganisms of lactose, may present a greenish-yellow colour. With brilliant green agar the colonies are red; and with bismuth sulphite agar (BS) the colonies are winged with a black centre.
Presumptive biochemical identification (screening methods): the aim is to verify that the typical colonies obtained from the plates are truly Salmonella, specifically strains of Salmonella enterica subsp. Enterica, which is the main target for food analysis and which has a biochemical profile which is considered typical in detection assays (Table 3). Once the suggestive colonies have been selected by the indicating methods, they will be transferred to the screening mediums:\n
Triple sugar iron agar (TSI Agar): this medium is used to differentiate Gram-negative rods based on fermentation and the gas production from the carbohydrates: glucose, lactose and sucrose and the production of hydrogen sulphide. For the test, inoculate the triple sugar iron agar by deep, grooved stabbing motions and in inclined surface of the bevel. Incubate at 36°C for 18–24 hours. In the presence of Salmonella, the glucose is rapidly depleted, and is verified by the appearance of a yellow colour in the base. After the fermentation of glucose, the aerobic degradation of the protein substrate of the medium occurs, producing ammonia, which gives the medium an alkaline pH, changing the bezel colouring to intense pink. Gas production is indicated by the formation of blisters or cracks in the medium. Most Salmonellas do not ferment sucrose and lactose. When these two sugars are not fermented, the apex keeps its original colour—amber. The production of H2S is indicated by the black colour at the base of the central portion of the tube. Microorganisms such as Proteus mirabilis, Edwardsiella tarda, C. freundii and Salmonella spp may exhibit a similar behaviour.
Lysine-iron agar (LIA Agar): this medium is used to verify the decarboxylation of lysine which is evidenced by the violet colouration—alkaline—of the base. When this does not occur, the yellow colour indicates only the fermentation of glucose. The positive reaction for the deamination of lysine is visible at the apex (coppered violet) and the production of H2S by the appearance of black colouring from the base to the central portion of the tube.
Hydrolysis of urea (Stuart urea broth and Christensen urea agar: determines the ability of a microorganism to degrade, enzymatically, the urea by urease, with the formation of two molecules of ammonia and carbon dioxide, with the alkalization of the medium and increased pH. Streak only on the surface of the broth or the urea agar. Incubate at 36°C for 18–24 hours. The colour is caused by addition of the phenol red to the medium. The positive reaction turns the yellow (the original colour of the medium) to intense pink. Proteus features a more intense reaction; the negative reaction maintains the yellow colour of the medium. A total of 99% of the Salmonella strains do not produce urease.
Indole test (SIM medium): check the motility of the microorganisms and the H2S and indole production capacity. Inoculate the culture medium. Incubate at 36°C for 24–30 hours. The motility reading is characterized by the diffusion of growth throughout the medium. If restricted to the line of streaking, it indicates that the microorganism is immobile. After the motility reading, verify H2S production by the development of the black colour in the medium. Bacteria that possess the tryptophanase enzyme are capable of hydrolyzing and deaminating the tryptophan with the production of indole, pyruvic acid and ammonia. To verify its production, add a few drops of Kovac\'s reactive to the tubes; if there is indole production a red ring will form. In most cases (99%) the strains of Salmonella do not produce indole, do produce H2S and are mobile.
Voges-Proskauer (VP) test: determine the ability of some bacteria to oxidize glucose producing organic acid as a final product. Transfer the microorganism to be tested to test tubes with red broth methyl-Voges-Proskauer (Clark and Lubs medium). Incubate the tubes at 37°C for 24–48 hours. To read, add 5 drops methyl red. Positive: red colour (pH < 4.0); Negative: original medium colour (yellow) (pH ≥ 6.0). The Salmonellas are VP negative. From the VM-VP medium, remove 2 mL of culture to a new tube and add 15 drops of α-naphthol 5% reagent (reagent A) and 5 drops of KOH 40% solution (Reagent B) to each tube for each ml of culture medium. Agitate the tubes so that there is oxygenation of the medium. Wait for 10–30 minutes. Positive: development of pinkish to red colouring; Negative: absence of pink or red.
Utilization of citrate (Simmons citrate agar): characterize microorganisms capable of utilizing citrate as the sole carbon source, which cause the pH of the culture medium to increase due to the metabolism of citrate ions. Transfer by streaking the bacteria to be tested on the inclined surface of the Simmons citrate agar with a needle. Incubate the tubes at 37°C for 24–48 hours. The Salmonella strains (95%) are positive, except for the serotypes Typhi, Paratyphi A, Pullorum and Galinarum (100% of negative strains) and Choleraesuis (75% of negative strains), and can utilize the citrate and extract nitrogen ammonium salt, leading to alkalization of the medium from the conversion of the NH3 in ammonia hydroxide (NH4OH). After incubation, examine the cultures contained in the tubes with an inclined medium and assess the presence or absence of bacteria growth, checking for any change in colour: if positive, the medium becomes intense blue, especially at the apex; if negative, the natural colour of the medium does not change, but remains green.
Lysine decarboxylation: determine the enzymatic ability of a microorganism by decarboxylating the amino acid lysine, with the subsequent alkalization of the medium, by the presence of the enzyme lysine decarboxylase. The colour is promoted by bromocresol (pupura indicator), which has a violet colour at alkaline pH. Inoculate with lysine iron agar with deep stabbing incisions, streaking the inclined surface of the bevel. Add sterile seal (Vaseline), to avoid the contact of the medium with air and the consequent appearance of a false alkalization on the surface of the medium by aerobically degrading the protein substrate. Incubate at 36°C for 24–30 hours. The majority of Salmonellas (96%) can produce lysine decarboxylase. During the initial period of incubation, the medium turns yellow due to the fermentation of glucose present. If the amino acid is decarboxylated, alkaline amines are formed and the colour of the medium returns to the original purple colour, with the production of H2S.
Motility indole-8-ornithine (MIO) and motility indole-lysine (MIL): inoculate by deep stabbing and incubate at 24 h/35°C. These mediums demonstrate the decarboxylation of ornithine or lysine amino acid, mobility and indole production. Mobility is interpreted by microorganism dissemination in the inoculation area (growth only in line of incision = negative motility); decarboxylation of ornithine or lysine is evidenced by a purple (alkaline) colouring in the base which neutralizes the acid (yellow) formed by the fermentation of glucose. Indole production is observed by the formation of a red ring after adding 2–4 drops of Kovacs reagent to the medium surface.
