Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- sensor networks --- automation --- beer acceptability --- beer fermentation --- RoboBEER --- machine learning --- ultrasonic measurements --- long short-term memory --- industrial digital technologies --- yeast morphology --- automated image analysis --- heat stress --- vacuoles --- cell size --- computer vision --- foam stability --- image analysis --- lager beer --- foam retention --- polyphenols --- LC-ESI-QTOF-MS/MS --- HPLC --- medicinal plants --- ginger --- lemon --- mint --- herbal tea infusion --- antioxidants --- black pepper --- focus group --- hops --- Kawakawa --- off aromas --- gas sensors --- robotic pourer --- aroma thresholds --- climate change --- artificial neural networks --- volatile phenols --- glycoconjugates --- bushfires --- sparkling wine --- fermentation --- biogenic amines --- wine quality --- liquid chromatography --- principal component analysis --- augmented reality --- non-dairy yogurt --- contexts --- consumer acceptability --- emotional responses --- Fermentation --- Olea europaea --- respiration rate --- storage conditions --- transport --- TeeBot --- high throughput --- liquid handling robot --- metabolite analysis --- stochastic dynamic optimisation --- uncertainty --- sensor networks --- automation --- beer acceptability --- beer fermentation --- RoboBEER --- machine learning --- ultrasonic measurements --- long short-term memory --- industrial digital technologies --- yeast morphology --- automated image analysis --- heat stress --- vacuoles --- cell size --- computer vision --- foam stability --- image analysis --- lager beer --- foam retention --- polyphenols --- LC-ESI-QTOF-MS/MS --- HPLC --- medicinal plants --- ginger --- lemon --- mint --- herbal tea infusion --- antioxidants --- black pepper --- focus group --- hops --- Kawakawa --- off aromas --- gas sensors --- robotic pourer --- aroma thresholds --- climate change --- artificial neural networks --- volatile phenols --- glycoconjugates --- bushfires --- sparkling wine --- fermentation --- biogenic amines --- wine quality --- liquid chromatography --- principal component analysis --- augmented reality --- non-dairy yogurt --- contexts --- consumer acceptability --- emotional responses --- Fermentation --- Olea europaea --- respiration rate --- storage conditions --- transport --- TeeBot --- high throughput --- liquid handling robot --- metabolite analysis --- stochastic dynamic optimisation --- uncertainty

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

sensor networks --- automation --- beer acceptability --- beer fermentation --- RoboBEER --- machine learning --- ultrasonic measurements --- long short-term memory --- industrial digital technologies --- yeast morphology --- automated image analysis --- heat stress --- vacuoles --- cell size --- computer vision --- foam stability --- image analysis --- lager beer --- foam retention --- polyphenols --- LC-ESI-QTOF-MS/MS --- HPLC --- medicinal plants --- ginger --- lemon --- mint --- herbal tea infusion --- antioxidants --- black pepper --- focus group --- hops --- Kawakawa --- off aromas --- gas sensors --- robotic pourer --- aroma thresholds --- climate change --- artificial neural networks --- volatile phenols --- glycoconjugates --- bushfires --- sparkling wine --- fermentation --- biogenic amines --- wine quality --- liquid chromatography --- principal component analysis --- augmented reality --- non-dairy yogurt --- contexts --- consumer acceptability --- emotional responses --- Fermentation --- Olea europaea --- respiration rate --- storage conditions --- transport --- TeeBot --- high throughput --- liquid handling robot --- metabolite analysis --- stochastic dynamic optimisation --- uncertainty --- n/a