Category: Research Paper

New Paper: Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and artificial neural networks modelling techniques

By

Claudia Gonzalez Viejo 1, Sigfredo Fuentes 1*, Damir Torrico1, Kate Howell1, and Frank R. Dunshea1.

1 University of Melbourne, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, VIC 3010, Australia

* Correspondence: sfuentes@unimelb.edu.au; Tel.: +61 3 9035 9670

Journal of The Science of Food and Agriculture

Full Article: CLICK HERE

ABSTRACT:

Beer quality is mainly defined by its color, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and color-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectrometry (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam.

NIRANN

Why there are more and bigger bushfires?: Smoke Taint in Berries and Wine, What can we do about it?

Non-invasive smoke-taint detection in berries from grapevine (Vitis vinifera L.) using near infrared spectroscopy and machine learning models

I have been spending some whole nighters working on this topic due to recent news about extensive bush fires in Chile and in New South Wales, in January and February 2017 respectively. Both of these events have been named by the media as the biggest bush fire events in their respective histories. In Chile, the majority of the bush fires were in the central part of Chile, coincidentally to the majority of grapevine plantations. We do know that smoke taint has the biggest effect on berry contamination after veraison (7 days after on-set), which was about the timing of bushfires for a few cultivars for both Chile and NSW.

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smoke-taint-chile

A recent report (February 2017) from the Victorian Government – Australia, has concluded that bush fires will increase severity and the window of opportunity due to climate change, specifically due to increases in temperature, increased frequency and intensity of heat waves and drought events.

smoke-taint-report

So, what can we do about it?

As said before, I started to dig up some data from an ARC – Linkage project in which I worked as part of a team from The University of Adelaide. In that project we smoked artificially a number of cultivars to see the physiological effects of smoke contamination. From the canopy perspective and stomatal conductance, to be more specific, this effect can be explained through the chemical reaction between the main compounds found in smoke: carbon dioxide (CO2) and carbon monoxide (CO) and water. When getting in contact with stomata, smoke gases they can pass to the sub-stomata cavity, which is at 100% relative humidity. These compounds are then mixed with water forming carbonic acid (H2CO3) which reduced pH (acidic) hence close stomata.

This effect was reported in a poster in which I did a model to detect this effect on stomata conductance to discriminate canopies that have been contaminated or not with smoke. See posting by clicking here. The models worked really well for all the cultivars studied but Sauvignon blanc. I did attributed this effect to the morphology of leaves for this cultivar, which have high pubescence in the abaxial side. My hypothesis was that this offers a barrier to smoke, which can explain the inefficiency of the model based on the lack of stomatal conductance reduction.

I am currently working in the development of these models considering the top of canopies to apply them using infrared thermal imagery from Unmanned Aerial Vehicles (UAV), which can map a whole vineyard in the days after a bush fire event.

After the bush fires in Chile, I have revisited the Near Infrared (NIR) data from berries to see whether I was able to generate machine learning models to detect smoke taint in berries triggering the instrument around the skin and then measuring also in halved berries. The instrument that we used was a ASD FieldSpec® 3, Analytical Spectral Devices, Boulder, Colorado, USA. Which measures in the range of 350 – 1880 nm.

smoke-taintnir

I have decided to concentrate the models in the700 – 1100 nm range using the second derivative of data, since it is the range of inexpensive NIR instrumentation. Also, after analysis of the rest of the wavelength range, the improvements of models obtained did not justified the jump in price of instrumentation from around $2,000 – $3000 to $38,000 – $65,000.

I did obtain three interesting models, the first was to detect whether the berries measured in a bunch have been contaminated or not using Artificial Neural Networks. I tried with data from whole and half berries and surprisingly I got better results with full berries. This is important since it renders the methodology as non-invasive. And makes sense when reading reports which found that the majority of glycocongugates are found in the skin of berries, which are higher than the pulp and higher that those found in seeds.

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Then I tried models using machine learning fitting algorithms to see whether I could predict the levels of glycocongugates in the berries (whole):

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And finally, whether I was able to predict smoke taint compounds in the wine made with contaminated and non contaminated berries, such as guaiacol, Syringol and cresols:

smoke-taintcompoundsnew

 

These are very exciting preliminary results which I am in the process of writing up for a peer review publication. The best thing is that if measurements made in the field are associated to GPS tagging, this could produce a contamination map using simple kriging interpolation techniques. This tool can support the decision making process towards differential harvest to avoid contaminating the whole production and salvaging fruit that has not been contaminated to the levels of spoilage. It offers also alternatives for winemaking to avoid excessive crushing and fermentation with skins that could contribute to the increase of smoke taint compounds in the final wine. Decisions can also be made by assessing the timing of ageing in barrels and whether it is required at all. And obviously, making non-contaminated wine using non-contaminated fruit. As can be seen in my preliminary results, the model for glycocongugates rendered a 10% error, which by quantifying the levels of smoke taint compounds does not make much difference in the final wine and it may contribute even to increased organoleptic characteristics with tones of leather, wood, bacon, etc

Something to look forward after all these tragedies.

Dr Sigfredo Fuentes

The University of Melbourne

New Interview: Digital Vineyards: building technology so farms can think for themselves

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Dr Sigfredo Fuentes is a plant physiologist and engineer at the University of Melbourne whose end game is the smart farm.

In his mind, it won’t be restricted to a single crop – maybe it’ll grow peaches and nectarines and vines as well – all of which are mapped out digitally.

And at the heart of the operation will be the drone, autonomous and equipped to collect all the data it needs to monitor the entire farm down to the last plant or tree.

And it’s fast – it can cover 2500 hectares in a day, which is about 2000 football fields.

Link to full article: CLICK HERE

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