In malonate-phenylalanine broth: determines the capacity of the microorganism to deaminate phenylalanine in phenylpyruvic acid, by its enzymatic activity, with consequent acidity. Inoculate the surface of the phenylalanine agar by streaking. Incubate at 36 ± 1°C for 18–24 hours. Add 2–3 drops of 10% ferric chloride solution. In a negative test, as there is no phenylpyruvic acid, the colour of the reactive FeCl3 remains yellow. The change of the colour on the bevel surface to green indicates the deamination reaction of the phenylalanine. Salmonella does not deaminate phenylalanine, with the colour of the medium remaining unchanged.
Dulcitol broth: dulcitol fermentation occurs by turning the phenol red indicator to yellow. Most Salmonella are dulcitol positive (yellow).
Figure 5.
Procedure for the pre-enrichment non-selective broth.
Figure 6.
Procedure for the pre-enrichment selective broth.
Figure 7.
Procedure for differential plating and biochemical identification.
Culture medium used
Positive or negative reaction
Colour of culture medium
Positive or negative percentage
Glucose TSI (gas)
+
Yellow medium with gas
100.0
Glucose TSI (acid)
+
Yellow medium/red bevel
91.9
Lactose TSI
−
Red medium
99.2
Sucrose TSI
−
Red medium
99.5
TSI H2S
+
Dark medium
91.6
LIA
+
Violet copper medium + H2S production
98.0
SIM H2S
+
Dark medium colour the base
97.0
SIM (indole)
−
No red ring
98.9
SIM (motility)
+
Diffusion in inoculation zone
97.0
Urea hydrolysis
−
Yellow medium
99.0
Lysine decarboxylase
+
Violet medium + H2S
94.6
Ornithine decarboxylase
+
Violet medium + H2S
97.0
Voges-Proskauer reaction
−
No red ring
100.0
Table 3.
Colour of culture medium and Salmonella spp. positive and negative percentage in biochemistry test after 24 h incubated at 35°C.
\n
Observations:
\n
These percentages indicate the incidence of strains with reactions marked as + or −.
S. Typhi is anaerogenic;
Salmonella enterica arizonae: + or – reaction to lactose, positive β-galactosidase;
Salmonella enterica salamae: - reaction to lactose and β-galactosidase;
Salmonella pullorum and Salmonella gallinarum are immobile;
S. arizonae absorbs the malonate;
S. arizonae does not ferment the dulcitol;
25% of the Salmonella strains are citrate-negative.
\n
In general, the various regulatory bodies also recommend the use of miniaturized commercial kits which allow a great number of biochemical tests.
\n
Serological test using fast agglutination
\n
Serologic confirmation verifies the presence of ’O‘, ’V‘ and ’H‘ antigens by agglutination tests with polyvalent antisera:
\n
Add approximately 2 mL of saline solution 0.85% to the culture in inclined nutrient agar and homogenize;
With a Pasteur pipette deposit two drops of the suspension separately on a glass slide;
Add one drop of anti-Salmonella polyvalent ’O‘ serum to one of the droplets of the suspension on the slide and mix, and add one drop of saline to the other;
Perform the reading under illumination against a dark background for 1–2 minutes.
\n
Classify the reaction as follows:
\n
Positive: presence of agglutination only in the cultivation + antiserum mixture.
Negative: no agglutination in either mixture.
Nonspecific: presence of agglutination in both mixtures (rough forms).
\n
The cultures with positive results in the agglutination test with the anti-Salmonella polyvalent ’O‘ serum should be sent to certified laboratories for final classification.
\n
2.4. Determination of the antibacterial activity of honey
\n
With the exaggerated use of certain compounds such as ampicillin, cephalexin and others, bacteria have developed resistance to antibiotics, leading to studies for new compounds with antimicrobial activity from different natural products such as honey [35]. Since the beginning of civilization, honey has had a cultural importance that is not restricted merely to food but also includes use as folk medicine and as a cosmetic [36]. It has different therapeutic properties and is antimicrobial, antifungal, antioxidant, antiviral, anti-parasitic and anti-inflammatory [35, 37].
\n
Honey is a substance prepared from the nectar of flowers (floral honey), plant exudates, or the excretion of sucking insects of plants (honeydew) [18]. The enzyme content present in honey is differentiated, as it depends on the species of bee, soil characteristics, seasonal factors such as temperature, rainfall and bee flora, with the product distinguished by the amount of organic acids, enzymes, vitamins, flavonoids, minerals and an extensive range of organic compounds, contributing to its colour, odour and specific flavour [38].
\n
The antimicrobial action of honey is related to soil characteristics, atmospheric conditions, plant diversification, low water activity (Aw), high osmotic pressure, low pH, the glucose/oxidase system of hydrogen peroxide formation, the presence of phytochemical constituents and volatile substances [39]. These different qualities together create differences in the expression of antimicrobial activity of honey [40]. Molan [41] reported that in super-saturated sugar solutions, honey has a low water activity, which, as well as the natural acidification of the medium, creates unfavourable conditions for bacterial growth. In the presence of water and oxygen, the enzyme glucose oxidase converts glucose into gluconic acid and hydrogen peroxide, which are considered relevant substances for antioxidant action, which affect the microorganisms and preserve the sterility of honey during maturation [42].
\n
Method:Preparation of bacterial inoculum and standardization: from the pure culture of bacteria preserved under refrigeration at 6°C, proceed to the preparation and standardization of the inoculum, in accordance with to the Clinical and Laboratory Standards Institute CLSI M07-A9 document [43]. Transfer three to five colonies of the selected strain to a test tube with a screw top containing 4–5 mL Miller Hinton broth (MHB), incubate the culture in the broth at 35°C for 18–24 hours and standardize the bacterial suspension in 0.85% saline solution, obtaining an optical turbidity comparable to standard McFarland solution 0.5 to the naked eye under illumination against a white background card with contrasting black lines. Dilute the inoculum at a ratio of 1:10 in saline solution 0.85% resulting in a concentration of 107 CFU/ml.
\n
Antimicrobial susceptibility testing—broth microdilution method: perform as per Clinical and Laboratory Standards Institute CLSI M07-A9 document [43], which is used to verify the minimum inhibitory concentration (MIC) by the broth microdilution method. To perform the test use 96 U bottom wells with markings indicating the position of each well, lines (A–H) and columns (1–12) (Figure 8).
Figure 8.
96 well U bottom micro-plate with markings indicating the position of the lines (A–H) and columns (1–12).
\n
Pipette out 100 μL of Mueller Hinton broth in each well and then perform a dilution series of different samples of honeys, with each honey sample in a different line. For serial dilution, pipette out 100 μL of honey in the first well, homogenize, remove 100 μl from the first well and transfer to the second well, remove 100 μL from the second and transfer to the third; and so on, until the ninth well of each row. This provides the following honey concentrations in percentages (%) (Table 4).
No of wells
Honey (%)
1
50.000
2
25.000
3
12.500
4
6.250
5
3.125
6
1.560
7
0.780
8
0.390
9
0.195
Table 4.
