Back to Home
Food Omics

Tea Leaves Quality Discrimination with LC-QTOF and AI

Tea analysis workflow: sample preparation → LC-QTOF detection → data processing → AI analysis

Tea Analysis Workflow (Sample Prep → LC-QTOF → Data Processing → AI)

Machine Learning Model for tea quality prediction: trained on 1000+ samples with 90% accuracy

Machine Learning Model (90%+ Accuracy on 1000+ Samples)

Overview

How do we find the high quality of tea leaves in China? We use to try on our tea markers, their smell and taste - just global tea lovers, and that is it in short. But we are offering an alternative approach combining LC-QTOF with AI.

The Challenge

Traditional tea quality assessment relies heavily on sensory evaluation by expert tasters, which can be subjective and variable. We needed a more objective and consistent approach to evaluate tea quality.

Our Solution

Our combined LC-QTOF and AI approach offers:

  • Objective chemical profiling of tea leaves
  • AI-powered quality classification
  • Consistent results across different samples
  • Identification of key chemical markers for quality

Key Results

This innovative approach provides a powerful tool for tea quality assessment, combining the analytical power of LC-QTOF with the pattern recognition capabilities of artificial intelligence to deliver reliable and objective quality evaluations.