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| data science in food industry: Analytics of Life Mert Damlapinar, 2019-11-11 Analytics of Life provides the reader with a broad overview of the field of data analytics and artificial intelligence. It provides the layperson an understanding of the various stages of artificial intelligence, the risks and powerful benefits. And it provides a way to look at big data and machine learning that enables us to make the most of this exciting new realm of technology in our day-to-day jobs and our small businesses. Questions you can find answers* * What is artificial intelligence (AI)? * What is the difference between AI, machine learning and data analytics? * Which jobs AI will replace, which jobs are safe from data analytics revolution? * Why data analytics is the best career move? * How can I apply data analytics in my job or small business? Who is this book for? * Managers and business professionals * Marketers, product managers, and business strategists * Entrepreneurs, founders and startups team members * Consultants, advisors and educators * Almost anybody who has an interest in the future According to an article by Cade Metz in The New York Times, Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Oxford University professor Nick Bostrom argues that if machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. MIT professor Max Tegmark describes and illuminates the recent, ground-breaking advances in Artificial Intelligence and how it might overtake human intelligence. As Oxford University economist Daniel Susskind points out, technological progress could bring about unprecedented prosperity, solving one of humanity's oldest problems: how to make sure that everyone has enough to live on. Distinguished AI researcher and professor of computer science at UC Berkeley, Russell Stuart suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Industry experts claim that AI will have a negative impact on blue-collar jobs, but Mert predicts that Americans and Europeans will experience a strong impact on white-collar jobs as well. And Mert also provides research results and a clear description of which jobs will be affected and how soon, which jobs could be enhanced with AI. Analytics of Life also provides solutions and insight into some of the most profound changes to come in human history. |
| data science in food industry: Statistics for Food Scientists Frank Rossi, Victor Mirtchev, 2015-10-06 The practical approached championed in this book have led to increasing the quality on many successful products through providing a better understanding of consumer needs, current product and process performance and a desired future state. In 2009, Frank Rossi and Viktor Mirtchev brought their practical statistical thinking forward and created the course Statistics for Food Scientists. The intent of the course was to help product and process developers increase the probability of their project's success through the incorporation of practical statistical thinking in their challenges. The course has since grown and has become the basis of this book. - Presents detailed descriptions of statistical concepts and commonly used statistical tools to better analyze data and interpret results - Demonstrates thorough examples and specific practical problems of what food scientists face in their work and how the tools of statistics can help them to make more informed decisions - Provides information to show how statistical tools are applied to improve research results, enhance product quality, and promote overall product development |
| data science in food industry: Innovation Strategies in the Food Industry Charis M. Galanakis, 2021-10-21 Innovation Strategies for the Food Industry: Tools for Implementation, Second Edition explores how process technologies and innovations are implemented in the food industry, by i.e., detecting problems and providing answers to questions of modern applications. As in all science sectors, Internet and big data have brought a renaissance of changes in the way academics and researchers communicate and collaborate, and in the way that the food industry develops. The new edition covers emerging skills of food technologists and the integration of food science and technology knowledge into the food chain. This handbook is ideal for all relevant actors in the food sector (professors, researchers, students and professionals) as well as for anyone dealing with food science and technology, new products development and food industry. - Includes the latest trend on training requirements for the agro-food industry - Highlights new technical skills and profiles of modern food scientists and technologists for professional development - Presents new case studies to support research activities in the food sector, including product and process innovation - Covers topics on collaboration, entrepreneurship, Big Data and the Internet of Things |
| data science in food industry: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science in food industry: Advances in Food Authenticity Testing Gerard Downey, 2016-08-08 Advances in Food Authenticity Testing covers a topic that is of great importance to both the food industry whose responsibility it is to provide clear and accurate labeling of their products and maintain food safety and the government agencies and organizations that are tasked with the verification of claims of food authenticity. The adulteration of foods with cheaper alternatives has a long history, but the analytical techniques which can be implemented to test for these are ever advancing. The book covers the wide range of methods and techniques utilized in the testing of food authenticity, including new implementations and processes. The first part of the book examines, in detail, the scientific basis and the process of how these techniques are used, while other sections highlight specific examples of the use of these techniques in the testing of various foods. Written by experts in both academia and industry, the book provides the most up-to-date and comprehensive coverage of this important and rapidly progressing field. Covers a topic that is of great importance to both the food industry and the governmental agencies tasked with verifying the safety and authenticity of food products Presents a wide range of methods and techniques utilized in the testing of food authenticity, including new implementations and processes Highlights specific examples of the use of the emerging techniques and testing strategies for various foods |
