Link to the paper: CLICK HERE
Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling.
Smelling Faults in beers using AI and a low-cost e-nose
Digital Agriculture, Food and Wine research group received a 2021 Grants4Ag. Bayer Crop Science Awards 2021
After receiving more than 600 proposals from almost 40 countries, the Bayer Crop Science team has selected 24 proposals to fund. From protecting plants with beneficial bacteria to detecting disease through drones and AI, these 24 scientists have outstanding innovations to help farmers protect crops. Congratulations to the 2021 Grants4Ag awardees!
Click HERE to go to the site, and HERE to know more about the project funded.
Dear Colleagues,
Recent digital developments in food, beverage, and packaging analysis have been focused on using contactless sensors, such as remote sensing and biometrics, to assess the quality traits of produces, detect packaging defects during the production process, and understand perception through sensory analysis based on consumer biometrics. The implementation of these novel techniques is aiding the food, beverage, and packaging industries in the rapid, efficient, cost-effective, and reliable evaluation and assessment of products at any stage of the production chain for the early detection of potential faults within the process and consumer evaluation of the final product and packaging.
This Special Issue will bring together high-quality papers focused on these technologies and their applications in the food and beverage industries, including packaging assessments. These contactless sensors incorporate electronic noses and tongues, the use of infrared thermal cameras, UV-Vis, mid- and near-infrared spectroscopy, and computer vision analysis, among others. These technologies may be applied to assess food, beverages, and packaging quality traits such as sensory attributes, physicochemical components, nutritional values, and defects, at any stage in the production chain, which aid in the quality assessment of products.
Dr. Claudia Gonzalez Viejo
Dr. Sigfredo Fuentes
Dr. Damir Torrico
Guest Editors
https://findanexpert.unimelb.edu.au/news/6533-using-tech-to-save-wine-from-bushfire-smoke
Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as “ashy”, “burning rubber”, and “smoked meats”, resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.
Read the full article by clicking HERE
Abstract
Labels concepts, information, logo, figures, and colors for beverages are critical for consumer perception, preference, and purchase intention. This is especially relevant for new beverage products. During social isolation, many sensory laboratories were unable to provide services, making virtual sensory sessions relevant to study different label concepts and design preferences among consumers. This study proposed a novel virtual sensory system to analyze coffee labels using videoconference, self-reported and biometric analysis software from video-recordings to obtain sensory and emotional responses from 69 participants (Power analysis: 1 – β > 0.99) using six different label concepts: i) fun; ii) bold; iii) natural; iv) everyday; v) classic and vi) premium. Results showed that the label concept rated as highest perceived quality was the Premium presenting significant differences (p < 0.05) compared to all other concepts. The least score for perceived quality was attributed to Bold due to confronting aroma lexicon (cheese sip), which is supported by previous studies. Furthermore, even though graphics, colors, and product name could be considered positive attributes, they do not determine perceived quality or purchase intent, which was found for the concepts bold, everyday, and classic. The findings form this study were expected and are consistent with those from similar publications related with labels, which shows that the proposed virtual method for sensory sessions and biometrics is reliable. Further technology has been proposed to use this system with multiple participants, which could help beverage companies perform virtual sensory analysis of new products’ labels.
Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce UHI by regulating and reducing ambient temperatures. Forest cities (i.e. Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring the green infrastructure of cities requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance, and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016-17. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.
Digital Agriculture (DA) deals with the implementation and integration of digital data, sensors, and tools on agricultural, food, and wine applications from the paddock/vineyard to consumers. These technologies can range from big data, sensor technology, sensor networks, remote sensing, robotics, unmanned aerial vehicles (UAV).
Data processing is performed using new and emerging technologies, such as computer vision, machine learning, and artificial intelligence, among others.
The latest advances made by the DAFW group for crop monitoring/decision making, assessment of the quality of produces, sensory analysis for consumer perception and animal stress, and welfare assessment.
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