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Kovsie Brew triumphs at the 2025 Intervarsity Brewing Competition

06 November 2025 | Story Azil Coertzen | Photo Supplied

The University of the Free State (UFS) showcased its innovation-driven student talent as the Kovsie Brew team secured multiple awards at the 2025 Intervarsitybrew™: Brewing and Tasting Challenge – South Africa’s premier student brewing competition.

Co-hosted by the Central University of Technology (CUT) and the Beer Association of South Africa (BASA), the prestigious annual event brings together student brewers from universities across the country to demonstrate creativity, technical skill, and a passion for craft beer.

A year of dedication, experimentation, and on-campus brewing innovation

The three-day competition, held in Bloemfontein from 23 to 25 October 2025, featured participants from 17 universities and a chef school, highlighting its growing national reputation. Each team was challenged to brew six different beers and design an original label, while taking part in presentations, sensory training, blind tastings conducted by qualified judges, and technical sessions led by industry experts. The Intervarsitybrew™ also promotes responsible drinking while celebrating scientific knowledge, creativity, and collaboration.

Representing the UFS, the Kovsie Brew team, consisting of eight student brewers – Casey van Baalen, Jana Bischoff, Ruan Jacobs, Tyla Baker, Joni Muller, Martin Visser, Hendre Heymans, and team captain Azil Coertzen – was guided by mentors Dr Vaughn Swart, Dr Christopher Rothmann, and Prof Errol Cason. The team worked throughout the year to refine their craft and push boundaries in student brewing.

In 2025, the Kovsie team brewed an impressive 14 beers, experimenting with different styles and flavour profiles. They hosted tasting sessions with mentors and peers, attended the Clarens Beer Festival for industry feedback, assisted with the brewing of 500 litres of pale ale at the UFS Paradys Experimental Farm, and actively participated in Free State Fermenters meetings – where some members earned awards based on Beer Judge Certification Programme (BJCP) standards.

Award-winning brews with standout creativity and technical excellence

For this year’s Intervarsitybrew™, the team presented six competition beers:

Hazy IPA (IPA category) – A hop-forward, tropical brew that won first place in its category.
British Ordinary Bitter (Summer category) – A refreshing, malt-driven ale showcasing classic English brewing.
Coffee Imperial Stout (Aged category) – A dark, flavourful stout praised for its smooth finish.
Margarista Gose (Wild category) – A citrus-inspired, tart beer earning second place in the African Wild Ale category.
Jalapeño Sour (Sour category) – A daring blend of heat and acidity, taking third place in the Sour/Fruit Beer category.
Czech Lager (Lager category) – A clean, crisp lager representing traditional European brewing.

Their standout performance earned them three major accolades:

Best IPA – Hazy IPA (sponsored by Fermentis and Anton Paar)
Second place: African Wild Ale – Margarista Gose (sponsored by SAB and Heineken Beverages)
Third place: Sour/Fruit Beer – Jalapeño Sour (sponsored by Shimadzu)

Reflecting on the team’s success, mentor Dr Vaughn Swart expressed his pride: “After the disappointment of a total loss last year, their determination and creativity truly shone through. Watching them transform into success has been deeply inspiring. Their growth, not just as brewers but as passionate, resilient individuals, reminds me why mentorship and shared passion matter so much. This year’s wins are a testament to the team’s perseverance and to the spirit of Kovsie excellence.”

The Kovsie Brew Team extended its gratitude to its supporters – the Department of Microbiology and Biochemistry, the Centre for Mineral Biogeochemistry (CMBG), and LiquidCulture Yeast – as well as the Intervarsitybrew™ organisers for continuing to foster a vibrant brewing culture at the UFS.

The UFS proudly celebrates the Kovsie Brew team’s achievements, which reflect the institution’s commitment to nurturing innovation, collaboration, and scientific excellence – brewed to perfection, the Kovsie way.

News Archive

Mathematical methods used to detect and classify breast cancer masses

2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers.

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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