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Spectroscopic Techniques & Artificial Intelligence for Food and Beverage Analysis
ISBN-13:
9789811564956
Veröffentl:
2020
Einband:
eBook
Seiten:
121
Autor:
Ashutosh Kumar Shukla
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:
This informative book discusses the various spectroscopic techniques applied in the analysis of food and beverages. The respective chapters cover techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), FTIR spectroscopy, Electron Spin Resonance (ESR) spectroscopy and Thermoluminescence. The book also presents artificial intelligence applications that can be used to enhance the spectral data analysis experience in food safety and quality analysis. Given its scope, the book will appeal to novice researchers and students in the area of food science. It offers an equally exciting read for food scientists and engineers working in the food industry.
This informative book discusses the various spectroscopic techniques applied in the analysis of food and beverages. The respective chapters cover techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), FTIR spectroscopy, Electron Spin Resonance (ESR) spectroscopy and Thermoluminescence. The book also presents artificial intelligence applications that can be used to enhance the spectral data analysis experience in food safety and quality analysis.
Given its scope, the book will appeal to novice researchers and students in the area of food science. It offers an equally exciting read for food scientists and engineers working in the food industry.
Chapter 1: Laser Induced Breakdown spectroscopy in food analysis.- Chapter 2: The use of FTIR spectroscopy combined with multivariate analysis in Food composition Analysis.- Chapter 3: Spectrophotometric methods and Electronic Spin Resonance for evaluation antioxidant capacity of food.- Chapter 4: Thermoluminescence the method for the detection of irradiated foodstuffs.- Chapter 5: Advantages of Multi-target modeling for spectral regression.