Real-time quality control in tea processing
Traditional quality control in the tea production process is often based on visual inspection and experience. Local Tea tests the feasibility of a Near Infrared (NIR) sensor to gain real-time insight into the quality of tea leaves during the drying process. This technology allows the process to be adjusted faster and more accurately, which increases efficiency.
Innovation package, use case and type test
Food processing
Application of NIR sensor in production process
Status: final report
Technical functionality
Broad knowledge question
Can a NIR sensor provide real-time quality data?
In tea processing, the moisture content of the leaves is a crucial indicator. This is currently determined based on experience or by laboratory analyses. This project investigates whether a NIR sensor offers a reliable alternative by generating real-time data on the dry matter content of the tea leaves.
Approach
Sensor placement and data collection
The NIR sensor is placed in the dryer, where it continuously collects data. Based on this data, a predictive model is trained, which can determine the dry matter value as accurately as possible. To check the reliability of the measurements, the sensor results are validated using reference measurements.
Goal
Automatic threshold detection
The purpose of this test is to determine whether the NIR sensor is suitable for integration into the production process and whether it is able to detect when a certain threshold value for the dry matter content has been reached. This can contribute to a more standardized and efficient production.
Result and reflection
Promising results, but more data needed
Based on the collected data and initial test results, the sensor can predict the threshold value for dry matter content with approximately 90% accuracy.
Successes:
The sensor has demonstrated the capability to provide real-time data on dry matter content.
The model can predict with high accuracy when the desired value is reached.
Lessons learned:
More data is needed for definitive validation to fully test the sensor across different tea types and moisture variations.
Implementing NIR technology in a production environment requires further optimization and fine-tuning to specific process variables.