Honey concentrations in percentage (%) for wells from 1 to 9.
\n
As a bacterial control (without the addition honey), use well 10, and as a broth control (without the addition of honey and inoculum), use well 11. After adding the honey, inoculate 5 μL of a standardized suspension of the bacteria in question in each well, except the broth control well, so that the end of test bacteria concentration is 5 × 104 CFU/well. Identify the micro-plates, incubate in a bacteriological incubator at 35°C for 24 hour. After 24 hours of incubation the micro-plates are analysed to determine the MIC, which is defined as the lowest concentration of honey in which there is no visible growth after incubation. Finally, analyse the well contents indicated with the minimum inhibitory concentration by microscope to confirm if there is growth or not. Perform the tests in triplicate for each of the bacteria analysed.
\n
2.5. Microscopy of honey
\n
Microscopy of food is a technique used to identify foreign components in products, making it possible to check if they comply with standards. Several countries use government and health-related agencies to ensure food safety, by monitoring their supply chains. MERCOSUR GMC Resolution N° 15/1994 approved the Technical Regulations for the Identity and Quality of Honey based on resolutions N°. 18/1992 and N° 91/1993 of the Common Market Group [17]. Normative Instruction N°. 11 of October 20, 2000 approved the Technical Regulations for the Identity and Quality of Honey [18] and pursuant to Ordinance N°. 46 of 10 February, 1998 the Ministry of Health and the Ministry of Agriculture and Supply established the adoption of the Hazard Analysis and Critical Control Point (HACCP) system by the Food Industries for animal products [44]. This system is recommended by international bodies such as the World Trade Organization (WTO) and the World Health Organization [16], both forming part of the United Nations (UN), for Food and Agriculture.
\n
In compliance with these standards, any problem identified in honey lots should be corrected immediately and possible causes should be identified. Once the failures are identified, the company must take corrective action to prevent new problems arising. These corrective actions must be validated through audits and microbiological tests that prove the definitive correction of the non-compliance.
\n
According to the macroscopic and microscopic criteria established in Brazil [18] honey must be free from any foreign substance. In practice, dirt present in honey may come from two sources—the first occurs inside the beehive, and is more difficult to control as it is added to honey by bees which carry fragments of other insects, pollen and soil. Secondary sources are present from harvesting through the steps of obtaining, processing, and distribution of honey [45], and include wax fragments, propolis, larvae, wood fragments and among others. Use of Good Apicultural Practices reduces the risk of secondary contamination ensuring a quality product in accordance with standard rules [46]. Camargo [47] recommends the procedures of the Good Apicultural Practices should be applied during the processing of honey, including: use stainless steel trays for stacking wooden beehives, allow no contact between the wooden beehive and the ground; choose honeycombs free of bees, larvae, or pollen; open wooden beehives only in the reception of the honey house for prior cleaning (removing of adhered bees, wax and propolis); filter the honey with the aid of sieves with meshes of various diameters, pumps or filters; decant the honey for soil removal at lower densities.
\n
Method: The analysis of dirt and foreign matter can be performed following the method of the Association of Official Analytical Chemistry AOAC [9] N°. 945.79, which uses filtration of the sample in the presence of nitric acid. The method is based on dissolving 100 g of the honey sample in 200 mL of distilled water which is heated and acidified with 5 mL of nitric acid (HNO3) at a concentration of 6 M. Filter the sample in a Buchner funnel. Mark four quadrants on filter paper. Analyse using a stereoscopic microscope with a total multiplication of 100× and confirm the type of sediment between slide and cover slip under an optical microscope with a multiplication of 100–400×.
\n
2.6. Clostridium botulinum
\n
The pathogenic microorganism of importance in honey is the Clostridium botulinum bacterium, which is capable of producing spores. Bacterial spores are latent and resistant to adverse environmental conditions and can thus endure processing and storage for long periods. Contamination of honey by C. botulinum spores occurs within the colony, making practical procedures for its prevention difficult.
\n
In practice, the bees carry the spores of this bacterium in their legs and antennae, taken from the soil where they land constantly. These spores begin to grow in the colonies, and remain in the combs together with the honey. Contamination is also possible in the act of collecting the product if hygiene practices are poor, and further contamination can occur through contact with the ground. Once present in honey, it survives in the medium without competition from other microorganisms. The incidence of spores in honey may also be related to multiplication and sporulation in dead bees and their larval forms in the colonies [48].
\n
Honey is the only food recognized as a risk factor for infant botulism. Although there have been many cases of occurrence of infant botulism from honey contaminated with Clostridium botulinum, literature on this topic remains scarce. Consequently, in Brazil the administration of honey to children is not recommended, especially in the breastfeeding phase. This practice is also adopted in the United States, the United Kingdom and Argentina, where spores were isolated [49].
\n
This disease occurs in children under 12 months, and 95% of cases occur in the first 6 months of life, when honey is used as a sweetener for bottles and juices as well as to bathe pacifiers to soothe the child. A child\'s intestine possesses an immature flora. The intake of honey with spores leads to germination, multiplication and the production of botulinum neurotoxins in the intestinal lumen, causing many problems for the health of children [50]. The consumption of honey by adults or older children does not seem to provide any kind of risk in relation to botulism. Consequently, it is recommended by the World Health Organization and the US Centers for Diseases that honey should not be given to infants under 6 and 12 months, respectively [16, 31].
\n
Honey added as an ingredient in commercial infant formulas for babies aged less than 1 year must be thermally processed to destroy botulinum spores. No reports exist about the use of honey as an ingredient in other foods which have caused botulism. The analysis of honey for C. botulinum is not recommended as a control measure [49].
\n
The microbiological analysis of honey detects product contamination. The presence of microorganisms or their spores in honey can cause its deterioration and result in enzymatic changes, the production of mycotoxins and even consumer illness. Due to the therapeutic properties attributed to honey, antimicrobial evaluation is essential to contribute to the quality maintenance of this product, adding to its commercial value.
