Implementation of Digital Technologies on Beverage Fermentation

In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products...

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Bibliographic Details
Other Authors: Viejo, Claudia Gonzalez (Editor), Fuentes, Sigfredo (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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520 |a In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products' quality traits and international competitiveness, especially within the past five years. Fermented beverages have been one of the most researched industries to implement these technologies to assess product composition and improve production processes and product quality. This Special Issue (SI) is focused on the latest research on the application of digital technologies on beverage fermentation monitoring and the improvement of processing performance, product quality and sensory acceptability. 
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653 |a sensor networks 
653 |a automation 
653 |a beer acceptability 
653 |a beer fermentation 
653 |a RoboBEER 
653 |a machine learning 
653 |a ultrasonic measurements 
653 |a long short-term memory 
653 |a industrial digital technologies 
653 |a yeast morphology 
653 |a automated image analysis 
653 |a heat stress 
653 |a vacuoles 
653 |a cell size 
653 |a computer vision 
653 |a foam stability 
653 |a image analysis 
653 |a lager beer 
653 |a foam retention 
653 |a polyphenols 
653 |a LC-ESI-QTOF-MS/MS 
653 |a HPLC 
653 |a medicinal plants 
653 |a ginger 
653 |a lemon 
653 |a mint 
653 |a herbal tea infusion 
653 |a antioxidants 
653 |a black pepper 
653 |a focus group 
653 |a hops 
653 |a Kawakawa 
653 |a off aromas 
653 |a gas sensors 
653 |a robotic pourer 
653 |a aroma thresholds 
653 |a climate change 
653 |a artificial neural networks 
653 |a volatile phenols 
653 |a glycoconjugates 
653 |a bushfires 
653 |a sparkling wine 
653 |a fermentation 
653 |a biogenic amines 
653 |a wine quality 
653 |a liquid chromatography 
653 |a principal component analysis 
653 |a augmented reality 
653 |a non-dairy yogurt 
653 |a contexts 
653 |a consumer acceptability 
653 |a emotional responses 
653 |a Fermentation 
653 |a Olea europaea 
653 |a respiration rate 
653 |a storage conditions 
653 |a transport 
653 |a TeeBot 
653 |a high throughput 
653 |a liquid handling robot 
653 |a metabolite analysis 
653 |a stochastic dynamic optimisation 
653 |a uncertainty 
653 |a n/a 
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