| data science in food industry: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science in food industry: The Interaction of Food Industry and Environment Charis M. Galanakis, 2020-01-22 The Interaction of Food Industry and Environment addresses all levels of interaction, paying particular attention to avenues for responsible operational excellence in food production and processing. Written at a scientific level, this book explores many topics relating to the food industry and environment, including environmental management systems, environmental performance evaluation, the correlation between food industry, sustainable diets and environment, environmental regulation on the profitability of sustainable water use in the food industry, lifecycle assessment, green supply chain network design and sustainability, the valorization of food processing waste via biorefineries, food-energy-environment trilemma, wastewater treatment, and much more. Readers will also find valuable information on energy production from food processing waste, packaging and food sustainability, the concept of virtual water in the food industry, water reconditioning and reuse in the food industry, and control of odors in the food industry. This book is a welcomed resource for food scientists and technologists, environmentalists, food and environmental engineers and academics. - Addresses the interaction between the food industry and environment at all levels - Focuses on the past decade's advances in the field - Provides a guide to optimize the current food industry's performance - Serves as a resource for anyone dealing with food and environmental science and technology - Includes coverage of a variety of topics, including performance indicators, the correlation between the food industry, sustainable diets and the environment, environmental regulations, lifecycle assessments, green supply chain networks, and more |
| data science in food industry: Food Politics Marion Nestle, 2013-05-14 We all witness, in advertising and on supermarket shelves, the fierce competition for our food dollars. In this engrossing exposé, Marion Nestle goes behind the scenes to reveal how the competition really works and how it affects our health. The abundance of food in the United States--enough calories to meet the needs of every man, woman, and child twice over--has a downside. Our over-efficient food industry must do everything possible to persuade people to eat more--more food, more often, and in larger portions--no matter what it does to waistlines or well-being. Like manufacturing cigarettes or building weapons, making food is big business. Food companies in 2000 generated nearly $900 billion in sales. They have stakeholders to please, shareholders to satisfy, and government regulations to deal with. It is nevertheless shocking to learn precisely how food companies lobby officials, co-opt experts, and expand sales by marketing to children, members of minority groups, and people in developing countries. We learn that the food industry plays politics as well as or better than other industries, not least because so much of its activity takes place outside the public view. Editor of the 1988 Surgeon General's Report on Nutrition and Health, Nestle is uniquely qualified to lead us through the maze of food industry interests and influences. She vividly illustrates food politics in action: watered-down government dietary advice, schools pushing soft drinks, diet supplements promoted as if they were First Amendment rights. When it comes to the mass production and consumption of food, strategic decisions are driven by economics--not science, not common sense, and certainly not health. No wonder most of us are thoroughly confused about what to eat to stay healthy. An accessible and balanced account, Food Politics will forever change the way we respond to food industry marketing practices. By explaining how much the food industry influences government nutrition policies and how cleverly it links its interests to those of nutrition experts, this path-breaking book helps us understand more clearly than ever before what we eat and why. |
| data science in food industry: Agri-Food Industry Strategies for Healthy Diets and Sustainability Francisco J. Barba, Predrag Putnik, Danijela Bursac Kovacevic, 2020-03-03 Divided into five sections, Agri-Food Industry Strategies for Healthy Diets and Sustainability: New Challenges in Nutrition and Public Health provides an overview of the challenges and future perspectives related to nutrition, public health, and sustainability. The book addresses strategies to reduce fat, trans fat, saturated fat, sugar, and salt consumption, while also exploring the manufacturing, safety, and toxicology of new food manufacturing. This book examines commercial labeling and nutritional education, nutrigenomics and public health, and provides coverage of the valorization of waste and by-products from the food industry. Nutrition researchers and practitioners, food scientists, technologists, engineers, agronomists, food