\n',keywords:"microbiological analysis, microbiological standards, good habits, antimicrobial action, antimicrobial agents",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/54073.pdf",chapterXML:"https://mts.intechopen.com/source/xml/54073.xml",downloadPdfUrl:"/chapter/pdf-download/54073",previewPdfUrl:"/chapter/pdf-preview/54073",totalDownloads:3008,totalViews:1482,totalCrossrefCites:2,totalDimensionsCites:4,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:71,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"May 31st 2016",dateReviewed:"November 28th 2016",datePrePublished:null,datePublished:"March 15th 2017",dateFinished:"February 10th 2017",readingETA:"0",abstract:"The aim of this chapter is to describe the most commonly used techniques to evaluate the microbiological characteristics of honey for the purpose of identifying its contaminant flora, its significance and its control in this type of food. Honey is a product that is rich in simple sugars, minerals, vitamins and bioactive compounds and possesses an antimicrobial activity of great significance for human health. However, as it has physical and chemical properties that are unfavourable for the proliferation of micro-flora, honey can contain a large population of microorganisms from two sources of contamination—the first primarily represented by pollen, the digestive system of the bee, dust, air and the flower itself; and the second as the result of negligence and the absence of good health practices during handling and use; for example, placing honey in wooden beehives directly on the floor or the use of improperly washed honey extraction equipment, rather than equipment based on the oxidizable material, or using very dark honeycombs and storing the honey for long periods in wooden beehives. As honey is a natural product, the risks inherent to the lack of industrial processing, such as pasteurization and strict microbiological quality control, are often overlooked.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/54073",risUrl:"/chapter/ris/54073",book:{id:"5520",slug:"honey-analysis"},signatures:"Maria Josiane Sereia, Marcia Regina Ferreira Geraldo Perdoncini,\nPaulo Henrique Março, Rejane Stubs Parpinelli, Erica Gomes de\nLima and Fernando Antônio Anjo",authors:[{id:"180403",title:"MSc.",name:"Rejane Stubs",middleName:null,surname:"Parpinelli",fullName:"Rejane Stubs Parpinelli",slug:"rejane-stubs-parpinelli",email:"rezootecnia@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"192847",title:"Dr.",name:"Márcia",middleName:null,surname:"Perdoncini",fullName:"Márcia Perdoncini",slug:"marcia-perdoncini",email:"mperdoncini@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Sampling plan for the microbiological analysis of honey lots",level:"2"},{id:"sec_2_2",title:"1.2. Transport of samples",level:"2"},{id:"sec_3_2",title:"1.3. Analytical unit",level:"2"},{id:"sec_4_2",title:"1.4. Homogenization of the honey sample and withdrawal of the analytical unit",level:"2"},{id:"sec_6",title:"2. Description of methods",level:"1"},{id:"sec_6_2",title:"2.1. Total and thermotolerant coliforms",level:"2"},{id:"sec_7_2",title:"2.2. Yeast and mould counting",level:"2"},{id:"sec_8_2",title:"2.3. Salmonella sp",level:"2"},{id:"sec_9_2",title:"2.4. Determination of the antibacterial activity of honey",level:"2"},{id:"sec_10_2",title:"2.5. Microscopy of honey",level:"2"},{id:"sec_11_2",title:"2.6. Clostridium botulinum",level:"2"}],chapterReferences:[{id:"B1",body:'Brazil. Ministry of Health. National Sanitary Surveillance Agency. Resolution RDCNo. 12 of January 2, 2001. Approves the technical regulation on microbiological standards for food [Internet]. 2001. Available from: http://portal.anvisa.gov.br/documents/33880/2568070/RDC_12_2001.pdf/15ffddf6-3767-4527-bfac-740a0400829b [Accessed: 2016-06-14].'},{id:"B2",body:'Brazil. Ministry of Agriculture and Supply. Normative Instruction No. 6 of August 26, 2003. Official Analytical Methods for Microbiological Analysis for the Control of Products of Animal Origin and Water [Internet]. 2003. Available from: http://sistemasweb.agricultura.gov.br/sislegis/action/detalhaAto.do?method=consultarLegisl acaoFederal [Accessed: 2016-06-14].'},{id:"B3",body:'Andrews WH, Flowers RS, Silliker J, Bailey JS. Salmonella. In: Downes FP, Ito K, editors. Compendium of methods for the microbiological examination of foods. 4th ed. Washington, DC: American Public Health Association; 2001. pp.357–380.'},{id:"B4",body:'Downes FP, Ito K. Compendium of methods for the microbiological examination of foods. American Public Health Association. 4th ed. Washington, DC: American Public Health Association; 2001. 676p.'},{id:"B5",body:'Morton RD. Aerobic plate count. In: Downes FP, Ito K, editors. Compendium of methods for the microbiological examination of foods. 4th ed. Washington, DC: American Public Health Association; 2001. pp.63–67.'},{id:"B6",body:'International Commission on Microbiological Specifications for Foods (ICMSF). Microorganisms in Foods 2. Sampling for microbiological analysis: principles and specific applications. 2nd ed. Toronto: University of Toronto Press; 1986. 131 p.'},{id:"B7",body:'International Commission on Microbiological Specifications for Foods (ICMSF). Microorganisms in Foods 7: Microbiological testing in food safety management. Norwell: Kluwer Academic/Plenum Publishers; 2002. 362 p.'},{id:"B8",body:'United States of America. Department of Agriculture. Fact sheets. Production and inspection. risk analysis [Internet]. 2003. 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In: Staley JT, editor. Bergey\'s Manual of Systematic Bacteriology. Vol. 3. Baltimore: Williams & Wilkins; 1989. pp. 1658–1682.'},{id:"B23",body:'Silva N, Junqueira VCA, Silveira NFA, Taniwaki MH, Santos RFS, Gomes ARR. [Manual methods of microbiological analysis of food, in Portuguese]. Manual de métodos de análise microbiológica de alimentos. São Paulo: Varela; 2010. 624 p.'},{id:"B24",body:'Marquezi MC. [Methodological comparison to estimate the most probable number (MPN) of coliform in water samples, in Portuguese]. Comparação metodológica para a estimativa do número mais provável (NMP) de coliformes em amostras de água [dissertation]. Piracicaba: Universidade de São Paulo; 2010.'},{id:"B25",body:'International Commission on Microbiological Specifications for Foods (ICMSF). Food microorganisms: techniques of microbiological analysis. 1984. 431 p.'},{id:"B26",body:'Silva N. [New methods of microbiological analysis of food, in Portuguese]. 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[Good Practices in harvesting, extraction and processing of honey, in Portuguese]. Boas Práticas na Colheita, Extracão e Beneficiamento do Mel [Internet]. 2003. Available from: https://www.infoteca.cnptia.embrapa.br/bitstream/doc/66838/1/Doc78.pdf [Accessed: 2016-06-15].'},{id:"B48",body:'Nakano H, Kizaki H, Sakaguchi G. Multiplication of Clostridium botulinum in dead honey-bees and bee pupae, a likley source of heavy contamination of honey. International Journal of Food Microbiology. 1994;21(3):247–252. DOI: 10.1016/0168-1605(94)90031-0.'},{id:"B49",body:'International Commission on Microbiological Specifications for Foods (ICMSF). [Microorganisms in food 8: use of data for process control assessment and acceptance of the product, in Portuguese] Microrganismos em alimentos 8: utilização de dados para avaliação do controle de processo e aceitação do produto. São Paulo: Bhucher; 2015. 522 p.'},{id:"B50",body:'Arnon SS, Midura TF, Clay SA, Wood RM, Chin J. Infant botulism. Epidemiological, clinical, and laboratory aspects. Journal American Medical Association, 1977;237(18):1946–1951. DOI:10.1001/jama.1977.03270450036016.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Maria Josiane Sereia",address:"mjsereia@gmail.com",affiliation:'
Department of Engineering and Food Technology, Federal Technology University of Parana (UTFPR), Campo Mourāo, Brazil
'},{corresp:null,contributorFullName:"Marcia Regina Ferreira Geraldo Perdoncini",address:null,affiliation:'
