The Sentia analyzer measures free SO₂, malic acid, glucose, fructose, acetic acid and titratable acidity in the winery. With a rich touchscreen display and long battery life, the Sentia system makes it easy for you to achieve rapid test results wherever and whenever you need.
Customer Testimonial – Chrysalis Vineyards and why they chose Sentia to take control of their wine testing
Want to see why our customers love Sentia?
Jake from Chrysalis Vineyards explains the value Sentia brings and how it has revolutionized their wine testing process.
Sentia – New Customer Information
Please review all information below prior to running analyses.
Sample Preparation – degassing & dilutions
www.sentiaanalysis.com – for strip reorders
Please ensure to keep strips and buffers in the fridge at all times while not in use.
Set up tips:
When starting your Sentia Device, you will need to connect to Wifi.
– Please make sure you have a strong signal.
– 2.4 gHz (wifi frequency band) works best. | You can also use your phone hotspot
*also, after you enter your email address, the code to verify sometimes appears in junkmail so keep a lookout.
*when setting up time zone, select a city in your time zone.
Once set up, please make sure to update your device to the latest Software version, you can find out how to update your software here.
Sentia free sulfur test better than before
After launching in 2021 with a commitment to continuous improvement, the team at Universal Biosensors (UBI) recently circled back to where it all began to enhance the existing free sulfur dioxide test.
The strip chemistry remains the same and UBI still uses the same patented technology our customers have come to love worldwide. So what has changed? Using machine-based learning the scientists behind Sentia have implemented a more sophisticated method by which the meter analyzes each sample profile. The new algorithm has been trained using thousands of data points to more accurately predict the free sulfur concentration based on the Thermo Gallery reference method.
The results, based on a newly collected Australian Wine Research Institute (AWRI) dataset (n=967) is a significant improvement in overall accuracy.
| Criteria | Original algorithm | New AI algorithm |
| Standard error (mg/L) | 3.9 | 2.9 |
| Coefficient of determination (r2) | 0.87 | 0.93 |
What are machine based learning algorithms and why are we using them?
The new free sulfur method now utilizes machine-based learning where an algorithm is trained on a large dataset of wine samples of different types to accumulate a diverse range of training data. The algorithm uses data mining methods to learn patterns in the training data set that would not be visible to an algorithm implemented in the original method. These learnings are applied to wines not seen before to provide more accurate predictions of the free sulfur dioxide content. UBI has been working with these types of algorithms since 2020. Due to the fact that machine-based learning algorithms can identify patterns and trends in data that may be challenging for humans to recognise, the capability can lead to more accurate predictions and insights than more traditional methods of algorithm implementation.