product developers, medical and public health professionals, and postgraduate students focused in food science and nutrition are sure to find this reference work a welcomed addition to their libraries. - Contains innovative strategies to achieve a healthy diet through the design of new food products - Provides comprehensive information related to agriculture, nutrition, food industry, government, and sustainable waste management and details their roles in addressing food waste - Explores the ways in which innovative approaches, used to valorize and give an added value to agri-food waste and by-products, ensure the sustainability of the production process - Presents nutritive education about reducing empty calories by lowering consumption of fats, sugars, and other high-calorie nutrients - Delineates the roles of food industry and government in shaping the best policies for the general public and the design of new products |
| data science in food industry: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science in food industry: Microbial Decontamination in the Food Industry Ali Demirci, Michael O Ngadi, 2012-06-26 The problem of creating microbiologically-safe food with an acceptable shelf-life and quality for the consumer is a constant challenge for the food industry. Microbial decontamination in the food industry provides a comprehensive guide to the decontamination problems faced by the industry, and the current and emerging methods being used to solve them.Part one deals with various food commodities such as fresh produce, meats, seafood, nuts, juices and dairy products, and provides background on contamination routes and outbreaks as well as proposed processing methods for each commodity. Part two goes on to review current and emerging non-chemical and non-thermal decontamination methods such as high hydrostatic pressure, pulsed electric fields, irradiation, power ultrasound and non-thermal plasma. Thermal methods such as microwave, radio-frequency and infrared heating and food surface pasteurization are also explored in detail. Chemical decontamination methods with ozone, chlorine dioxide, electrolyzed oxidizing water, organic acids and dense phase CO2 are discussed in part three. Finally, part four focuses on current and emerging packaging technologies and post-packaging decontamination.With its distinguished editors and international team of expert contributors, Microbial decontamination in the food industry is an indispensable guide for all food industry professionals involved in the design or use of novel food decontamination techniques, as well as any academics researching or teaching this important subject. - Provides a comprehensive guide to the decontamination problems faced by the industry and outlines the current and emerging methods being used to solve them - Details backgrounds on contamination routes and outbreaks, as well as proposed processing methods for various commodities including fresh produce, meats, seafood, nuts, juices and dairy products - Sections focus on emerging non-chemical and non-thermal decontamination methods, current thermal methods, chemical decontamination methods and current and emerging packaging technologies and post-packaging decontamination |
| data science in food industry: Cooling Technology in the Food Industry Aurel Ciobanu, 1976 Scientific background. General systems applied in food refrigeration. Applications: meat, poultry, fish, milk and dairy products, eggs, fruits and vegetables, ice cream, prepared foods, fermented beverages, other food products, cold chain. |
| data science in food industry: Food Safety Management Huub L. M. Lelieveld, Yasmine Motarjemi, 2013-11-01 Food Safety Management: A Practical Guide for the Food Industry with an Honorable Mention for Single Volume Reference/Science in the 2015 PROSE Awards from the Association of American Publishers is the first book to present an integrated, practical approach to the management of food safety throughout the production chain. While many books address specific aspects of food safety, no other book guides you through the various risks associated with each sector of the production process or alerts you to the measures needed to mitigate those risks. Using practical examples of incidents and their root causes, this book highlights pitfalls in food safety management and provides key insight into the means of avoiding them. Each section addresses its subject in terms of relevance and application to food safety and, where applicable, spoilage. It covers all types of risks (e.g., microbial, chemical, physical) associated with each step of the food chain. The book is a reference for food safety managers in different sectors, from primary producers to processing, transport, retail and distribution, as well as the food services sector. - Honorable Mention for Single Volume Reference/Science in the 2015 PROSE Awards from the Association of American Publishers - Addresses risks and controls (specific technologies) at various stages of the food supply chain based on food type, including an example of a generic HACCP study - Provides practical guidance on the implementation of elements of the food safety assurance system - Explains the role of different stakeholders of the food supply |