Department of Engineering and Food Technology, Federal Technology University of Parana (UTFPR), Campo Mourāo, Brazil
Agricultural Science Center, State University of Maringa (UEM), Maringa, Brazil
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1. Introduction
The goal of the drug development process is to find bioactive molecules that can help in disease therapy [1, 2]. The drug discovery life cycle has multiple steps: drug target identification, target validation, hit identification, lead optimization, preclinical development, clinical trial, approval, and postmarketing monitoring (Figure 1A). Because going through all of these stages of developing a new drug can cost between $1 and $2 billion and take 10–17 years, drug discovery is a big issue in the pharmaceutical business [3]. To speed up the drug discovery process, a considerable number of developments were made in the 1990s using combinatorial and high-throughput screening (HTS) approaches. These approaches were widely used since they allowed for the quick synthesis and screening of vast libraries, but no meaningful success was achieved, and little progress was made toward the discovery of new compounds. To aid the discovery process, a combination of modern computer approaches, biological research, and chemical synthesis was developed, and this combined approach increased the scope of discovery. The term "computer-aided drug design" (CADD) was eventually used to describe the use of computers in drug discovery. Computer-aided drug design (CADD) is one of the most widely utilized approach for reducing drug development costs and time. CADD is a specialized field, in which various computational approaches are employed to mimic receptor–drug interactions in order to identify binding affinities (Figure 1B). The approach, however, is not just for studying chemical interactions and predicting binding affinity; it can be used for everything from designing compounds with desired physiochemical features to managing digital databases of chemicals. CADD is a wide term that encompasses both structure- and ligand-based drug developments. Virtual screening (VS) is a computational method for screening large databases of compounds that has successfully supplemented HTS in drug discovery. The fundamental purpose of VS is to make it feasible to quickly and cheaply screen enormous virtual chemical databases for potential leads for synthesis and future study [4]. Computer-assisted virtual screenings have been a widely used method for estimating various types of ligands to bind with target over time [5, 6, 7, 8]. Additionally, in order to investigate atomistic level of protein/nucleic acid-small molecule interactions, one of the widely utilized computational biophysics tools is molecular dynamics (MD) simulation [9, 10]. MD simulation finds its relevance in shedding lights on the conformational ensembles either of the small molecule or of the target. The technique is seldom utilized to capture the dynamics of proteins and/or to check the stability of modeled protein structures enabling CADD for designing efficient inhibitors [11, 12]. It is also leveraged to investigate a comparative binding of small molecule to different proteins along with complementing experimental observations [13].
Figure 1.
Flowchart of events taken place in (A) drug discovery lifecycle and (B) computer aided drug discovery.
The recent expansion of make-on-demand libraries to billions of synthesizable molecules has piqued the interest of the drug-discovery community, as such massive databases allow access to previously unexplored chemical realms. The introduction of ultralarge libraries, on the other hand, has revealed substantial limitations of traditional docking techniques, which typically work on the scale of millions of molecules at a high cost of computation. This aspect depicts CADD as a very useful process, in which only a small portion of the highest-scoring compounds often leaving low scoring but potential compounds are considered for experimental examination. Artificial intelligence (AI) and machine learning (ML) aided approaches provide a low-cost, high-reliability solution to a variety of problems (Figure 2), from protein three-dimensional (3D) structure prediction to physiochemical property calculation and bioactivity prediction to ultralarge docking [14, 15].
Figure 2.
Biomolecular analysis core scheme for drug discovery via AI/ML.
2. Artificial intelligence (AI) and machine learning (ML)
The ultimate goal of artificial intelligence (AI) is to train computer programs with human-like intellect. For this, AI uses computers to learn human behaviors, such as learning, judgment, and decision-making by simulating human intelligent behavior with computers. The term artificial intelligence was first proposed in 1956 at a conference at Dartmouth University; however, the major AI-related research started since the end of the twentieth century. AI has provided enormous economic benefits to humanity and has helped all parts of life, while also considerably promoted social growth and ushered in a new era of social development [16, 17]. Both the volume and the multidimensionality of data have increased dramatically as a result of the advent of numerous high-throughput technologies. Big data is both a requirement and a key component for AI to improve its recognition rate and accuracy [16].
Machine learning (ML), a branch of AI, is the use of an algorithm that improves its performance by learning from data. Machine learning, according to Arthur Samuel, is described as a computer\'s ability to analyze without being explicitly programmed [16, 18]. Supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning are the four types of machine learning algorithms [16]. In supervised learning, the test data are trained with a labeled dataset to predict the type or value of new data, whereas unsupervised learning uses unlabeled data based on the input pattern. Support vector machine (SVM), linear discrimination, and decision tree are some of the types of supervised learning algorithms, whereas k-clustering and principal component analysis are the examples of unsupervised learning algorithms. Semisupervised learning combines the benefits of both supervised and unsupervised learning. It can be useful if there exist unlabeled data and collecting the labeled data is a time-consuming procedure. Reinforcement learning seeks to solve a problem through a hit-and-trial strategy, including feedback and decisions, with the ultimate goal of increasing total reward [16, 18, 19]. Deep learning (DL), a subset of machine learning, is one of the most cutting-edge areas of research and development in practically every scientific and technical discipline. Many problems that normal ML algorithms could not solve, such as image recognition and speech recognition, can be solved with the help of DL methods. DL methods also have immense role in the drug discovery pipelines, including drug activity prediction, target identification, and lead molecule discovery. The foundations of DL are frequently implicated in neural network systems, where they are employed to develop systems capable of complicated data recognition, interpretation, and production [20].
3. AI and ML in protein structure modeling
Drug design is based on the idea of creating compounds with a regulated interaction profile against a variety of target and off-target proteins in an organism. To understand the mode of action of a candidate drug, three-dimensional (3D) details are of paramount importance. Despite the availability of a variety of experimental methods to decipher the 3D structure of proteins, such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy, the sequence–structure gap, in which protein sequences vastly outnumber the number of corresponding 3D structures, frequently causes problems. In such cases, protein structure prediction methods come as a remedy [21, 22].
