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220822 ||| eng |
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|a books978-3-0365-3656-9
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|a 9783036536569
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|a 9783036536552
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|a Viejo, Claudia Gonzalez
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|a Implementation of Digital Technologies on Beverage Fermentation
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (220 p.)
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|a stochastic dynamic optimisation
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| 653 |
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|a machine learning
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|a foam stability
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|a industrial digital technologies
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|a off aromas
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|a cell size
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|a fermentation
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|a yeast morphology
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| 653 |
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|a Technology, Engineering, Agriculture, Industrial processes / bicssc
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| 653 |
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|a transport
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| 653 |
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|a Fermentation
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| 653 |
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|a RoboBEER
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|a n/a
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|a medicinal plants
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| 653 |
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|a consumer acceptability
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|a antioxidants
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| 653 |
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|a robotic pourer
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| 653 |
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|a black pepper
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| 653 |
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|a vacuoles
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| 653 |
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|a uncertainty
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|a lager beer
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| 653 |
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|a volatile phenols
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|a sparkling wine
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|a automation
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| 653 |
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|a Olea europaea
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|a high throughput
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| 653 |
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|a aroma thresholds
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| 653 |
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|a respiration rate
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|a storage conditions
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| 653 |
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|a liquid handling robot
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| 653 |
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|a LC-ESI-QTOF-MS/MS
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| 653 |
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|a lemon
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| 653 |
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|a automated image analysis
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| 653 |
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|a beer acceptability
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| 653 |
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|a foam retention
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| 653 |
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|a principal component analysis
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| 653 |
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|a sensor networks
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|a biogenic amines
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|a emotional responses
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| 653 |
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|a contexts
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| 653 |
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|a image analysis
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| 653 |
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|a Biology, life sciences / bicssc
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| 653 |
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|a Kawakawa
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| 653 |
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|a HPLC
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| 653 |
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|a wine quality
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| 653 |
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|a glycoconjugates
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| 653 |
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|a non-dairy yogurt
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|a long short-term memory
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| 653 |
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|a liquid chromatography
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|a computer vision
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|a hops
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|a beer fermentation
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|a climate change
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|a bushfires
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|a herbal tea infusion
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|a artificial neural networks
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|a TeeBot
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|a ultrasonic measurements
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| 653 |
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|a metabolite analysis
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| 653 |
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|a mint
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| 653 |
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|a heat stress
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| 653 |
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|a gas sensors
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| 653 |
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|a Research and information: general / bicssc
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| 653 |
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|a focus group
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| 653 |
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|a augmented reality
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| 653 |
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|a polyphenols
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| 653 |
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|a ginger
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| 700 |
1 |
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|a Fuentes, Sigfredo
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| 700 |
1 |
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|a Viejo, Claudia Gonzalez
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| 700 |
1 |
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|a Fuentes, Sigfredo
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-3656-9
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|u https://www.mdpi.com/books/pdfview/book/5218
|7 0
|x Verlag
|3 Volltext
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|u https://directory.doabooks.org/handle/20.500.12854/81186
|z DOAB: description of the publication
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|a 551.6
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|a 414
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|a 630
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|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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