| data science in food industry: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science in food industry: In Defense of Processed Food Robert L. Shewfelt, 2016-11-23 It has become popular to blame the American obesity epidemic and many other health-related problems on processed food. Many of these criticisms are valid for some processed-food items, but many statements are overgeneralizations that unfairly target a wide range products that contribute to our health and well-being. In addition, many of the proposed dangers allegedly posed by eating processed food are exaggerations based on highly selective views of experimental studies. We crave simple answers to our questions about food, but the science behind the proclamations of food pundits is not nearly as clear as they would have you believe. This book presents a more nuanced view of the benefits and limitations of food processing and exposes some of the tricks both Big Food and its critics use to manipulate us to adopt their point of view. Food is a source of enjoyment, a part of our cultural heritage, a vital ingredient in maintaining health, and an expression of personal choice. We need to make those choices based on credible information and not be beguiled by the sophisticated marketing tools of Big Food nor the ideological appeals and gut feelings of self-appointed food gurus who have little or no background in nutrition. |
| data science in food industry: Data Science and Analytics Sneha Kumari, K.K. Tripathy, Vidya Kumbhar, 2020-12-04 Data Science and Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. |
| data science in food industry: Robotics and Automation in the Food Industry Darwin G Caldwell, 2012-12-03 The implementation of robotics and automation in the food sector offers great potential for improved safety, quality and profitability by optimising process monitoring and control. Robotics and automation in the food industry provides a comprehensive overview of current and emerging technologies and their applications in different industry sectors.Part one introduces key technologies and significant areas of development, including automatic process control and robotics in the food industry, sensors for automated quality and safety control, and the development of machine vision systems. Optical sensors and online spectroscopy, gripper technologies, wireless sensor networks (WSN) and supervisory control and data acquisition (SCADA) systems are discussed, with consideration of intelligent quality control systems based on fuzzy logic. Part two goes on to investigate robotics and automation in particular unit operations and industry sectors. The automation of bulk sorting and control of food chilling and freezing is considered, followed by chapters on the use of robotics and automation in the processing and packaging of meat, seafood, fresh produce and confectionery. Automatic control of batch thermal processing of canned foods is explored, before a final discussion on automation for a sustainable food industry.With its distinguished editor and international team of expert contributors, Robotics and automation in the food industry is an indispensable guide for engineering professionals in the food industry, and a key introduction for professionals and academics interested in food production, robotics and automation. - Provides a comprehensive overview of current and emerging robotics and automation technologies and their applications in different industry sectors - Chapters in part one cover key technologies and significant areas of development, including automatic process control and robotics in the food industry and sensors for automated quality and safety control - Part two investigates robotics and automation in particular unit operations and industry sectors, including the automation of bulk sorting and the use of robotics and automation in the processing and packaging of meat, seafood, fresh produce and confectionery |
| data science in food industry: Food Analysis Laboratory Manual S. Suzanne Nielsen, 2010-03-20 This second edition laboratory manual was written to accompany Food Analysis, Fourth Edition, ISBN 978-1-4419-1477-4, by the same author. The 21 laboratory exercises in the manual cover 20 of the 32 chapters in the textbook. Many of the laboratory exercises have multiple sections to cover several methods of analysis for a particular food component of characteristic. Most of the laboratory exercises include the following: introduction, reading assignment, objective, principle of method, chemicals, reagents, precautions and waste disposal, supplies, equipment, procedure, data and calculations, questions, and references. This laboratory manual is ideal for the laboratory portion of undergraduate courses in food analysis. |
| data science in food industry: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science in food industry: Food Process Design Zacharias B. Maroulis, George D. Saravacos, 2003-05-09 This timely reference utilizes simplified computer strategies to analyze, develop, and optimize industrial food processes and offers procedures to assess various operating conditions, engineering and economic relationships, and the physical and transport properties of foods for the design of the most efficient food manufacturing technologies and eq |
| data science in food industry: Innovations in Classification, Data Science, and Information Systems Daniel Baier, Klaus-Dieter Wernecke, 2006-06-06 The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques. |
| data science in food industry: Elementary Food Science Ernest R. Vieira, 2013-04-17 Following the success of the previous editions, this popular introductory text continues to provide thorough, up-to-date information covering a broad range of topics in food science, with emphasis on food processing and handling and the methodology of specific foods. Presenting a multitude of easy-to-understand figures, tables, illustrated concepts and methods. This text maintains the strengths of the previous edition while adding new information. The book opens with a revised chapter on what food science actually is, detailing the progression of food science from beginning to future. Succeeding chapters include the latest information on food chemistry and dietary recommendations, food borne diseases and microbial activity. A complete revision of HACCP is outlined, accompanied by numerous examples of flow charts and applications, as well as major additions on food labeling. Extensive updates have been made on processing methods and handling of foods, such as new procedures on: candy making; coffee and tea production; beer and wine production; soft drinks; ultra high temperature processing; aseptic packaging; aquaculture and surimi; and UHT and low temperature pasteurization of milk. In addition, there is a completely new section which includes safety and sanitation as well as laboratory exercises in sensory, microbiological, chemical quality test, and processing methods for a variety of the foods described in previous chapters. |