Over the past 30 years, AI and ML methods have been used to predict protein structure. AI programs have helped to assess and identify most accurate models. To compare the predicted models to known crystal structures, these programs are trained utilizing numerous numerically represented atomic parameters from the models, such as bond lengths, inter-residue interactions, physiochemical properties, and so on. The Critical Assessment of Protein Structure Prediction (CASP) contests have been held biannually since 1994 for the blind evaluation of cutting-edge methods for predicting three-dimensional (3D) protein structures from protein sequences [23, 24]. For the cases where a template is not available for modeling, two approaches are considered: fragment-based assembly and ab initio folding. Fragment-based assembly is advantageous than ab initio folding due to its higher accuracy and higher capability [25].
With near-experimental precision, Alphabet\'s DeepMind won the 13th edition of CASP in 2018 with its latest artificial intelligence (AI) system, AlphaFold [25]. The 3D structure prediction by assembling the most probable fragments by AlphaFold is done by using co-evolution analysis of a multiple sequence alignment and using deep neural networks (DNNs) to discover coevolutionary patterns in protein sequences as contact distributions and transform them into protein-specific statistical energy potentials [23]. DeepFragLib, a fragment library constructed utilizing deep contextual learning techniques to give high-quality, native-like fragments for every segment of a protein for the efficient assembly of near-native conformations, is another example of AI breakthrough in the field of structure prediction. Table 1 represent applications of some AI-ML approaches for protein structure prediction/quality assessment.
Few examples of applications of AI–ML methods in protein structure methods.
4. ML and AI in physiochemical property calculation
In the biopharmaceutical sectors, the effective and precise forecasting of molecular characteristics of drug compounds is indeed a fundamental component of rationalized compound synthesis. Current techniques span from basic atom summation through bond energy additions, paired interatomic configurations, and more sophisticated machine learning systems capable of representing aggregate reactions among several particles or bonds. In addition, simple correlation force fields show predictive performance comparable to reference energy sources determined utilizing density functional theory with hybrid exchange-correlation functional for steady-state geometric models; even so, properly accounting for the collaborative many-body connections is required for advancing the “magic formula” of compound accuracy of 1 kcal/mol for both the steady-state and out-of-equilibrium topologies [32]. In the years 2010–2012, the initial machine learning (ML) methods for molecular modeling relied on tiny datasets with quantum mechanical (QM) features for 102–103 molecule systems. It is believed that the chemical compound space has 1060–10,100 molecular systems. Chemical spaces have grown in size and complexity during the previous decade. Data are being generated at an astonishing rate owing to large-scale QM and MD methodologies, as well as developments in high-throughput studies [33].
Machine learning models predict small molecule’s properties based on their chemical structure. Because of their ease of interpretation and effectiveness on small datasets, linear models were initially used. However, over time, nonlinear models were developed to capture more complex relationships between structure and activity. The nonlinear approaches include support vector machines, recursive partitioning methods, and deep learning methods. With the availability of standardized large-scale data, deep-learning-based techniques for ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction are showing growing promise and utility. The ability to identify small compounds with increased efficacy, safety, and dosage is considerably aided by understanding ADMET characteristics. In terms of consistency and predictive performance, the graph convolutional DNN (GCNN) technique is proposed to be superior to existing approaches such as random forest (RF), Cubist, and support vector machine (SVM) for calculating ADMET characteristics [34]. The AI-based ADMET predictors utilize cellular permeability data from a diverse class of molecules generated by different cell lines. To predict acid dissociation constant of compounds, artificial neural network (ANN)-based models, graph kernels, and kernel-ridge-based models have been used. To predict the solubility of the compounds, undirected graph recursive neural networks and GCNN have been used. GCNN methods are also used to predict cytotoxicity, which is one of the important properties used in drug discovery to avoid toxic effects [35]. The latest ANN studies support immunoinformatics and chemoinformatics analysis for novel vaccine and drug discovery [11]. Examples of the AI-based tools for molecular property calculation are DeepTox (www.bioinf.jku.at/research/DeepTox), Chemputer (https://zenodo.org/record/1481731), and ORGANIC (https://github.com/aspuru-guzik-group/ORGANIC) [35].
5. ML and AI in bioactivity prediction
In order to prioritize compounds for synthesis and/or biological evaluation, quantitative structure-activity relationship (QSAR) modeling has been used [36]. The goal of QSAR models is to find a mathematical relationship between the physicochemical qualities of substances, which are represented by molecular descriptors, and their biological activity. These models are important in drug optimization because they provide a preliminary in silico assessment of key qualities such as activity, selectivity, and toxicity of candidate compounds. In QSAR modeling, AI/ML techniques (such as RF, SVM, Naïve Bayesian, and ANN) have been widely used. Among these techniques, the RF algorithm has been regarded as a gold standard in QSAR studies [23]. In the case of bioactivity prediction, DL approaches have shown improved performance compared with ML [35]. Few examples of AI-based tools to determine bioactivity are WDL-RF (integration of DL and RF) (https://zhanglab.ccmb.med.umich.edu/WDL-RF/), pairwiseMKL (multiple-kernel-learning-based method) (https://github.com/aalto-ics-kepaco), and DeepMalaria [37] (DL based).
6. ML and AI in drug–target interactions
To fully comprehend a drug\'s efficacy and usefulness, it is important to determine how it interacts with a receptor or target. Drug-protein interactions have recently been a hot topic in drug repurposing research [38]. ML algorithms have become the advanced approach for estimation of drug–target interactions due to the huge amount of obtainable drugs and target information in huge datasets, advancing as well as innovative computer networking, and inherent characteristics of different types of deep learning. A vast number of proteins have indeed been sequenced, and numerous molecules have now been synthesized since the advent of sequencing technology, high-throughput technologies, and computer-aided drug design methods. Actual information has been organized, and multiple databases have been developed based on existing related efforts and acquired expertise. The majority of data in these sources is open to the public and free to download, thus providing a strong data basis for using deep learning to solve drug-target contact predictions issues. PubChem presently comprises 109 million chemicals and is the world’s biggest database with open access to chemical characterization. PubChem has grown in importance as a source of chemical knowledge for researchers, learners, and the general public. Artificial intelligence can be used to train deep learning models for drug discovery using known drug data [39]. Several ML techniques have been used to predict drug–target interactions including SVM, DL, DNN, convolutional neural network (CNN), etc. The de novo drug design approach has been frequently employed to create therapeutic compounds in recent years. The old approach of de novo drug design is being phased out in favor of emerging DL methodologies, which have the advantages of less complicated synthesis routes and easier prediction of novel molecule bioactivity [35]. However, classical algorithms cannot be completely ignored, as studies point out that the classical algorithms show higher and more stable performance than the machine-learning-based methods at different similarity levels of training sets. Hence, many tools have been developed combining both classical and deep learning models [40]. Few of the tools and servers available for finding drug-protein interactions using AI and ML methods are mentioned in Table 2.
