Predictive Quality
Assembly Line
Using machine learning techniques in wine quality testing
The Profiling capability from Thermo Scientific™ SampleManager™ LIMS software provides an innovative way for laboratories to predict test results using historical data and novel machine learning (ML)-based techniques. For example, a food and beverage company might apply the Profiling capability to enable supervised learning in the food production process. In this case, SampleManager LIMS would use historical data to gain an understanding of the critical variables that determine whether a product is safe for consumers. This holistic approach considers not only the values of the individual critical variables themselves, but also the relationships between them. If a sample were to be flagged as failing, the system would alert stakeholders in advance to issue adjustments or investigations to avoid any risk to finished products.
In a wine production facility, the result of the “Quality Test” is of utmost importance. The laboratory has great flexibility and control over the testing process, so they could use the Profiling capability to redefine the order of the standard tests conducted to a wine sample.