| data science in food industry: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science in food industry: Sous Vide and Cook-Chill Processing for the Food Industry Sue Ghazala, 1998-08-31 The emerging sous vide and cook-chill techniques are becoming increasingly important in the food industry. The contributors discuss the advantages and disadvantages of using these techniques and possible future implications for the industry. |
| data science in food industry: Data Science and Digital Transformation in the Fourth Industrial Revolution Jongbae Kim, Roger Lee, 2021-01-02 This edited book presents scientific results of the International Semi-Virtual Workshop on Data Science and Digital Transformation in the Fourth Industrial Revolution (DSDT 2020) which was held on October 15, 2020, at Soongsil University, Seoul, Korea. The aim of this workshop was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The workshop organizers selected the best papers from those papers accepted for presentation at the workshop. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 17 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science. |
| data science in food industry: Food Safety Practices in the Restaurant Industry Nurhayati Khairatun, Siti, Zakiah Abu Bakar, Ainul, Azira Abdul Mutalib, Noor, Fatimah Ungku Zainal Abidin, Ungku, 2021-11-26 In recent years, cases of food-borne illness have been on the rise and are creating a significant public health challenge worldwide. This situation poses a health risk to consumers and can cause economic loss to the food service industry. Identifying the current issues in food safety practices among the industry players is critical to bridge the gap between knowledge, practices, and regulation compliance. Food Safety Practices in the Restaurant Industry presents advanced research on food safety practices investigated within food service establishments as an effort to help the industry pinpoint risks and non-compliance relating to food safety practices and improve the practices in preventing food-borne illnesses from occurring. Covering a range of topics such as food packaging, safety audits, consumer awareness, and standard safety practices, it is ideal for food safety and service professionals, food scientists and technologists, policymakers, restaurant owners, academicians, researchers, teachers, and students. |
| data science in food industry: Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies Murugan, Thangavel, E., Nirmala, 2023-09-21 Disruptive innovations are now propelling Industry 4.0 (I4.0) and presenting new opportunities for value generation in all major industry segments. I4.0 technologies' innovations in cybersecurity and data science provide smart apps and services with accurate real-time monitoring and control. Through enhanced access to real-time information, it also aims to increase overall effectiveness, lower costs, and increase the efficiency of people, processes, and technology. The Handbook of Research on Data Science and Cybersecurity Innovations in Industry 4.0 Technologies discusses the technological foundations of cybersecurity and data science within the scope of the I4.0 landscape and details the existing cybersecurity and data science innovations with I4.0 applications, as well as state-of-the-art solutions with regard to both academic research and practical implementations. Covering key topics such as data science, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, computer scientists, scholars, researchers, academicians, practitioners, instructors, and students. |
| data science in food industry: Science Breakthroughs to Advance Food and Agricultural Research by 2030 National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Environmental Change and Society, Health and Medicine Division, Food and Nutrition Board, Division on Earth and Life Studies, Water Science and Technology Board, Board on Life Sciences, Board on Atmospheric Sciences and Climate, Board on Agriculture and Natural Resources, Committee on Science Breakthroughs 2030: A Strategy for Food and Agricultural Research, 2019-04-21 For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate. More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face. Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030). |