Few examples of applications of AI–ML methods in drug–protein interactions.
7. Conclusion
Although AI is frequently portrayed as a magic wand that can provide flawless output regardless of the quality of the input, it is not the solution to every problem. The ultimate goal of using AI and machine learning approaches to drug development challenges is to bring the best drugs to market. Throughout the drug discovery process, the combined effort of different AI methods allows for a better understanding and design of novel inputs [45]. The AI-based applications are getting more intelligent, cost-effective, and time-efficient while increasing efficacy, because of more precise algorithms, more powerful supercomputers, and significant private and public investment in the sector [20]. To properly leverage AI in drug development, one must increase the quality of decisions we make regarding compounds that are progressed to clinical trials. However, in many circumstances, the data available to make those decisions are not totally sufficient for this purpose [46]. Since the entire success of AI depends on the availability of a substantial amount of data, we need to conduct trials more efficiently, which can be supported by computational methods [35, 46]. Major challenges faced by AI methods include data accuracy and availability, reproducibility, model appropriateness, etc. Despite the challenges, AI is projected to advance the field of personalized/precision medicine to the point where it becomes regular practice even in the treatment of minor illnesses in the future [47]. By 2028, AI is expected to save the pharmaceutical industry more than US$70 billion in drug discovery costs [48]. With more clinical data and improved AI calculations, AI is projected to improve many elements of drug discovery and development and will eventually become the standard computer-assisted technique for drug discovery.
Acknowledgments
ANJ would like to acknowledge Department of Science and Technology (DST-SERB File no. CRG/2020/001829), Government of India, for providing computational facilities.
\n',keywords:"ADMET, deep learning, neural network, virtual screening, QSAR",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81739.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81739.xml",downloadPdfUrl:"/chapter/pdf-download/81739",previewPdfUrl:"/chapter/pdf-preview/81739",totalDownloads:9,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 9th 2022",dateReviewed:"March 30th 2022",datePrePublished:"May 13th 2022",datePublished:null,dateFinished:"May 13th 2022",readingETA:"0",abstract:"In recent years, the pharmaceutical business has seen a considerable increase in data digitization. With digitization, however, comes the challenge of obtaining, analyzing, and applying knowledge to solve complex clinical problems. Artificial intelligence (AI), which entails a variety of advanced tools and networks that can mimic human intellect, can overcome such challenges with traditional pharmaceutical development. Artificial intelligence and machine learning have a vast role in therapeutic development, including the prediction of drug target and properties of small molecules. By predicting the 3D protein structure, AI techniques, such as Alpha Fold, can help with structure-based drug development. Machine learning algorithms have been utilized to anticipate the properties of small molecules based on their chemical structure. Many researches have shown the importance of using in silico predictive ADMET (absorption, distribution, metabolism, excretion, and toxicity) models to speed up the discovery of small compounds with enhanced efficacy, safety, and dosage. This chapter discusses various roles of these methods in the development of effective therapeutics.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81739",risUrl:"/chapter/ris/81739",signatures:"Subhomoi Borkotoky, Amit Joshi, Vikas Kaushik and Anupam Nath Jha",book:{id:"11091",type:"book",title:"Drug Development Life Cycle",subtitle:null,fullTitle:"Drug Development Life Cycle",slug:null,publishedDate:null,bookSignature:"Prof. Juber Akhtar, Dr. Badruddeen, Dr. Mohammad Ahmad and Dr. Mohammad Irfan Khan",coverURL:"https://cdn.intechopen.com/books/images_new/11091.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-048-9",printIsbn:"978-1-80356-047-2",pdfIsbn:"978-1-80356-049-6",isAvailableForWebshopOrdering:!0,editors:[{id:"345595",title:"Prof.",name:"Juber",middleName:null,surname:"Akhtar",slug:"juber-akhtar",fullName:"Juber Akhtar"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. 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WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm. Computational and Structural Biotechnology Journal. 2021;19:3573-3579'},{id:"B41",body:'Skalic M et al. Shape-based generative modeling for de novo drug design. Journal of Chemical Information and Modeling. 2019;59(3):1205-1214'},{id:"B42",body:'Bai Q et al. MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Briefings in Bioinformatics. 2020;22(3)'},{id:"B43",body:'Karimi M et al. DeepAffinity: Iinterpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019;35(18):3329-3338'},{id:"B44",body:'Green H, Koes DR, Durrant JD. DeepFrag: A deep convolutional neural network for fragment-based lead optimization. Chemical Science. 2021;12(23):8036-8047'},{id:"B45",body:'Sellwood MA et al. Artificial intelligence in drug discovery. Future Medicinal Chemistry. 2018;10(17):2025-2028'},{id:"B46",body:'Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: What is realistic, what are illusions? Part 2: A discussion of chemical and biological data. Drug Discovery Today. 2021;26(4):1040-1052'},{id:"B47",body:'Arabi AA. Artificial intelligence in drug design: Algorithms, applications, challenges and ethics. Future Drug Discovery. 2021;3(2):FDD59'},{id:"B48",body:'Piekarz D. AI in Drug Development: A Glimpse Into the Future of Drug Discovery. 2021. Available from: https://www.dataart.com/blog/ai-in-drug-development-a-glimpse-into-the-future-of-drug-discovery'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Subhomoi Borkotoky",address:"drsubhomoi.b@invertis.org",affiliation:'
Department of Biotechnology, Invertis University, India
Department of Molecular Biology and Biotechnology, Tezpur University, India
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Two hundred years ago, industrial revolution in the west has transformed or evolved from mechanical production driven or powered by water, and to date, we are in an era characterised by cyber physical systems. This transformation or industrial revolution has been driven by humans using creative minds to solve problems that were confronted. The Industrial 1.0 Revolution around 1700 AD, mass production was carried out by mechanical production powered by water (steam engines), which was labour intensive. The more manpower an industrial organisation has, the more goods and services would be produced, though this could take long to reach the market but that was the industrial system at that time. From mechanical production powered by steam engines between 1700s and 1800s to the second Industrial Revolution mass production powered by electricity between 1800s and 1900s to the third Industrial Revolution powered by electronic and IT automation and finally to Industry 4.0 Revolution cyber systems in 2000 and beyond, human capital has generated innovative solutions to human problems more than ever before. 