Category: Research Paper

New Paper: Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER

Abstract

There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automatedpost hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.

New Article:Development of a robotic and computer vision method to assess foam quality in sparkling wines.

Abstract
Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of foam quality, including foamability (the ability of the foam to form) drainability (the ability of the foam to resist drainage) and bubble count and allometry. The foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher quality scores from trained panelists were positively correlated with foam stability and negatively correlated with the velocity of foam dissipation and the height of the collar. Significant correlations were observed between the wine quality measurements of total protein, titratable acidity, pH and foam expansion. The percentage of the wine in the foam was found to promote the formation of smaller bubbles and to reduce foamability, while drainability was negatively correlated to foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their foam and bubble characteristics, quality scores and chemical parameters. The technique developed in this study objectively assessed foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and foam parameters obtained automatically, might assist in unraveling factors contributing to wine quality and directions for further research.
Fig-1-Diagram-illustrating-the-procedure-used-for-quantifying-foam-parameters-by-using
Pourer

New Article:Assessment of an automated digital method to estimate leaf area index (LAI) in cherry trees

Abstract
A study was carried out during two growing seasons to evaluate the performance of a digital photography method to estimate the LAI (LAID). The trial consisted in 10 ‘Bing’ and 10 ‘Sweetheart’ trees where actual LAI (LAIA) was obtained by allometric relations. Estimations of LAID were obtained by the batch processing of images of the canopy of the same trees which were obtained by a conventional digital RGB camera. Comparisons of averages LAIA and LAID resulted in a good level of agreement for ‘Sweetheart’ for the two growing seasons (mean absolute percent error: MAE% = 10.4 %). For ‘Bing’, LAID was accurate in the first growing season (MAE% = 17.7 %) but underestimated by 44% (MAE%) in the second growing season, presumably due to differences observed in the clumping index and the light extinction coefficient. Results evidenced the robustness of this simple method for determining the LAI of cherry trees.
CherryLAI

New Article: Distribution of rotundone and possible translocation of related compounds amongst grapevine tissues in Vitis vinifera L. cv. Shiraz

ArticleinFrontiers in Plant Science · June 2016

Impact Factor: 3.95 ·
Abstract
Rotundone is an attractive wine aroma compound, especially important for cool climate Shiraz. Its presence in wine is mainly fromthe grape skin, but can also be found in non-grape tissues, such as leaves and stems. Whether rotundone is produced independently within different grapevine tissues or transported amongst non-grape tissues and grape berries remains unclear. The current study investigated the distribution of this compound in different vine tissues during development and studied the most likely mode of rotundone translocation – via phloem – using stable isotope feeding. In addition, local production of rotundone induced by herbivore feeding was assessed. Results showed that rotundone was firstly detected in the petioles and peduncles/rachises within the development of Vitis vinifera L. cv. Shiraz. Different grapevine tissues had a similar pattern of rotundone production at different grape developmental stages. In the individual vine shoots, non-grape tissues contained higher concentrations and amounts of rotundone compared to berries, which showed that non-grape tissues were the larger pool of rotundone within the plant. This study confirmed the local production of rotundone in individual tissues and ruled out the possibility of phloem translocation of rotundone between different tissues. In addition, other terpenes, including 1 monoterpenoid (geraniol) and six sesquiterpenes (clovene, α‐ylangene, β‐copaene, α‐muurolene, δ‐cadinene, and cis/trans‐calamenene) were, for the first time, detected in the EDTA-facilitated petiole phloem exudates, with their originality unconfirmed. Unlike other herbivore-induced terpenes, herbivorous activity had limited influences on the concentration of rotundone in grapevine leaves.
FULL PAPER: CLICK HERE

VitiCanopy: A new smartphone and Tablet PC App to assess vigour and canopy architecture of Horticultural Trees

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Authors:

Roberta De Bei 1, Sigfredo Fuentes 2, Matthew Gilliham 1,3, Steve Tyerman 1,3, Everard Edwards 4, Nicolò Bianchini 4,5, Jason Smith 6,† and Cassandra Collins 1,*

1School of Agriculture, Food and Wine, Waite Research Institute, the University of Adelaide,
PMB 1 Glen Osmond 5064, South Australia, Australia; roberta.debei@adelaide.edu.au (R.D.B.); matthew.gilliham@adelaide.edu.au (M.G.); stephen.tyerman@adelaide.edu.au (S.T.)
2Faculty of Veterinary and Agricultural Sciences, the University of Melbourne, Parkville 3010, Victoria, Australia; sigfredo.fuentes@unimelb.edu.au
ARC Centre of Excellence in Plant Energy Biology, Waite Research Institute, PMB 1 Glen Osmond 5064, South Australia, Australia
3CSIRO Agriculture, Waite Campus Laboratory, Private Bag 2, Glen Osmond 5064, South Australia, Australia; Everard.Edwards@csiro.au (E.E.); nicolo.bianchini@gmail.com (N.B.)
4Dipartimento di Scienze Agrarie (DipSA), the University of Bologna, Area Colture Arboree, Viale Fanin 46, 40127 Bologna, Italy
5National Wine and Grape Industry Centre, Charles Sturt University, Locked Bag 588, 6Wagga Wagga 2678, New South Wales, Australia; Jason.Smith@hs-gm.de
Correspondence: cassandra.collins@adelaide.edu.au; Tel.: +61-08-8313-6813
Current address: Hochschule Geisenheim University, Department of General and Organic Viticulture, Von-Lade-Str. 1, 65366 Geisenheim, Germany.

Abstract: 

Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or indirectly using dedicated and expensive instrumentation, both of which require a high level of know-how to operate equipment, handle data and interpret results. Recently, a novel smartphone and tablet PC application, VitiCanopy, has been developed by a group of researchers from the University of Adelaide and the University of Melbourne, to estimate grapevine canopy size (LAI and PAI), canopy porosity, canopy cover and clumping index. VitiCanopy uses the front in-built camera and GPS capabilities of smartphones and tablet PCs to automatically implement image analysis algorithms on upward-looking digital images of canopies and calculates relevant canopy architecture parameters. Results from the use of VitiCanopy on grapevines correlated well with traditional methods to measure/estimate LAI and PAI. Like other indirect methods, VitiCanopy does not distinguish between leaf and non-leaf material but it was demonstrated that the non-leaf material could be extracted from the results, if needed, to increase accuracy. VitiCanopy is an accurate, user-friendly and free alternative to current techniques used by scientists and viticultural practitioners to assess the dynamics of LAI, PAI and canopy architecture in vineyards, and has the potential to be adapted for use on other plants.

Keywords: canopy vigor; LAI; PAI; computer application; light extinction coefficient; image analysis; cover photography