| data science in food industry: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data science in food industry: Hygiene in Food Processing H.L.M. Lelieveld, M A Mostert, B White, John Holah, 2003-07-25 A high standard of hygiene is a prerequisite for safe food production, and the foundation on which HACCP and other safety management systems depend. Edited and written by some of the world's leading experts in the field, and drawing on the work of the prestigious European Hygienic Engineering and Design Group (EHEDG), Hygiene in food processing provides an authoritative and comprehensive review of good hygiene practice for the food industry.Part one looks at the regulatory context, with chapters on the international context, regulation in the EU and the USA. Part two looks at the key issue of hygienic design. After an introductory chapter on sources of contamination, there are chapters on plant design and control of airborne contamination. These are followed by a sequence of chapters on hygienic equipment design, including construction materials, piping systems, designing for cleaning in place and methods for verifying and certifying hygienic design. Part three then reviews good hygiene practices, including cleaning and disinfection, personal hygiene and the management of foreign bodies and insect pests.Drawing on a wealth of international experience and expertise, Hygiene in food processing is a standard work for the food industry in ensuring safe food production. - An authoritative and comprehensive review of good hygiene practice for the food industry - Draws on the work of the prestigious European Hygienic Engineering and Design Group (EHEDG) - Written and edited by world renowned experts in the field |
| data science in food industry: Quantitative Methods for Food Safety and Quality in the Vegetable Industry Fernando Pérez-Rodríguez, Panagiotis Skandamis, Vasilis Valdramidis, 2018-02-06 This book focuses on the food safety challenges in the vegetable industry from primary production to consumption. It describes existing and innovative quantitative methods that could be applied to the vegetable industry for food safety and quality, and suggests ways in which such methods can be applied for risk assessment. Examples of application of food safety objectives and other risk metrics for microbial risk management in the vegetable industry are presented. The work also introduces readers to new preservation and packaging methods, advanced oxidative processes (AOPs) for disinfection, product shelf-life determination methods, and rapid analytic methods for quality assessment based on chemometrics applications, thus providing a quantitative basis for the most important aspects concerning safety and quality in the vegetable sector. |
| data science in food industry: Food Industry 4.0 Abdo Hassoun, 2024-04-15 Developments in Food Quality and Safety Series is the most up-to-date resource covering trend topics such as Advances in the analysis of toxic compounds and control of food poisoning; Food fraud, traceability and authenticity; Revalorization of agrifood industry; Natural antimicrobial compounds and application to improve the preservation of food; Non-thermal processing technologies in the food industry; Nanotechnology in food production; and Intelligent packaging and sensors for food applications. Volume 4, Food Industry 4.0: Emerging Trends and Technologies in Food Production and Consumption covers several technologies (e.g., robotics, smart sensors, artificial intelligence, and big data) at different development and research levels in order to provide holistic multidisciplinary approaches that embrace simultaneously as many Industry 4.0 technologies as possible, reflecting the long journey of food from farm (or sea) to fork. Chapters explore automation, digitalization, and green technologies, besides food quality, food safety food traceability, processing and preservation 4.0. Topics such as smart sensors, artificial intelligence and big data revolution, additive manufacturing, and emerging food trends are also explored. The series is edited by Dr. José Manuel Lorenzo and authored by a team of global experts in the fields of Food Quality and Safety, providing comprehensive knowledge to food industry personals and scientists. - Provides a comprehensive view of Industry 4.0 technologies as applied to the food industry - Covers the most trend topics related to novel foods in the light of emerging innovations and developments - Discusses how implementing innovative technologies holds significant potential to increase efficiency and value added, save time and cost, and increase profitability in various food sectors |
| data science in food industry: Artificial Intelligence: A Real Opportunity in the Food Industry Aboul Ella Hassanien, Mona Soliman, 2022-11-03 This book emphasizes the latest developments and achievements in AI and related technologies with a special focus on food quality. The book describes the applications, and conceptualization of ideas, and critical surveys covering most aspects of AI for food quality. |
| data science in food industry: A Framework for Assessing Effects of the Food System National Research Council, Institute of Medicine, Board on Agriculture and Natural Resources, Food and Nutrition Board, Committee on a Framework for Assessing the Health, Environmental, and Social Effects of the Food System, 2015-06-17 How we produce and consume food has a bigger impact on Americans' well-being than any other human activity. The food industry is the largest sector of our economy; food touches everything from our health to the environment, climate change, economic inequality, and the federal budget. From the earliest developments of agriculture, a major goal has been to attain sufficient foods that provide the energy and the nutrients needed for a healthy, active life. Over time, food production, processing, marketing, and consumption have evolved and become highly complex. The challenges of improving the food system in the 21st century will require systemic approaches that take full account of social, economic, ecological, and evolutionary factors. Policy or business interventions involving a segment of the food system often have consequences beyond the original issue the intervention was meant to address. A Framework for Assessing Effects of the Food System develops an analytical framework for assessing effects associated with the ways in which food is grown, processed, distributed, marketed, retailed, and consumed in the United States. The framework will allow users to recognize effects across the full food