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Therefore, problems and their solutions also change. The industrial revolution which realized in the eighteenth century had some important impacts not only on the economic life but also on social structure. It was aimed to solve social problems and ensure prosperity through social policies, which is a multidisciplinary field, and consequently, the concept of welfare state emerged. The states, which had liberal concerns and traditional protection functions and reached a powerful position with their internationalist approaches, underwent a transformation period because of the economic and social developments which took place in the last quarter of the twentieth century. It has been subject of criticism that states increased the social expenses to satisfy the social needs and therefore caused an economic crisis in this period when the effects of globalization were discussed. In this study, the change and transformation process in the welfare states and their social policies at the global scale will be handled conceptually and from the historical development perspective. Making determinations about the past and present, as well as having assumptions for future, this study aims to contribute to literature.",book:{id:"7598",slug:"public-economics-and-finance",title:"Public Economics and Finance",fullTitle:"Public Economics and Finance"},signatures:"Esra Dundar Aravacik",authors:[{id:"282700",title:"Dr.",name:"Esra",middleName:null,surname:"Dündar Aravacık",slug:"esra-dundar-aravacik",fullName:"Esra Dündar Aravacık"}]},{id:"65684",title:"Theory of Public Debt and Current Reflections",slug:"theory-of-public-debt-and-current-reflections",totalDownloads:2938,totalCrossrefCites:6,totalDimensionsCites:7,abstract:"From the ancient ages to today, administrations needed continuous financing and met this financing with various sources. The process of social development necessitated public borrowing for different purposes ranging from creation of a consumer society to sell the surplus of developed countries to postwar human relations and from the development financing of developing countries to the payment of debt by debt. Particularly after World War II (1941–1945), the developed countries provided the external resources to developing countries for development financing. As a result of the increase in the mobility of capital in the process of globalization (especially short-term speculative capital investments), developing countries were dragged to the debt-interest helix problem and the external debt crises. The stabilization programs proposed by the IMF led to government guarantee of private sector external debts in the developing countries and led to a rapid increase in the public debt stock.",book:{id:"7598",slug:"public-economics-and-finance",title:"Public Economics and Finance",fullTitle:"Public Economics and Finance"},signatures:"Sibel Aybarç",authors:[{id:"286689",title:"Dr.",name:"Sibel",middleName:null,surname:"Aybarç",slug:"sibel-aybarc",fullName:"Sibel Aybarç"}]},{id:"58010",title:"Fourth Industrial Revolution: Current Practices, Challenges, and Opportunities",slug:"fourth-industrial-revolution-current-practices-challenges-and-opportunities",totalDownloads:6296,totalCrossrefCites:41,totalDimensionsCites:66,abstract:"The globalization and the competitiveness are forcing companies to rethink and to innovate their production processes following the so-called Industry 4.0 paradigm. 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This chapter aims to present the main good practices, challenges, and opportunities related to Industry 4.0 paradigm.",book:{id:"6291",slug:"digital-transformation-in-smart-manufacturing",title:"Digital Transformation in Smart Manufacturing",fullTitle:"Digital Transformation in Smart Manufacturing"},signatures:"Antonella Petrillo, Fabio De Felice, Raffaele Cioffi and Federico\nZomparelli",authors:[{id:"161682",title:"Prof.",name:"Fabio",middleName:null,surname:"De Felice",slug:"fabio-de-felice",fullName:"Fabio De Felice"},{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo"},{id:"205141",title:"Dr.",name:"Federico",middleName:null,surname:"Zomparelli",slug:"federico-zomparelli",fullName:"Federico Zomparelli"},{id:"208748",title:"Dr.",name:"Raffaele",middleName:null,surname:"Cioffi",slug:"raffaele-cioffi",fullName:"Raffaele Cioffi"}]},{id:"63402",title:"Decentralized Territorial Communities and Implementation of Public Policies: The Case of Cameroon",slug:"decentralized-territorial-communities-and-implementation-of-public-policies-the-case-of-cameroon",totalDownloads:1281,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Cameroon’s Constitutional Law of 18 January 1996 enshrined decentralization as a fundamental principle of the organization of state governance, and subsequent implementing legislation affirms the central government’s commitment to transferring a number of powers to local authorities with a view to local management. Local and regional authorities then appear as an essential link in the implementation of public policies at the local level. Their genuine autonomy in financial and administrative matters is a necessary condition for achieving local development objectives. However, a review of the existing literature reveals that these communities do not have real autonomy in public policy decision-making, which is illustrated by mixed development at the local level.",book:{id:"7598",slug:"public-economics-and-finance",title:"Public Economics and Finance",fullTitle:"Public Economics and Finance"},signatures:"Guy Yakana Yombi, Mounton Chouaïbou and Lucie Yakana Agoume",authors:[{id:"257106",title:"Mr.",name:"Guy",middleName:null,surname:"Yakana Yombi",slug:"guy-yakana-yombi",fullName:"Guy Yakana Yombi"},{id:"267660",title:"MSc.",name:"Mounton",middleName:null,surname:"Chouaïbou",slug:"mounton-chouaibou",fullName:"Mounton Chouaïbou"},{id:"267661",title:"MSc.",name:"Yakana Agoume",middleName:null,surname:"Lucie",slug:"yakana-agoume-lucie",fullName:"Yakana Agoume Lucie"}]},{id:"58030",title:"Manufacturing Transformation toward Mass Customization and Personalization in the Traditional Food Industry",slug:"manufacturing-transformation-toward-mass-customization-and-personalization-in-the-traditional-food-i",totalDownloads:1576,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Digital transformation of the manufacturing process in high-tech has been underway for a long time. On the other hand, the transformation in low-tech and traditional industries progresses more slowly. Especially, the human factor is greater in the food manufacturing industry, which retains many more labor-intensive elements. This is because the development of foods was traditionally customized to the cultures of particular regions, so many foods were not suitable for mass production, which has led to the high level of personal skills. However, new trends have been shown recently in the sake manufacturing industry. Head craftsmen at a sake brewery, known as Toji, have managed the entirety of the manufacturing process and determined the length and timing of each process for hundreds of years. In these circumstances, some sake breweries have started to make sake in a new way that breaks with tradition. They implement smart manufacturing and customization to respond to diversified customer needs without altering the product price through the digitization of the manufacturing process and the formalization of personal skills. This chapter also discusses the prospects of this transition and considers its effects on the industry with theoretical framework and social background of manufacturing transformation.",book:{id:"6291",slug:"digital-transformation-in-smart-manufacturing",title:"Digital Transformation in Smart Manufacturing",fullTitle:"Digital Transformation in Smart Manufacturing"},signatures:"Daisuke Kanama",authors:[{id:"210580",title:"Associate Prof.",name:"Daisuke",middleName:null,surname:"Kanama",slug:"daisuke-kanama",fullName:"Daisuke Kanama"}]}],onlineFirstChaptersFilter:{topicId:"65",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,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:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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