Spectroscopy and Machine Learning Tools for Food Quality and Safety, Volume 115 in the Advances in Food and Nutrition Research series, highlights new advances in the field, with this new volume presenting interesting chapters related to Spectroscopy and Machine Learning Tools. Chapters in this new release include NIR applications in livestock farming systems and derived products, Fluorescence spectroscopy in grape and wine composition and quality control, From Farm to Fork: Spectroscopy in Meat Quality and Safety Assurance, Raman and Mid infrared in food safety and composition, Spectroscopy food functionality and safety, and Handheld NIR spectroscopy for real-time on-site food quality and safety monitoring.
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Associate Professor Daniel Cozzolino is a Principal Research Fellow with the Queensland Alliance for Agriculture and Food Innovation (QAAFI) - University of Queensland. Associate Professor Cozzolino was trained in agricultural sciences at the Universities of La Republica (Uruguay) (BSc) and Aberdeen (Scotland) (PhD), and worked in several positions such as Associate Professor in Food Chemistry within the School of Science at RMIT University (Melbourne, VIC) (2018-2020), Head of Agriculture and Associate Professor in Agriculture at CQUniversity (Rockhampton, QLD) (2015-2019), Senior Research Fellow with the Barley Breeding Program at The University of Adelaide (Adelaide, SA) (2013-2015), Team Leader of the Rapid Analytical Group at The Australian Wine Research Institute (Adelaide, SA) (2002-2012), and Head of Animal Nutrition at INIA La Estanzuela (Uruguay) (1992-2002). He has published more than 350 peer-review articles and book chapters (h index 65 Google Scholar, Scopus).
Associate Professor Cozzolino research focusses on the utilization of chemometric and spectroscopic methods (e.g. UV-VIS, NIR, MIR) to develop applications in agriculture (e.g. crops, animal products), environment and food (e.g. composition, process, authentication and provenance). Recent research projects have spanned to the authentication, fraud and origin of foods, the effect of climate change in food quality (e.g. grain), process analytics, in field and high-throughput spectroscopic techniques, monitoring plastics in the food chain, identification of markers in biological tissues (e.g. cancer cells). In 2013, he was presented with the Thomas Hirschfeld Award by the International Council for NIR Spectroscopy for his outstanding contributions in the field of NIR spectroscopy.Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardback. Etat : New. Spectroscopy and Machine Learning Tools for Food Quality and Safety, Volume 115 in the Advances in Food and Nutrition Research series, highlights new advances in the field, with this new volume presenting interesting chapters related to Spectroscopy and Machine Learning Tools. Chapters in this new release include NIR applications in livestock farming systems and derived products, Fluorescence spectroscopy in grape and wine composition and quality control, From Farm to Fork: Spectroscopy in Meat Quality and Safety Assurance, Raman and Mid infrared in food safety and composition, Spectroscopy food functionality and safety, and Handheld NIR spectroscopy for real-time on-site food quality and safety monitoring. N° de réf. du vendeur LU-9780443345937
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Hardback. Etat : New. Spectroscopy and Machine Learning Tools for Food Quality and Safety, Volume 115 in the Advances in Food and Nutrition Research series, highlights new advances in the field, with this new volume presenting interesting chapters related to Spectroscopy and Machine Learning Tools. Chapters in this new release include NIR applications in livestock farming systems and derived products, Fluorescence spectroscopy in grape and wine composition and quality control, From Farm to Fork: Spectroscopy in Meat Quality and Safety Assurance, Raman and Mid infrared in food safety and composition, Spectroscopy food functionality and safety, and Handheld NIR spectroscopy for real-time on-site food quality and safety monitoring. N° de réf. du vendeur LU-9780443345937
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