system, consider all domains and dimensions of effects, account for systems dynamics and complexities, and choose appropriate methods for analysis. This report provides example applications of the framework based on complex questions that are currently under debate: consumption of a healthy and safe diet, food security, animal welfare, and preserving the environment and its resources. A Framework for Assessing Effects of the Food System describes the U.S. food system and provides a brief history of its evolution into the current system. This report identifies some of the real and potential implications of the current system in terms of its health, environmental, and socioeconomic effects along with a sense for the complexities of the system, potential metrics, and some of the data needs that are required to assess the effects. The overview of the food system and the framework described in this report will be an essential resource for decision makers, researchers, and others to examine the possible impacts of alternative policies or agricultural or food processing practices. |
| data science in food industry: Emerging Technologies for the Food Industry C. Anandharamakrishnan, Jeyan Arthur Moses, 2024-04-30 With changing consumer preferences and the focus on developing resilient food systems, food processing is finding its place in key policies, government interventions, global trade, and the overall food and nutritional security. Given this, this new 3-volume collection offers a compilation of emerging and futuristic food processing technologies, presenting fundamental concepts of food technology, trending applications, and a range of interdisciplinary concepts that have found numerous interwoven applications in the food industry. Volume 3 is an exploration of the future of food processing, highlighting certain emerging and disruptive technologies and their gaining influence in the food sector. The first five chapters focus on computers and information technology-linked applications such as CFD modeling, robotics, automation, artificial intelligence, big data, the Internet of Things, cloud computing, and blockchain management for the food industry. The book then details selected interesting concepts that have made phenomenal advancements in recent years: approaches for improved delivery of nutrients, micro- and nanofluidics, novel drying technologies, smart and intelligent packaging, as well as 3D food printing technology. The other volumes in the series are Volume 1: Fundamentals of Food Processing Technology, which presents the basics of food preservation, covering hurdle technology, aspects of minimal processing, ohmic heating of foods, edible coatings, and electromagnetics and allied applications in food processing; and Volume 2: Advances in Nonthermal Processing Technologies, which focuses on the interesting field of nonthermal processing and its applications. |
| data science in food industry: Data Science for Genomics Amit Kumar Tyagi, Ajith Abraham, 2022-11-27 Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR. - Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics - Presents a roadmap of future trends suitable for innovative Data Science research and practice - Includes topics such as Blockchain technology for securing data at end user/server side - Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns |
| data science in food industry: Data Science Chengqi Zhang, Wei Huang, Yong Shi, Philip S. Yu, Yangyong Zhu, Yingjie Tian, Peng Zhang, Jing He, 2015-10-29 This book constitutes the refereed proceedings of the Second International Conference on Data Science, ICDS 2015, held in Sydney, Australia, during August 8-9, 2015. The 19 revised full papers and 5 short papers presented were carefully reviewed and selected from 31 submissions. The papers focus on the following topics: mathematical issues in data science; big data issues and applications; data quality and data preparation; data-driven scientific research; evaluation and measurement in data service; big data mining and knowledge management; case study of data science; social impacts of data science. |
| data science in food industry: Data Science and Big Data Analytics Durgesh Mishra, |
| data science in food industry: Data Science Projects with Python Stephen Klosterman, 2019-04-30 Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful. |
| data science in food industry: Design of Experiments for Chemical, Pharmaceutical, Food, and Industrial Applications Carrillo-Cedillo, Eugenia Gabriela, Rodríguez-Avila, José Antonio, Arredondo-Soto, Karina Cecilia, Cornejo-Bravo, José Manuel, 2019-12-13 Statistics is a key characteristic that assists a wide variety of professions including business, government, and factual sciences. Companies need data calculation to make informed decisions that help maintain their relevance. Design of experiments (DOE) is a set of active techniques that provides a more efficient approach for industries to test their processes and form effective conclusions. Experimental design can be implemented into multiple professions, and it is a necessity to promote applicable research on this up-and-coming method. Design of Experiments for Chemical, Pharmaceutical, Food, and Industrial Applications is a pivotal reference source that seeks to increase the use of design of experiments to optimize and improve analytical methods and productive processes in order to use less resources and time. While highlighting topics such as multivariate methods, factorial experiments, and pharmaceutical research, this publication is ideally designed for industrial designers, research scientists, chemical engineers, managers, academicians, and students seeking current research on advanced and multivariate statistics. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
