Semi-automated and automated assessment of sparkling wine quality based on foam stability, assessed using a robotic pourer and image analysis algorithms

By Sigfredo Fuentes; Bruna Lima, Maeva Caron and Kate Howell


Figure 1: 3D model of the robotic pourer.


Figure 2: Inside and outside of actual pourer.

The Australian wine industry contributes strongly to the country’s economy (Lin, 2013). Australia is the sixth largest wine producer and the fourth largest wine exporter (WFA, 2013). Sparkling wine accounts for approximately 9% of the domestic wine sales and 13% of total wine imports in Australia (ABS, 2013). Wines with dissolved carbon dioxide are also economically important for several countries, France, Spain, Italy, USA, and Chile (IWSR, 2011; CIVC, 2013). Increase in temperature and carbon dioxide levels in the atmosphere have shown to affect flavour and aroma of wines (Johnson and Robinson, 2013), decrease protein concentration in plants (Högy et al., 2009; Azam et al., 2013; Mishra et al., 2013) and increase alcohol in wines (Howden and Stokes, 2009). Consequently, climate change is expected to influence the final quality of sparkling wine, since several compounds, including protein concentration and alcohol are related to foam stability and the ability of the wine to produce foam (Pozo-Bayón et al., 2009; Coelho et al., 2011).

Figure 3: Sparkling wine pouring

The quality of sparkling wine is visually assessed by its colour, bubble behaviour, appearance (bead) and foam persistence (mousse) (Liger-Belair, 2013). However, as discussed by a number of authors, these parameters are extremely variable and are affected by pouring, reception vessel shape and type as well as temperature (Cilindre et al., 2010; Liger-Belair et al., 2012; Liger-Belair et al., 2013). Robotics and chemometrics allows us to control and monitor these parameters, and thus repeatedly measure sparkling wine for quality assessment and to correlate it with traditional measures of quality. A robotic bottle pourer has been developed to standardise time and wine volume of pouring into a standardised vessel. Images are collected automatically with a digital camera attached to the pourer and transferred to a computer. These images are then evaluated by image analysis algorithms, which convey the information into bubble size and speed, foamability (ability of the wine to produce foam), foam persistence and stability, and collar stability.


Figure 4: Pourer in action in parallel to a sensory analysis at Moet Chandon, Yarra Valley, VIC – Australia.

As a result, this robotic pourer and image analysis algorithms, which simultaneously quantify both bubble’s individual behaviour and collectively as part of the foam, allows the development of a reproducible, easy and inexpensive method to measure sparkling wine quality. Results from this novel technology have been compared to chemometrics and sensory panel data using multivariate data analysis.

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